Essay Writing Service

1 Star2 Stars3 Stars4 Stars5 Stars (No Ratings Yet)
Loading...

Dissection of Microrna-30D’s Function Roles in Mammalian Pancreatic-beta Cells

DISSECTION OF MICRORNA-30D’S FUNCTION ROLES IN MAMMALIAN PANCREATIC-BETA CELLS
Table of Contents
LIST OF FIGURES
LIST OF TABLES
PERFACE
ACKNOWLEDGEMENT
ABSTRACT
INTRODUCTION AND BACKGROUND
The Brief History of Diabetes and Research
Environmental and Genetic Contributions of T2D
The Islet Architecture and β-cell Fate
Glucose Stimulate Insulin Secretion (GSIS)
Compensatory Islet Expansion During Insulin Resistance
The Brief History of MicroRNAs
MicroRNA Induced Gene Silencing
MicroRNAs in Insulin-Responsive Cell and Beta-Cell Function
MiR-30d in Pancreatic β-Cells and Cancer Cells
AIM OF THIS STUDY
CHAPTER 1 microRNA-30d regulates mouse glucose homeostasis by inducing pancreatic -cell death and inhibiting the proliferation
1.1 Introduction
1.2 Methods and Materials
Culture of Pancreatic Beta Cell line MIN6
Transfection
Mouse Islet Isolation and Culture
Insulin Secretion Assay
RNA Extraction
cDNA Synthesis
Quantitative real-time PCR for mRNA Transcripts
Quantitative real-time PCR for microRNAs
Animal Care and Treatment
Genotyping of Mouse Tail Biopsies
Blood Glucose Measurement, In Vivo Glucose Tolerance Test and in vivo Insulin Tolerance Test
Insulin/Glucagon Quantification using an Elisa kit
Pancreatic Insulin or Glucagon Content
Western Blotting
Immunohisto-chemistry Staining and In-situ Hybridization
Pancreatic Cells and MIN6 Cells Proliferation Determination
In-situ TUNEL Assay
Slides Imaging and Quantification
Statistical Methods
1.3 Results
1.3.1 Generation of a transgenic mouse model that specifically overexpresses miR-30d at different levels in -cells.
1.3.2 Under normal diet condition, miR-30d does not affect the glucose homeostasis, nor -cell function.
1.3.3 Under high-fat diet, miR-30d worsen the glucose homeostasis in TgH mice
1.3.4 Highly overexpress miR-30d in β-cells induces cell death and inhibit compensate β-cell proliferation in response to high-fat diet
1.3.5 Overexpression of miR-30d inhibited β-cell proliferation and induced β-cell apoptosis under high-fat diet
1.3.6 CCNE2 is one of the targets of miR-30d to regulate the cell cycle
1.3.7 miR-30d promoted BNIP3 expression in β-cells
1.3.8 miR-30d may inhibit β-cell function as well
1.4 Discussion
CHAPTER 2 Identification of miR-30d’s targets in pancreatic β-cells
2.1 Introduction
2.2 Methods and Materials
Cell culture and generation of miR-30d knocked down lines
Transfection
RNA Extraction
Quantitative real-time PCR for mRNA Transcripts
RNA sequencing by HiSeq analysis
Statistical Analysis
2.3 Results
2.3.1 Identification of differentially expressed genes in miR-30d knockdown line using HiSeq analysis
2.3.2 Functional enrichment analysis of differently expressed genes in miR-30d KO
2.3.3 Identification of novel targets of miR-30d
2.4. Discussion
CHAPTER 3Determination of miR-30d function in Cre-STTM30fl/fl mice
3.1 Introduction
3.2 Methods and Materials
Animal Care and Treatment
Mouse Islet Isolation and Culture
RNA Extraction
Quantitative real-time PCR for miRNA Transcripts
Genotyping of Mouse Tail Biopsies
Blood Glucose Measurement, In Vivo Tolerance Tests and in vivo Insulin Release
Insulin Quantification using an Elisa kit
Statistical Methods
3.3 Results
3.3.1 Generation of β-cell specific miR-30 KO mice (STTM-30fl/fl)
3.3.2 miR-30 knock out mice obtained normal body weight and glucose level
3.4 Discussion
REFERENCES
APPENDIX
A. List of all 1422 differently expressed genes in miR-30d KO cell line from HiSeq analysis
BTable of functional enrichment analysis of differently expressed gene in miR-30d KO line
C. Figure reuse permissions

LIST OF FIGURES

Figure 1.1 Insulin regulates glucose homeostasis
Fig 1.2. Islet morphological changes during the progression of T2D
Fig 1.3 Glucose stimulated insulin secretion
Fig 1.4 microRNA biogenesis and function
Fig.1.5 microRNAs in the crosstalk of pancC:UsersasusDesktop3333.jpgreas and insulin-responsive tissues to maintain the glucose homeostasis
Fig.2.1 miR-30d transgenic mice generation
Fig 2.2 miR-30d specifically overexpressed in pancreatic islet cells.
Fig 2.3 miR-30d does not change the glucose or insulin level
Fig 2.4 miR-30d does not change the glucose tolerance or insulin sensitivity
Fig 2.4 miR-30d decreased the insulin content in TgH mice
Fig 2.6 TgH mice exhibited hyperglycemia and decreased plasma insulin level under high-fat diet treatment
Fig 2.7 TgH mice exhibited glucose intolerance and poor glucose stimulated insulin secretion response
Fig 2.8 miR-30d induced significant β-cell loss in TgH mice
Fig 2.9 Overexpression of miR-30d inhibited β-cell proliferation and induced β-cell apoptosis under high-fat diet
Fig 2.10 CCNE2 is one of miR-30d’s targets in MIN6 cells
Fig 2.11 miR-30d promoted BNIP3 expression
Fig 2.11 miR-30d may inhibit β-cell function as well
Figure 3.1. Differentially expressed genes after knocking down miR-30d
Fig 2.3.2 Alignment details of miR-30d and 14 candidate targets using miRNA.org database
Fig 4.1 Transgene integration and genotyping of the STTM30 transgenic model
Fig 4.2 Validation of down-regulated expression in miR-30 KO mice
Fig 4.3 miR-30 KO mice maintained normal body weight and glucose level…….
Fig 4.4 miR-30 KO didn’t change the glucose or insulin tolerance under normal diet condition.

LIST OF TABLES

Table 1.2.1: Primer Sequences for Mouse Genotyping
Table 3.1 Functional Enrichment analysis of differently expressed genes in miR-30d KO cell.
Table 3.2. List of predicted targets of miR-30d in MIN6 cells
Table 4.1: Primer Sequences for Mouse Genotyping

ABSTRACT

 
MicroRNAs (miRNAs) are a group of small non-coding RNAs (about 21-22 nucleotides long) that fine tune target protein output through messenger RNA degradation or inhibition of its translation. Recent studies showing that miRNAs and their function components respond to cellular stress to maintain steady state physiology of the cells. Since there are thousands of microRNAs existing in all kind of cells, their functional characterization during the normal states or stress conditions is not fully addressed yet.
In this thesis, important aspects of pancreatic β-cell function under normal or stress condition such as apoptosis, proliferation, insulin production and release and their regulation by the miRNA were explored. Pancreatic β-cells is a group of insulin producing cells and plays critical role in maintaining glucose hemostasis. By combining mouse and cell line genetic approaches, high-throughput deep sequencing, a list of cell assays and molecular techniques, we have shed light on the novel roles of miR-30d, one highly expressed miRNA when β-cell responding to high glucose stimulus, in regulating β-cell mass on the middle-aged mice. We demonstrated that overexpress of miR-30d deteriorated glucose tolerance ability of the mice with high fat diet treatment by significantly reducing the β-cell mass with less insulin production. Additionally, we demonstrated that both apoptosis pathway and proliferation pathway have been effected by miR-30d by targeting variety of protein factors’ expression. cyclin E2 (CCNE2) had been confirmed as miR-30d’s target and the effect of their regulation by miR-30d in pancreatic β-cell proliferation aspects have been addressed as well. BCL2 interacting protein 3 (BNIP3) had also been found out regulated by miR-30d expression. Furthermore, we showed that silencing of miR-30d in MIN6 cell (β-cell mimicking cell line) by CRISPR-CAS9 gene editing system promotes the insulin secretion, which is through potentiated expression of MAFA, an insulin transcription factor. High-Seq analysis and further target searching of this silencing cell line revealed 13 candidate targets of miR-30d in MIN6 cells involved in various functions. Among them, CXCL10 appealed to be a strong potential target.
These studies uncovered novel functional roles of the miR-30d pathway in mediating β-cell function and fate. Further dissection of these pathways shall uncover the mechanisms by which the β-cells utilized to maximize their efficiency during disease states such as T2D.

INTRODUCTION AND BACKGROUND

The Brief History of Diabetes and Research

Long before terming “diabetes”, which means “to pass through” by Greek Apollonius of Memphis in 250 BCE and the re-discovery of ‘honey-like-urine’ (glycosuria) by Thomas Willis, who included the term “mellitus”, Diabetes has discovered its first saying around 1500 B.C. in an Egyptian manuscript. It was perceived as a disease related with ‘too great emptying of the urine’ (polyuria). Later, important discoveries includes Matthew Dobson’s first proof of elevated urine and blood glucose levels (hyperglycemia) in people with diabetes [3]. In 1889, Joseph von Mering and Oskar Minkowski were the first to give evidence that pancreas removal in dogs induced diabetes, proposing that pancreas functional related to glucose levels. Afterward, Edward Albert Sharpey-Schafer proposed that diabetes could be brought on by losing a pancreatic chemical, which he named as insulin in 1910 [4]. Taken together, diabetes is currently well recognized as a gathering of heterogeneous disorders characterized by hyperglycemia because of loss of insulin or its effectiveness. Current worldwide trends demonstrate a surprising 382 million individuals have diabetes and this is predicted to ascend to 592 million by the year 2035[5]. The cases of diabetes complications, including diabetic retinopathy, cardiovascular disease and renal failure are constantly rising and the death rate because of those are worsen every year.
Claude Bernard’s identification of liver as the major glucose production organ led to the first concept of “homeostasis” which has been termed and expanded by Walter Bradford in the mid-nineteenth century, to describe the maintaining of steady-state physiology of the cells [6]. This gave the notion that actually the disturbed glucose homeostasis is one of the important events of the diabetes progression. Given the evidences of insulin is involved in maintaining the glucose homeostasis, Frederick Banting and Charles Best integrated a series of scientific approaches, and were able to purify the insulin from the pancreas (Fig 1.1). Moreover, they successfully treated the patients who suffer from the diabetes, with their purified insulin [7, 8]. This landmark finding set the stage for treating the severe diabetes with insulin. However, it has been almost a century now since the first-time insulin was discovered and purified, diabetes remains the incurable disease, requires life-time attention and treatment because of its complexity.
1Figure 1.1 Insulin regulates glucose homeostasis

Figure 1.1 Insulin regulates glucose homeostasis. Increased blood glucose level induces pancreatic β-cells to secrete insulin into the blood. Insulin binds to a series of tissues including liver, muscle and adipose to promote the glycogenesis and glucose uptake, slower down the gluconeogenesis, for maintaining the normal glucose level. Besides responding to insulin, the insulin responsive tissues also secrete their own hormones to regulate other tissues through this network. Betatrophin is known to secreted by liver that can maintain β-cell mass; Leptin is a satiety hormone secreted by adipocytes which could crosstalk to brain to affect the appetite. Insulin could promote bone resorption and in return, osteocalcin, released by bone cells could crosstalk to liver, adipose and islets to maintain their function. Insulin can also regulate its own proliferation and insulin secretion in an autocrine way. [9] [10]
Diabetes has been classified as a couple different types nowadays, the major two types are known as the “Type I Diabetes” (T1D) or “Insulin Dependent Diabetes Mellitus” (IDDM) and “Type II Diabetes” (T2D) or “Non-Insulin Dependent Diabetes Mellitus” (NIDDM). Surprisingly, these two major types have been notified as early as 100-200 B.C by Indian physicians [11] . However, the first scientific evidence was brought by Wilhelm Falta and Harold Himsworth after they set the standardized glucose/insulin tolerance test in human to distinguish the insulin sensitive from non-sensitive patients [12]. Insulin dependent T1D is featured in insufficient insulin production due to autoimmune response to the pancreatic beta-cells and it mostly affects young kids. T2D is more described as a “metabolic syndrome” with emergence of insulin resistance and obesity etc., and it is more common among adults [13]. In spite of the fact that there are uncommon monogenic types of T2D, most cases are results of extensive variety of environmental and genetic varieties [14]. One of the primary factor for T2D is the insulin resistance, the insulin responsive tissues, for example, the liver, muscle and adipose, ineffectively to respond to insulin. Therefore, gluconeogenesis is not shutting down in the liver and glucose in the blood is most certainly not taken up by muscle and fat. But insulin resistance itself does not really lead to T2D, beta-cell failure in later stage happens and ameliorate the progress of T2D [15].

Environmental and Genetic Contributions of T2D

Environmental factors are recognized to be one of the significant reason of the pathogenesis of diabetes [14]. Due to the life styles changes in the past 50 years, the incidence of T2D has been dramatically increased, therefore it is considered as the major risk factor for the progression of T2D. Excess food taking or imbalanced food consumption and lacking of physical activity synergistically result in overweight and obesity and constitute the major driver [16]. As an outcome, over-amount of free fatty acid (FFA) accumulates in the plasma and hampers the insulin-signaling pathway in muscle and liver [17]. Constant hyperglycemia because of excess food taking also brings glucotoxicity to both beta-cells and insulin-responsive cells and result in the dysfunction of them [18]. There are also some other risky reasons for the T2D include aging [19]. Recently, intrauterine condition has also been highlighted in contribution to the prevalence of T2D [14].
Besides the environmental factors, the genetic contributions have been considered as the fundamental reason for the pathogenesis of T2D for the past several years. The development of genome wide associate study techniques opens the gate for risky candidate gene searching and till 2012, there are about 65 variants have been identified, however, these variants contribute less than 30% of risk increment of T2D. The most intensely studied and confirmed diabetes risk single nucleotide polymorphism (rs1801282) with clear and obesity-independent effects on whole-body insulin sensitivity [20, 21] is located in exon 2 of the PPARG gene and results in the amino acid exchange P12A. PPARG encodes the lipid-activated nuclear receptor and transcription factor peroxisome proliferator-activated receptorγ(PPARγ). The P12Avariant, is only present in the PPARγ2 encoding transcript. This variant exerts its insulin-sensitizing effect directly inside adipose tissue, with increased release of fatty acids as a consequence of impaired adipose tissue insulin sensitivity which represents an attractive molecular mechanism of this SNP because fatty acids are well known to impair insulin sensitivity of skeletal muscle and liver [22]. Some of these gene with variants that have effect on insulin secretion or insulin sensitivity, but most part of these variations are located in non-coding regions of the gene, which makes it hard to study the physical functions. Therefore, even multiple genetic risk factors have been identified as strongly related with the pathogenesis of T2D, the pathological contributions are still not fully understood and requires further investigation.

The Islet Architecture and β-cell Fate

The endocrine part of pancreas is specifically named as the islets of Langerhans and are the only part producing the secreting hormones. They basically comprise of various cell sorts named α, β, δ, PP, and ε that secret the islet hormones glucagon, insulin, somatostatin, polypeptide Y, and ghrelin individually, and these hormones are required to maintain the glucose homeostasis at normal or stress state. Furthermore, the islets are known to have dynamic and plastic architecture that is proposed to be adjusted over the time of development[23]. During the progression of insulin resistance, pregnancy or T2D, the islets extend in size to make up for expanded insulin requirement . But afterwards, there is a considerable loss of β-cell mass because of environmental or genetic factors, inducing the 
serious hyperglycemia in cause of insulin deficiency. (Fig 1.2)

2Fig 1.2. Islet morphological changes during the progression of T2D

Fig 1.2. Islet morphological changes during the progression of T2D. Pancreatic islets contain different cell types includes α, β, ε and PP cells which respectively secrete glucagon, insulin, somatostatin and polypeptide Y. These hormones function together to maintain the glucose homeostasis. Islet has dynamic architecture when responding to stress condition such as insulin resistance or T2D. During the insulin resistance, large amount of insulin is required, which induced the compensatory expansion of islets to meet the high demand of insulin production. As the result, the glucose level would be maintained at normal physical level. When the β-cells could not meet the high demand due to a series of factors, there is a large β-cell loss result in low level of circulated insulin and constant hyperglycemia, which is the typical features of T2D [25].
While islets count to just ~1-2% of the whole pancreas, the insulin producing β-cells represent ~65-80% of the islet mass, constituting to roughly 2% of pancreatic weight. Furthermore, the rest other cell types are considered as non β-cells of the islets [26]. During the development, these distinctive cell types are known to emerge from a single progenitor cell that producing Neurogenin3 or Ngn3, a transcription factor that decides endocrine cell destiny [27]. Afterwards, other transcription factors, for example, Pdx1, Pax4, Nkx2.2, Nkx6.1, MafA, and Foxo1 help the β-cell fate determination [28]. While it had been learned as that β-cells proliferate by self-duplication from old β-cells instead of differentiation from stem cell [29], some other study suggested that multipotent cells inside the pancreas could differentiate into β-cells as well [30]. The latter study is also confirmed by researches showing how expressing the β-cell specific transcription factors in non β-cell could trigger a β-cell lineage in mice [31, 32]. Recent studies additionally showing that non-β cells, for example, α-and δ-cells could experience “trans-differentiation” into β-like cells when the mice are suffering from significant β-cell loss [33] [34]. On the other hand, a few researches demonstrate that the β-cells can likewise lose their fate or “dedifferentiate” into non-β or progenitor cells when losing any of the previously mentioned β-cell particular transcription factor [28]. Moreover, a current study demonstrated that human β-cells are capable of converting into α-cells with no genetic modification [35]. Taken together, all these researches have showed that the dynamic plasticity of islet cells.

Glucose Stimulate Insulin Secretion (GSIS)


One unique and significant feature of β-cells is to detect the blood glucose changes and secrete insulin into extracellular milieu in response to keeping the glucose levels within the range of 4-8mM [26, 36]. This is primarily accomplished by the take-up of extracellular glucose by the glucose transporter 2 [37] at the cell membrane. Right beyond the uptake of the glucose, the intracellular glucose sensor, glucokinase (Gck), subjects’ glucose moieties to quick metabolism system by glycolysis. This brings about the producing of three carbon products: pyruvate, which takes part in the tricarboxylic corrosive [38] cycle inside the mitochondrion to eventually create adenosine triphosphates [39] by means of the electron transport chain system. The ATP therefore leads to the increase of ATP/ADP proportion in the cytoplasm, activating the closure of the ATP sensitive potassium (KATP) channel. Vitally, mutation in the kir6.2 subunit of this channel was demonstrated to induce neonatal diabetes in both mice and human because of loss of insulin secretion as a consequence of constitutively open KATP channel [40, 41]. It has long been realized that glucose stimulates the closure of these KATP channels thus leading the slow membrane depolarization [42]. This promotes extracellular calcium influx by voltage dependent calcium channels and potentiates the releasing of insulin [43] (Fig. 1.3).

Fig 1.3 Glucose stimulated insulin secretion.One unique and significant feature of β-cells is to detect the blood glucose changes and secrete insulin into extracellular milieu in response to keeping the glucose levels within the range of 4-8m. Extracellular glucose was taken up by the glucose transporter 2 at the cell membrane. Right beyond the uptake of the glucose, the intracellular glucose sensor, glucokinase (Gck), subjects’ glucose moieties to quick metabolism system by glycolysis. This brings about the producing of three carbon products: pyruvate, which takes part in the tricarboxylic corrosive cycle inside the mitochondrion to eventually create adenosine triphosphates by means of the electron transport chain system. The ATP therefore leads to the increase of ATP/ADP proportion in the cytoplasm, activating the closure of the ATP sensitive potassium (KATP) channel, thus leading the slow membrane depolarization. This promotes extracellular calcium influx by voltage dependent calcium channels and potentiates the releasing of insulin.
3Fig 1.3 Glucose stimulated insulin secretion

Insulin is secreted in an oscillatory manner because of the blood glucose level and triggers downstream insulin signaling cascade in insulin-responsive tissues for the taking up glucose. It has well been shown that islets can be entrained to small changes in glucose and thus the plasma insulin has high frequency of oscillation. However, this capacity of entrainment of the islets is disturbed in patients with T2D [44]. It exhibits β-cell malfunction because of loss of insulin secretion is a major issue during the clinical indication of T2D.

Compensatory Islet Expansion During Insulin Resistance

During the state of obesity or insulin resistance, elevated plasma insulin levels (named hyperinsulinemia) has been found in polygenic mouse models showing insulin resistance and human subjects because of increasing of insulin secretion [45]. It has been suggested later that both in rodents and people, this improved insulin secretion is apparently because of an expansion in β-cell mass by either β-cell proliferation [46] or β-cell hypertrophy [47]. On the other hand, “β-cell failure” because of different genetic or environmental variables, is known to cause declined plasma insulin levels in diabetics. It has been showed that lessened levels of insulin are frequently associated with a noteworthy loss of β-cell mass because of β-cell apoptosis [48] [49].
Other than the diabetes perspective, it has been demonstrated that matured β-cells have long life-span and low proliferative rates at steady state. This is because of a potential limitation of the entry of matured β-cells into cell cycle [50, 51]. Other than this perception, later study suggested that adult β-cells do have the ability to proliferate [52]. In light of these findings on β-cell proliferation, a few research groups have revealed various proteins essential for assisting β-cell proliferation on knockout or transgenic mouse. Known cell cycle controllers including Cyclins D1 and D2Cyclin subordinate kinase 4 (Cdk4), Cdk inhibitors (CKIs), transcription factors Retinoblastoma (Rb) and p53, have been proved on genetic mouse models as regulators of β-cell proliferation and survival [53].
Although transient high glucose has been considered as the result of insufficient insulin secretion or insulin resistance, it has also been revealed to promote the compensate β-cell mass expansion [54]. This hypothesis was further supported recently by another observation, that it is the glucose metabolism, instead the glucose itself that triggers compensatory β-cell proliferation in vivo [55]. Some other attentions have been centered on the effect of activation of insulin/IRS2 pathway on driving β-cell proliferation. The components of the pathway including IRS2 [56] and AKT [57] were demonstrated to be fundamental for β-cell survival. Moreover, study has shown that the impact of insulin in β-cell proliferation is even stronger when with hyperglycemia [58]. It is notable that when in the state of severe insulin resistance, the pancreatic islets adjust themselves to meet the expanding requirement for producing and secretion more insulin by increasing their β-cell mass [47]. Other than the involvement of protein coding genes, a few non-coding RNAs (ncRNAs), mostly microRNAs and long ncRNAs (lncRNAs), have been shown in the “diabetogenes” list, adding on to the complex genetic architecture of human diabetes.

The Brief History of MicroRNAs

The most recent decade has seen huge attention regarding a new and special class of little ncRNAs such as microRNAs (miRNAs) in regulating the structure and function of β-cells. With the first observation of a miRNA, lin-4 in C. elegans, researchers showed how a gene product encodes two little RNAs, instead of a protein. Besides, they demonstrated that these small RNAs binds to the compensatory sites at the 3′ end of untranslated region [59] of lin-14, a development related heterochronic gene. This interaction is appeared to negatively regulate the expression of lin-14 by blocking its translation [60], proposing miRNAs as negative regulators of gene expression.
Even since the discovery of microRNAs, a lot of related research results have updated the mechanism of gene regulation to a novel level. miRNAs now are known as a group of small ncRNAs of ~21-22 nucleotides long that can complementary or non-complementary base-pairing the mRNA of protein coding genes, thus to regulate their expression at post-transcriptional level [61]. Actually, after the identification of lin-4, another miRNA called let-7 was revealed like lin-4 in both biogenesis and function level. Soon after, there starts a prevail in discovering new microRNAs mostly by high throughput sequencing technologies in research area and surprisingly, about 30,000 miRNA over about 200 species have been identified, which includes about 2,500 mature human miRNAs [62]. Many computational methods have also been produced to predict the potential targets of miRNAs based on the stable miRNA-target mRNA binding model [63].

MicroRNA Induced Gene Silencing

The intercellular gene silencing mechanism, termed as RNA silencing (RNAi) or post-transcriptional gene silencing (PTGS) is currently well known to be led by a group of small RNAs, for example, short interfering RNA (siRNA), piwi interacting RNA (piRNA), or the miRNAs. In general, their working mechanisms are similar and the difference exist mostly in their biogenesis inside the cells. Mature miRNA producing has been through several steps: transcribed from DNA by RNA polymerase II, primary miRNA (pri-miRNA) has much longer sequence. Once transcribed, the pri-miRNA is further processed to precursor miRNA (pre-miRNA) about 60 nucleotides long by enzyme Dorsa and DGCR8 protein complex [64]. Once pre-miRNAs are produced inside the nucleus, it will be export out of the nucleus by protein Exportin 5 to the cytoplasm in a Ran-GTP dependent manner [65]. Another important enzyme, which is also critical for mouse development, Dicer would recognize the pre-miRNA and process it to about 22 nucleotides long mature miRNA duplex form [66]. Only one of stands of the duplex will be transported to miRNA-induced silencing complex (RISC) by Dicer, and the other strand, termed miRNA* is usually degraded in the end [67].
There are several components on the RISC, and one key protein component is AGO family. There are four well characterized members of AGO family in human AGO1AGO2, AGO3 and AGO4. And AGO2 is expressed more often than other forms [68]. All the AGO proteins have the ability to slice mRNA because of their PAZ and PIWI “cleavage” domains under the guidance of miRNA sequence. Each miRNA has an important “seed” region, typically from 2nd-7th nucleotides, that could fully or partially bind to the mRNA 3’ UTR sequence. The base pairing condition between the miRNA and target mRNA determines the target recognition and binding of miRISC, but also the fate of the mRNA- to be cleaved or to be repressed in translation. In animal system, the miRNA does not fully complementary bind to the 3’ UTR of the target mRNA, through blocking the translation machinery, miRNA silencing the gene expression, without interference of the target stability. However, recent studies on the miRNA mediated gene silencing in mammalian cells reveals that miRNA may act through two step modes: at first, repressing the translation, then deadenylation and destabilization of target mRNA [69]. The de-adenylation has been suggested to induce mRNA degradation [70] and translation inhibition is probably the requisite of mRNA degradation in mammalian cells [71] (Fig 1.4).

4Fig 1.4 microRNA biogenesis and function


Fig 1.4 microRNA biogenesis and function. Initially, miRNA genes are transcribed as pri-miRNAs with a hairpin loop structure and processed to pre-miRNAs by the enzyme Drosha and DGCR8 inside the nucleus. After transport to the cytoplasm by Exportin-5, pre-miRNAs are processed by the Dicer complex, and turned into mature miRNAs. The mature miRNAs associate with the RNA-induced silencing complex (RISC) that mediates the interaction of miRNAs with target mRNAs. miRNAs mediate gene silencing in mammalian cells reveals that miRNA may act through two step modes: at first, repressing the translation, then deadenylation and destabilization of target mRNA. The de-adenylation has been suggested to induce mRNA degradation and translation inhibition is probably the requisite of mRNA degradation in mammalian cells. [72]

MicroRNAs in Insulin-Responsive Cell and Beta-Cell Function

Numerous microRNAs have now been known to play vital roles in monitoring glucose homeostasis. Hormones like insulin, glucagon emitted from β-cell and a-cells of pancreas separately have real parts in keeping up the body’s blood glucose level by cooperating in a synchronized way. The levels of hormones, like insulin and glucagon cooperating in a synchronized method to keep the glucose level, can be controlled by adipokines and free fatty acids secreted by adipose cells and liver. This organ crosstalk is controlled by miRNAs through targeting on the key components required for glucose homeostasis. These miRNAs are tissues specific or, on the other hand differentially located in pancreas or insulin responsive tissues (fat, muscle and liver) (Fig.1.5). These miRNAs are assumed causing diabetes mellitus by controlling pathways required for insulin secretion, insulin production and β-cell fate, or affecting the insulin sensitivity in insulin responsive cells. miRNAs such as miR-143, miR-145, and miR-802 were shown upregulation in the livers of obese mice and humans, indicating the roles in glucose homeostasis off-regulating during insulin resistance [73, 74]. Adipose specific miR-103/107 and miR-133, were reported to contribute to insulin sensitivity in mice by controlling the expression of caveolin-1 and Prdm16, respectively [75] [76]. Other than liver and adipose, the muscle has been studied as well. Recent studies explored when using technique to specifically knock down Lin28a, an RNA binding protein known to block let-7 function [77] [78], or overexpress let-7, it could render mice insulin resistant and glucose intolerant [79, 80]. These studies implicate the role of Lin28a/Let7 axis in regulating glucose metabolism.
In the β-cells, miR-375 was discovered as the most abundant miRNA that was shown could control insulin secretion by targeting Myotrophin (Mtpn), a gene involved in insulin secretion pathway [81]. Furthermore, by silencing this miRNA in zebrafish or mice, it was demonstrated that miR-375 is basic for the keeping the islet mass [82]. Dysregulation of certain miRNAs have been suggested could affect the growth pathways thereby controlling β-cell proliferation. For example, miR-7 could promote β-cell proliferation by activating the mammalian Target of Rapamycin (mTOR) pathway [83]. Moreover, miR-7 can affect the insulin granule exocytosis process by targeting Pax6 and some other important factors [84] [85]. In light of the basic roles of the miRNA pathway in β-cells, a few research groups have exhibited miRNAs, for example, miR-375, let-7, miR-9 [86], miR-212 [87], miR-33a [88], miR-30a- 5p [89] and miR-7 [85] all play positive or negative role on insulin secretion of β-cells under normal or insulin resistance condition.
5Fig.1.5 microRNAs in the crosstalk of pancC:UsersasusDesktop3333.jpgreas and insulin-responsive tissues to maintain the glucose homeostasis
Fig.1.5 microRNAs in the crosstalk of pancreas and insulin-responsive tissues to maintain the glucose homeostasis. Pancreatic α-cells and β-cells secret glucagon and insulin, respectively, in response to the change of blood glucose level. Elevated glucose-stimulated insulin secretion increases glucose uptake in adipose and muscle, and decreases glucose production in the liver. Adipose fat cells release free fatty acids (FFAs) and adipokines (leptin, adiponectin and TNF-α) to regulate insulin sensitivity, food intake and energy expenditure. Liver cells can release factors such as betatrophin to regulate the β-cell proliferation. This inter-organ crosstalk is controlled by miRNAs through targeting the expression of key components required for glucose homeostasis. These miRNAs are tissue-specific or differentially expressed in pancreas or insulin targeting tissues. The circulating miRNAs may serve as long-distance communicators. Some diabetes-associated miRNAs are also abundantly expressed in the brain, but their functions in relation to glucose homeostasis remain to be determined.
 

MiR-30d in Pancreatic β-Cells and Cancer Cells

One of the many microRNAs, miR-30d, has been previously identified promoting insulin production, but not insulin secretion in pancreatic β-cell line via upregulating of the transcription factor MafA expression [90]. Further study discovered that miR-30d directly inhibits TNF-α-induced MAP4K4 and prevents the inhibitory effect of MAP4K4 on MafA and IRS2, leading to a partial compensation on TNF-α-induced suppression on insulin production and insulin secretion [90]. MAP4K4 has been known as a serine/threonine protein kinase, and it has previously been reported to induce insulin resistance in both insulin-responsive tissues [91]. While MafA, as a β-cell-specific transcription factor of the Maf family members, it correlates with other insulin transcription factors includes NeuroD and PDX1 and binds to the insulin promoter to initiate the insulin transcription [92]. MafA has also been discovered could increase insulin secretion when overexpress in β-cells [93]. While overexpression of miR-30d has no significant effect on insulin secretion in this study, it implicates miR-30d may target on more than one regulators in β-cell to affect its function. Identification of those potential target genes of miR-30d and complete the pathway will lead to a more comprehensive understanding of the roles of miR-30d in β-cells.
MiR-30d has been studied widely besides the pancreatic β-cells, and mostly in different cancer cells. There is several studies claiming that miR-30d is serving as an onco-microRNA by regulating cancer cell metastasis, proliferation, apoptosis as well as cell autophagy. One study found miR-30d affects multiple autophagy involved genes including BECN1, BNIP3L, ATG12, ATG5 and ATG2 and thus suppresses the cell autophagy [94]. Another study implicates that miR-30d could bind to the 3’ UTR of Beclin 1, resulting in the inhibition of the cisplatin-activated autophagic response, which could protect human anaplastic thyroid carcinoma cells from apoptosis [95]. Both of these studies implicate that miR-30d assists cancer cells to escape autophagy triggered cell death and promote the cancer aggression. However, also in cancer cells, it is reported that miR-30d is negatively regulated by phosphoinositide 3-kinase (PI3K)/Akt signaling pathway and overexpression of miR-30d induces apoptosis and suppresses proliferation of renal cell carcinoma cells by destabilizing the mRNA of the oncoprotein metadherin (MTDH) [96]. This study reveals miR-30d’s role as a tumor suppressor, and combined with other study, it is further proved that miR-30d have multiple roles in cells and even though it has same predicted target mRNAs in different cells, the cell specific function mechanisms and pathways are guiding miR-30d’s specific functions in the cells, and it will be interesting to further explore how microRNAs are being regulated and how it regulates the cell functions under different circumstances.

AIM OF THIS STUDY

Dysregulated levels of miRNAs associated with T2D, insulin resistance or obesity may be restored to their physiological levels by using the techniques like miRNA mimics or miRNA inhibitors that are available. With the availability of such techniques, miRNA-based therapy actually provides a new insight of T2D treatment, which will significantly improve the treatment options for T2D. There are some findings about strong candidate miRNAs targets, but yet not enough.
The present thesis focuses on: Objective 1. Elucidation of one important miRNA, miR-30d’s role in β-cells under normal physiological or food-induced diabetes condition using different cell or animal models. Objective 2. This study tries to understand what are important target genes of the miR-30d pathway. Objective 3. This thesis aims to understand how important targets mediate the function of the miR-30d pathway and how they contribute to the regulation of glucose and homeostasis in vitro.
Here we will mostly focus on the miR-30d’s role in regulating the β-cell fate under both normal and stress condition. This will shed light on the functional role of the miRNA pathway and further aims to understand how miRNAs orchestrate the regulation of energy homeostasis.

CHAPTER 1.

microRNA-30d regulates mouse glucose homeostasis by inducing pancreatic -cell death and inhibiting the proliferation

Yiping Mao, Ramkumar Mohan, Jacob Schoenborn, Shungang Zhang, Weixiang Liu, Yiyou Gu, Xiaoqing Tang1

1This chapter is wrote as a manuscript for submission in the near future.

1.1 Introduction

Maintaining glucose homeostasis requires multiple hormones and enzymes, insulin from pancreatic β-cells is one of the essentials [97]. Adequate insulin secretion from β-cells is necessary to lower down the blood glucose level within a regular physiological range. Insufficient insulin secretion usually causes hyperglycemia and eventually β-cell death and reduced β-cell mass in both animal and human subjects with T2D [48] [98]. Apoptosis of β-cells is an important problem needed to be addressed also in islets transplantation for the treatment of T1D because it affects the durability of those cells [99]. Slowing down or stopping the apoptosis of β-cell is always a hot topic in studying the T2D, and so far, the molecular underpinnings remain incompletely understood, although multiple mechanisms have been suggested, including inflammatory cytokines, oxidative stress and endoplasmic reticulum stress [98] [100] [101]
MicroRNAs (miRNAs) are a group of noncoding RNAs about 22 nucleotides in length that negatively regulate gene expression by inhibition of protein translation or inducing mRNA degradation [102]. MicroRNAs primarily negatively regulate gene expression by binding to the 3’ untranslated region of their target mRNA with uncomplimentary binding by the seed region 8. Recent studies have discovered the importance of miRNAs in specialized β-cell functions [85, 103, 104]. miR-375, one of the most abundant and well-studied miRNAs existed in islets, is important for both insulin expression and secretion, and also in β-cell proliferation and adaptation to insulin resistance [105] [76]. Another miR-200 family (miR-141, miR-200c, miR-200a, miR-200b and miR-429) has been revealed play vital roles in β-cell fate [97]. Loss of miR-200 function could decrease the cell death under the stress or diabetic condition via affecting a series of apoptosis or stress related protein expression, including the essential beta cell chaperone Dnajc3, the caspase inhibitor Xiap and the tumor suppressor Trp53 [97].
One of the many microRNAs, miR-30d, has been previously identified promoting insulin production, but not insulin secretion in pancreatic β-cell line via up-regulating of the transcription factor MafA expression [84]. Further study discovered that miR-30d directly inhibits TNF-α-induced MAP4K4 and prevents the inhibitory effect of MAP4K4 on MafA and IRS2, leading to a partial compensation on TNF-α-induced suppression on insulin production and insulin secretion [84]. MAP4K4 has been known as a serine/threonine protein kinase, and it has previously been reported to induce insulin resistance in both insulin-responsive tissues [85]. While MafA, as a β-cell-specific transcription factor of the Maf family members, it correlates with other insulin transcription factors includes NeuroD and PDX1 and binds to the insulin promoter to initiate the insulin transcription [86]. MafA has also been discovered could increase insulin secretion when overexpress in β-cells [87]. While overexpression of miR-30d has no significant effect on insulin secretion in this study, it implicates miR-30d may target on more than one regulators in β-cell to affect its function. Identification of those potential target genes of miR-30d and complete the pathway will lead to a more comprehensive understanding of the roles of miR-30d in β-cells.
MiR-30d has been studied widely besides the pancreatic β-cells, and mostly in different cancer cells. There are several studies claiming that miR-30d is serving as an onco-microRNA by regulating cancer cell metastasis, proliferation, apoptosis as well as cell autophagy. One study found miR-30d affects multiple autophagy involved genes including BECN1, BNIP3L, ATG12, ATG5 and ATG2 and thus suppresses the cell autophagy [88]. Another study implicates that miR-30d could bind to the 3’ UTR of Beclin 1, resulting in the inhibition of the cisplatin-activated autophagic response, which could protect human anaplastic thyroid carcinoma cells from apoptosis [89]. Both studies implicate that miR-30d assists cancer cells to escape autophagy triggered cell death and promote the cancer aggression. However, also in cancer cells, it is reported that miR-30d is negatively regulated by phosphoinositide 3-kinase (PI3K)/Akt signaling pathway and overexpression of miR-30d induces apoptosis and suppresses proliferation of renal cell carcinoma cells by destabilizing the mRNA of the oncoprotein metadherin (MTDH) [90]. This study reveals miR-30d’s role as a tumor suppressor, and combined with other study, it is further proved that miR-30d have multiple roles in cells. This study, we explored the physiological role of the miR-30d in metabolic regulation in vivo, and studied its impact on β-cells survival in response to stress condition. We found that gain of function of miR-30d in mouse ameliorates β-cell apoptosis and glucose intolerance despite the fact of increased MafA expression. This gives hint of miR-30d’s multi-effects on cells, which are relevant to human diabetes.

1.2 Methods and Materials

Culture of Pancreatic Beta Cell line MIN6

The murine insulinoma cell line MIN6 was cultured in Dulbecco’s modified Eagle’s medium DMEM (GIBCO) containing 4.5g/l glucose supplemented with 15 % fetal bovin serum, 50 μM β- mercaptoethanol, 1 % penicillin-streptomycin (Invitrogen) in a humidified incubator at 37°C and 5 % CO2. The media was changed every two days and cells were passaged after reaching a confluency of about 80 %. Therefore cells were washed twice with 1xPBS and trypsinized with 0.05 % Trypsin (GIBCO) at 37°C for 3 minutes, centrifuged for 5 min at 700rpm and the cell pellet re-suspended in media.

Transfection

For loss and gain of function studies, MIN6 cells were transfected with 5 g of oligonucleotides (oligos) or 10 g of plasmid using the Amaxa Nucleofector II (Lonza) according to the manufacturer’s instructions. In brief, cells were trypsinyzed as previously described. Four million cells were centrifuged at 700 rpm for 5 min at 4 degree Celcius. The pellet was resuspended in 100 μl transfection solution and supplemented with either siRNA or plasmid DNA. As a control a pool of non-targeting siRNA or a plasmid containing an EGFP expression cassette was used. The cells were transferred to a cuvette and electroporated. Afterwards, cells were re-suspended in media and seeded in 6- or 24-well plates. 48 hours after transfection, cells were treated with low (1mM) or high (25mM) glucose without serum for 16 hours, then cell lysates or total RNA or cell based assays werer collected or performed and subjected to analysis by western blotting or real time RT-PCR or Elisa assay. For cytokine treatment, cells were treated with 10 ng/ml of cytokine mixture (TNF-α, IL-1 and IFNγ) in 25 mM glucose medium for specific time.

Mouse Islet Isolation and Culture

All mice were anethesized via i.p injection of mixure of α-chloralose at dosage of 100 mg/kg and urethane at dosage of 1000 mg/kg. Pancreatic islets were isolated and purified by intra-ductal perfusion of collagenase V (0.6 mg/ml) following the protocol described [106]. The purified islets were cultured in RPMI 1640 medium supplemented with 10% FBS and 1% penicillin-streptomycin for 24 to 72 hours according to the experiments. All experiments were carried out in accordance with the approval of the Animal Care Committee at the Michigan Technological University.

Insulin Secretion Assay

To measure the release of insulin, MIN6 cells were seeded following the transfection in 6-well plates. For secretion, cells were washed twice with Krebs Ringer secretion buffer and afterwards primed for 2 hours in secretion buffer containing 2.8 mM glucose at 37 °C. Subsequently buffer was replaced by 1 ml of secretion buffer containing 2.8 (low) followed by 25 mM [107] glucose and incubated at 37°C for 1 h respectively. Afterwards the supernatant was collected, centrifuged 5 min at 5,000 rpm to remove dead cells and insulin was measured using an Elisakit . The release of insulin was normalized to total insulin content of MIN6 cells. Therefore, cells were lysed in 1 ml of acid ethanol (1.5 % HCl, in 70 % ethanol) for overnight at -20°C, centrifuged at 14,000 rpm for 10 min at 4°C and insulin was measured in the supernatant using an Elisa kit as well.
Krebs Ringer buffer:
0.54 mM CaCl2,
4.74 mM KCl,
1.19 mM KH2PO4,
1.19 mM MgCl2
119 mM NaCl,
25 mM NaHCO3
10 mM HEPES,
0.5 % BSA
pH 7.4, sterile filtered, warm up to 37 °C before use

RNA Extraction

Total RNA was extracted as described in the manufactures’ instructions. In brief, isolated islets or cells were homogenized in TriZol (Life Technologies) reagent and incubated for 10 min at room temperature. Afterwards 200 l chloroform per ml TriZol was added and samples were vigorously and manually shook, followed by centrifugation at 13,000 rpm at 4 °C. The RNA containing upper aqueous phase was transferred to a new tube and RNA was precipitated by adding 500 l of isopropanol overnight at -20 °C. The next day, the whole liquid was transferred to column provided by miRNeasy kit, RNA was washed and dissolved in RNase free water followed the manufactures’ instructions. RNA concentrations were measured using the NanoDrop photometer.

cDNA Synthesis

cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit from Applied Biosystems. Therfore 800 to 2000 ng of total RNA were reversed transcribed using a pool of random hexamers and microRNA specific primers for 40 min at 42 °C in the presence of dNTPs, RNase inhibitor and reverse transcriptase. Afterwards the reaction was terminated for 5 min at 75 °C. cDNA was usually stored at -20 °C.

Quantitative real-time PCR for mRNA Transcripts

Quantitative realtime PCR was used to measure differences in RNA expression, using the Power SYBR green PCR Master kit from Applied Biosystems. In brief, a master mix was prepared for 10 l reactions, containing 2x SYBR Green Master Mix and 600 nM gene specific primer mix. The PCR reaction was carried out in 8-tubes strip format, combining 8 l of the master mix and 2 μl cDNA diluted template and using the Applied Biosystems StepOne Real-Time PCR System using following program: 10 mins at 95 °C, 40 cycles of 95 °C for 15 secs and 60 °C for 1 min. All samples were run in duplicate and the relative expression levels were calculated using the 2(-ΔΔCt) Method[108], with Hypoxanthine guanine phosphoribosyl transferase (Hprt) mRNA as an internal standard.
 

Quantitative real-time PCR for microRNAs

MiRNAs were quantified using TaqMan miRNA Assays from Life Technologies according to the manufactures protocol. For cDNA synthesis 2000 ng of total RNA were reverse-transcribed using a miRNA specific looped primer in a 25 μl reaction and the miRNA Reverse Transcription kit from Applied Biosystems. Subsequently, 4 μl of cDNA was used to run the qPCR reaction in a total volume of 15 μl and using miRNAspecific Primer. U6 small nuclear RNA was used as a normalizer. The qPCR reaction was performed in 8-tube strips and PCR reaction was monitored using the StepOne Real-Time PCR System from Applied Biosystems.
 

Animal Care and Treatment

Mice were maintained on a 12-hour light/dark cycle with ad libitum access to regular chow food, high fat diet (containing 60% kcal fat, Research Diets) in accordance to requirements. All experimental procedures were approved by Animal Care Facility of Michigan Technological University. All animals are on the C57BL/6 background except db/db mice (BKS. Cg-m+/+leprdb/J, stock no. 000642). MiR-30d overexpressed transgenic mouse founders are generated. Wild type and miR-30d overexpressed transgenic mice are backcrossed, controls from the same backcrossed generations are used for experiments.

Genotyping of Mouse Tail Biopsies

Mice were weaned at an age of 3 to 4 weeks and separated based on gender with not more than 5 mice per cage. The tail biopsies were digested in 600 μl tail lysis buffer TNES, supplemented with 35 μl Proteinase K (10 mg/ml) and incubated over night at 55 °C. Cell debris was spun down at 12,000 rpm for 10 min after mixing with 6M NaCl and supernatants were transferred to a new tube. Subsequently, genomic DNA was precipitated by adding 700 μl of Ethanol (100 %) at -80°C. Precipitated DNA was pelleted for 10 min at 13,000 rpm, washed once with 500 μl of Ethanol (70 %). The pellet was resuspended in nucleotiase free water and DNA concentration was determined using NanoDrop photometer.
1x TNES:
50 mM Tris
10 mM EDTA pH 8.0
0.4 M NaCl
0.5% SDS
Sterile by autoclaving
Genotyping-PCR was performed in a 20 μl reaction, using a Taq-Polymerase (LifeTechnologies) and following the manufacturer’s instructions. Gene specific Primers are summarized in Table 1.2.1.
1Table 1.2.1: Primer Sequences for Mouse Genotyping
Table 1.2.1: Primer Sequences for Mouse Genotyping

allele Forward Primer (5’-3’) Reverse Primer (5’-3’)
GFP ATCCACGCTGTTTTGACCTC GAGCAGGAGAAGCAAGAACG
Globin TAGATGTGCTTTACTAAGTCATCGCG GAGATCGAGCGGGCCCTCGATGGTAG

Blood Glucose Measurement, In Vivo Glucose Tolerance Test and in vivo Insulin Tolerance Test

For glucose tolerance tests, mice were starved for 16h and interperitoneally (i.p.) injected with glucose in saline at 1.0 g/kg body weight. For an insulin tolerance test, mice were fasted for 6h i.p. injected with 0.75 units insulin per kg body weight. For all tolerance tests, plasma glucose levels were measured after 0, 15, 30, 45, 60, 90 and 120 min from tail vein blood. To measure the release of insulin in vivo, blood was drawn from the postorbital vein after 0, 15, 30 and 45 min. The blood was spun for 10 min at 6,000 rpm at 4°C and plasma was transferred to a new tube. Insulin was quantified in 20 μl plasma using an Insulin ELISA kit.

Insulin/Glucagon Quantification using an Elisa kit

Insulin/Glucagon was quantified in mouse plasma, tissue extracts or cell culture supernatants using the Mouse insulin or glucagon Elisa kit or Ultrasensitive Elisa kit. Briefly, samples were collected and diluted respectively according to the sample source and 10 or 20 l of samples were transferred to the wells which coated with anti-insulin or anti-glucagon antibody at the bottom. Following the manufactures protocol, the sample were incubated with100 l enzyme conjugated buffer provided from the kit in room temperature at 800 rpm for 2 hours for insulin assay, and overnight at 4°C for glucagon assay. The wells were washed 5 times with wash buffer and dried. 200 l Substrate TMB were added into each well and react in room temperature for 15 mins followed by 50 l reaction stop buffer. The wells were read at 450nm. The OD values were normalized to the calibrators and the final insulin concentration results were recorded.

Pancreatic Insulin or Glucagon Content

The pancreatic insulin content was measured in pancreatic lysates. Briefly, mice were scarified at the pancreas was carefully dissected and the weight was measured using an analytical balance. Afterwards the tissue was homogenized in 5 ml of acid ethanol (1.5 % HCl, in 70 % ethanol) for three times 20 secs on ice. The homogenates were stored over night at -20 °C. Next, lysates were vortexed for 30 secs and spun for 15 min at 2000 rpm and 4 °C. The supernatants were transferred to a new tube and the 5 ml of acid ethanol was added again to the pellet and homogenate again. Repeat the above procedure and about 13-15ml supernatant was finally collected. The lysates were 1:500 diluted in PBS and insulin or glucagon was measured using an Elisa kit. The content of insulin or glucagon in the lysates was normalized by pancreatic weight.

Western Blotting

For Western Blotting, cells or tissues were homogenized and lysed in an appropriate volume of lysis buffer with protease inhibitors and phosphatase inhibitors cocktails and agitated for 12 min on ice. Afterwards, lysates were spun for 10 min at 13,000 rpm and 4 °C and supernatants were transferred to a new tube. The protein concentration was done using a bicinchoninic acid-assay (BCA) and different bovine serum albumin (BSA) concentrations as a standard. 80-100 g of protein was boiled for 10 min at 95 °C along with 1x SDS sample buffer. Protein lysates were resolved by SDS polyacrylamide gel electrophoresis (PAGE) using 1x SDS running buffer and subsequently transferred at 4 °C onto nitrocellulose membranes in 1x transfer buffer for overnight. The membranes were blocked for 2h with 5 % milk in 1x TBST at room temperature and subsequently incubated with respective primary antibodies over night at 4 °C. The next day, membranes were washed 3 times with 1x TBST and incubated with HRP-conjugated secondary antibodies. Followed by another 3 washes, membranes were developed based on chemiluminescence.
1x SDS sample buffer:
310 mM Tris/HCl
10 % sodium dodecyl sulfate
50 % glycerol

  1. mM EDTA

0.5 % bromophenol blue
5 % β-mercaptoethanol
1X SDS running buffer:
25 mM Tris/HCl
192 mM glycine,
0.1 % SDS, pH 8.3
1x Transfer Buffer:
25 mM Tris/HCl, pH 8.4
192 mM glycine
20 % methanol
1x TBST:
20 mM Tris/HCl
137 mM NaCl, pH 7.6
0.05 % Tween20

Immunohisto-chemistry Staining and In-situ Hybridization

For MIN 6 cell staining, cells were seeded on the acid treated coverslip till they grew into required population. The coverslips were transferred into a new 6-well plate and washed five minutes with 1x PBS for three times. The cells then were fixed using fresh 4% paraformaldehyde (PH7.4) for 20 minutes, followed by glycine and Triton-X treatment for 15 minutes, respectively. If BrdU was stained later, the cells were treated with extra 20 minutes of 2x HCl at 37°C. The whole coverslips were blocked with 5% Normal Donkey Serum (NDS) for 1 hour and primary antibody for overnight later at 4°C. Mouse anti-BrdU antibody was used for primary antibody incubation at 1:500 dilution in this study. The next day, cells were washed again with 1x PBS and treated with secondary antibody Mouse Alexa-Fluor 488nm at 1:500 dilution in 5% NDS in PBS for 2 hours at room temperature. Then the coverslips were incubated with DAPI for 1 minutes and mount on the glass slides with mounting medium [109].
Dissected mouse pancreas was fixed in 4% formaldehyde (pH 7.4) for 24h at 4°C and then processed routinely for paraffin embedding. Tissues were cut into 5 μm sections and adhere to glass slides (Superflost, Fisher Scientific).
For immunohistochemistry, slides are deparaffinized and rehydrated using xylene and a series of ethanol at different concentration from high to low respectively, followed by antigen retrieval steamed in sodium citrate buffer (PH 8.3) for 14 minutes. If BrdU will be stained, the slides were treated extra 20 minutes of 2x HCl at 37°C. After 2 hours blocking in PBS with 3% BSA on the tissue, mouse anti-insulin antibody, rabbit anti-GFP antibody, mouse anti-BrdU antibody, rabbit anti-BrdU antibody, rabbit anti-glucagon diluted in PBS with 1% BSA followed manufacturer’s recommendation for overnight incubation at 4°C. The next day, the immunodetection is processed with rabbit or mouse Alexa Fluor 488- or Alexa Fluor 596 conjugated secondary antibodies (Invitrogen) incubation for 2 h at room temperature. The slides were treated with DAPI solution and then cover slipped with anti-fading mounting media [109]. The images are captured on Olympus FluoView FV1000 Confocal microscopy or normal Leica fluorescence microscopy.
For in-situ hybridization, pancreas tissue slides are first deparaffinized and rehydrated and then treated with 40g/ml proteinase K (Roche Applied Science). Briefly, a total of 3 pmol of digoxigenin-labeled locked nucleic acid (LNA) probes (Exiqon) are diluted into hybridization buffer, and applied on the slides at 37 °C for overnight. Slides are then washed at 37 °C at 2xSSC solution and incubated with alkaline phosphatase-conjugated sheep anti-digoxigenin antibody at 1:1000 dilution (Roche Applied Science) for overnight at 4 °C. Alkaline phosphatase reaction was carried out with 50 mg/ml nitro blue tetrazolium/5-bromo-4-chloro-3-indolyl phosphate (NBT/BCIP) staining solution for overnight. Slides are coverslipped with mounting media and images are captured on Olympus fluorescence microscopy.

Pancreatic Cells and MIN6 Cells Proliferation Determination

Islet analysis after intraperitoneal injections of BrdU on seven consecutive days (100 μg/g Body Weight, Sigma) was performed on 5 μm sections of paraffin-embedded pancreas approximately 50 μm apart. The slides were performed normal immunohisto-chemistry staining using anti-BrdU and anti-insulin antibody. Images were obtained and quantified later.
For MIN6 cells, about 1x 104 cells were firstly seeded on 1M HCl pre-treated coverslip in 6-well plate with normal growing medium, allow them to grow two days. Then the plate was washed twice with 1x PBS and change to DMEM medium containing 25mM glucose for 16hrs. 10μl BrdU(0.33mg/ml) were added to the each 1ml medium for 4 extra hours of incubation. The plates were then processed to stain BrdU.

In-situ TUNEL Assay

The paraffin embedded pancreas slides were deparaffinized and rehydrated, followed by proteinase K (1 KUnit/ml) treatment for 20 minutes. Fresh 4% paraformaldehyde was used for post-fixation for 20 minutes. Then the slides were washed, and incubated with 50 l TUNEL enzyme solution provided from the TUNEL assay kit (Roche), covered by coverslip and incubated at 37 °C for 1 hour. The slides were then processed from immunohisto-chemistry staining starting from blocking with 5% NDS in PBS, and used Mouse anti-Insulin antibody or Rabbit Anti-Glucagon antibody as primary antibody for overnight incubation, and Alexa anti-mouse 594 nm or Alexa anti-rabbit 594 nm were used for secondary antibody incubation. Slides were mounted with mounting medium and the images are captured on Olympus FluoView FV1000 confocal microscopy or Leica fluorescence microscopy.

Slides Imaging and Quantification

Cell numbers from all islets in 3 sections were counted with ImagePro software from 4x or 20x images and normalized by tissue area. Beta cell mass was measured as the ratio of insulin-positive cell area to the total tissue area, multiplied by the weight of the pancreas using ImagePro software. For the quantification of BrdU-positive and TUNEL-positive cells, islets from at least 20 islets per slide and 3 different sections per animal were analyzed and normalized by total number of insulin positive cells. For quantification of islet size, insulin-positive cell area was calculated and divided by the total islet numbers in total 3 different sections per animal.

Statistical Methods

All results are expressed as mean ± Standard Error of Mean (SEM). Statistical significance is determined by unpaired Student’s t-test (two-tailed) and ANOVA analysis was performed for comparisons of three or more groups. A p-value of less than or equal to 0.05 was considered statistically significant (*, p < 0.05; **, p < 0.01; and ***, p <0.005).

1.3 Results

1.3.1 Generation of a transgenic mouse model that specifically overexpresses miR-30d at different levels in -cells.

To fully understand the physiological function of miR-30d in -cells in vivo, one transgenic mouse model which over-expressing miR-30d was generated. Briefly, the primary miR-30d fragment was inserted into a β-globin intron associated with an enhanced GFP reporter driven by mouse insulin I gene promoter[110], terminated by a fragment of the human growth hormone [2] terminator, resulting in a 12 kb MIP-miR30d-GFP-hGH for high-level expression of miR-30d (Fig 2.1A). The fragment was later microinjected into the fertilized eggs of mixed C57BL/6 and SJL mice by our collaborative Transgenic Animal Core Facility at the University of Michigan. The transgenic mice were screened for the presence of miR-30d transgene using PCR from tail biopsies (Fig 2.1B). 7 transgenic founders have been identified and breed in the animal facility at the Michigan Tech University. The transgenic mice were backcrossed with wild-type C57BL6 mice to obtain sufficient offspring for further study. Experiments were performed to confirm the miR-30d’s exclusive expression. RNA was collected from pancreatic islets of male mice from all 7 founder lines, and miR-30d expression level were checked. miR-30d in all transgenic lines exhibit higher expression level than wild type, but at different levels (data not shown). For better understanding the miR-30d’s role, two specific lines which have lower overexpression (TgL) and higher overexpression (TgH) were chosen for further study. The expression level of miR-30d in TgL pancreatic islets exhibited 2.5 times higher compared to wild type while TgH has about 15 times more than wild type has (Fig 2.2 A). Total RNA was collected also from liver, muscle, brain, heart, lungs, spleen and kidney of these mice, and miR-30d’s expression level showed no significant difference compared to wild type (Fig 2.2 B). This indicates the miR-30d is overexpressed specifically in pancreatic cells under the insulin promoter.

6Fig.2.1 miR-30d transgenic mice generation

Double-staining immunohistochemistry for GFP, a miR-30d-associated reporter, and insulin was performed on the pancreas sections from 8-week-old mice (Fig 2.2 C). While insulin staining was detected exclusively in islets of both transgenic and non-transgenic mice, GFP-staining was detected only in islets of transgenic mice, and just perfectly overlapped with the insulin staining, indicating that the GFP associated miR-30d was also specifically expressed in the beta-cells. Indeed, In situ hybridization for miR-30d on same pancreas section showed a significantly increased signals in the islets of transgenic mice compared to the islets of non-transgenic mice (Fig 2.2 C). All the data above proved the successful transgenic mice were generated.

7Fig 2.2 miR-30d specifically overexpressed in pancreatic islet cells.
Fig 2.2 miR-30d specifically overexpressed in pancreatic islet cells. (A) RNA was harvested from islets of 14 weeks old male mice, miR-30d expression level were tested using RT-PCR. TgL line showing about 2.5 times overexpression of miR-30d while TgH line has more than 15 times higher miR-30d exists in the pancreatic islets. (B) RNA harvested from major organs, including heart, kidney, brain, liver, spleen, lung, muscle and adipose tissue of 14 weeks old random transgenic male mice, miR-30d expression level were tested using RT-PCR. None of the tissue has significant difference at miR-30d level between wild type and transgenic mice. (C) Immunohistochemistry was performed on tissue section of 12 weeks old male mice. Anti-Insulin (Red) and anti-GFP (green) antibodies were used, 60x images were taken. GFP was specifically located only in the insulin positive area on transgenic mice tissue, and was perfectly overlaid with that area, but not on wild type sides. In situ hybridization was performed on slides from same mouse tissue, DIG-labeled miR-30d probe (blue) (20x images) were used to detect miR-30d level. miR-30d was only found on transgenic mice but not wild type mice slides. (**p< 0.01 vs. wild type)

 
 

1.3.2 Under normal diet condition, miR-30d does not affect the glucose homeostasis, nor -cell function.

TgL and TgH mouse lines kept crossing with wild type and transgenic or non-transgenic pups from same litters were used for further studies. To look at if miR-30d has any effects on the glucose homeostasis of mice, general related parameters were monitored including weekly body weight change, blood glucose level, plasma insulin level, glucose clearance ability and insulin response related insulin sensitivity test.
Firstly, body weight was measured weekly starting from 4 weeks old till 12 weeks old. While TgL showed no significant difference compared to wild type at both genders, TgH male mice were much leaner than wild-type starting from 5 weeks old (Fig 2.3 A), and females of TgH mice had no significant changes on body weight (Data no shown). Random blood glucose level was also checked biweekly or monthly starting from 4 weeks till 12 weeks old on both genders, no significant difference was identified (Fig 2.3 B). Blood samples were collected for checking the insulin level at plasma monthly from 8 weeks till 16 weeks old, though insulin were slightly increased in TgL at certain age, there was not dramatic difference for both lines in both genders (Fig 2.4 C, female data not shown).
It is possible that miR-30d doesn’t contribute to the blood glucose or insulin changes under normal physiological conditions without any stimulus or stress. To fully test if there are any glucose homeostasis related phenotypes on transgenic mice, glucose tolerance test followed by insulin tolerance test had been done. 14 weeks old mice where challenged with glucose or insulin administration after certain hours of fasting, and then glucose levels were measured every 15 minutes after and later every half an hour till 2 hours.

8Fig 2.3 miR-30d does not change the glucose or insulin level
1.1g glucose per kg of mouse body weight were administrated into mice via i.p injection after 16 hours fasting with only water access. It is found the glucose tolerance ability didn’t change with miR-30d overexpressed since the curves after the injection for both early 4 weeks old and later 14 weeks old mice showed no big difference in both transgenic lines (Fig 2.4 A, B). Plasma samples were collected simultaneously at 0, 15, 30, 45, 90 minutes after glucose administration and after later insulin analysis, both wild type and transgenic had similar response to glucose and insulin released (Fig 2.4 C). 0.75 Unit of insulin per kg of mouse body weight was injected after 6 hours of fasting with water access only for the mice and glucose level was measured every half an hour after till 90 minutes. This insulin sensitivity test was performed only on TgL line, and not surprisingly, the wild type and transgenic mice obtained about same insulin sensitivity (Fig 2.4 D).
Realizing under normal physiological condition, miR-30d didn’t have much effect on maintaining the glucose homeostasis which is reasonable since glucose homeostasis was well regulated under this condition, and the body only required that much insulin releasing from islets to maintain it even the islets possible was producing more insulin within the cell. To fully look through if miR-30d has any function on β-cells under normal condition, mouse pancreas tissues were collected at 4, 14 and 20 weeks old for immunohistochemistry or total insulin content identification. For immunohistochemistry, anti-insulin and DAPI were used to localize the β-cells and nuclei. Images were taken and total β-cell mass and average islet size were quantitively analyzed.
Image J or Image Pro software were used for pancreas immunohistochemistry images analysis. TgL and TgH mice data were analyzed separately and normalized to wild type mice. While β-cell mass didn’t have much change on both transgenic lines, which means β-cells had not significant proliferation or death with miR-30d overexpression, average islets size didn’t get any affected as well (Fig 2.5 A, B). Total pancreas was also isolated and homogenized with acid ethanol (75% ethanol with 1.5% HCl) on these mice to collect total insulin content from pancreas samples, which is to roughly check the insulin production in all islets of pancreas. While TgL showed no big difference again, TgH surprisingly had half fold decrement in insulin content (Fig 2.5 C). Protein samples were also isolated from islets of these mice and western blot was performed to check the MafA’s expression since miR-30d could promote MafA’s level through targeting Map4k4 in vitro. As expected, transgenic mice showed increased MafA level in both TgL and TgH mice and TgH mice has more compared to TgL (Fig 2.5 D).

9Fig 2.4 miR-30d does not change the glucose tolerance or insulin sensitivity

Since more MafA was produced in transgenic mice, but not more insulin was stored in the islets, even TgH line had less insulin content, this means miR-30d may target some other key proteins which is against the insulin production. The comparable glucose level and glucose clearance ability between both transgenic lines and wild type is possible that even with less insulin content, the cells could still compensate the requirements under the normal physiological condition to maintain the glucose homeostasis. These finding implicates that miR-30d may have stronger effects on β-cells when they are under stress condition.

10Fig 2.4 miR-30d decreased the insulin content in TgH mice

1.3.3 Under high-fat diet, miR-30d worsen the glucose homeostasis in TgH mice

High-fat food induced obesity is a common model for studying β-cells under stress condition since the body is actively responding to the extra high glucose or lipid brought by high fat diet but the glucose homeostasis is still maintained by β-cell compensation. During the state of obesity or insulin resistance, elevated plasma insulin levels has been found in polygenic mouse models showing insulin resistance and human subjects because of increasing of insulin secretion [45]. It has been suggested later that both in rodents and people, this improved insulin secretion is apparently because of an expansion in β-cell mass by either β-cell proliferation [46] or β-cell hypertrophy [47].
Mice from 4 weeks old started to have high-fat diet till the pancreas were collected at 16 weeks old or 20 weeks old. During the high fat feeding, general metabolic/glucose index was monitored weekly or monthly. It was not surprising that both TgH and TgL mice had slightly slower body weight gain starting from 7 weeks and no big difference between two transgenic lines (Fig 2.6 A). The glucose level of TgH also starting to rise dramatically starting from 8 weeks old till the end compared to wildtype. TgL mice however maintains comparable glucose level as wild type (Fig 2.6 B). Since mice were under high-fat diet feeding, the longer feeding to the mice, the larger chance those mice will develop insulin resistance which is featured as increased plasma insulin level. Wild type mice showed steady increased insulin in blood as the age grows, however, the plasma insulin of transgenic mice didn’t follow this rule. TgH mice surprising had lower plasma insulin as older which explained the increased glucose level (Fig 2.6 C) and insulin of TgL mice didn’t show much change.
11Fig 2.6 TgH mice exhibited hyperglycemia and decreased plasma insulin level under high-fat diet treatment

Glucose tolerance test was performed on male mice at 16 weeks old when they substantially exhibited food induced obesity. Surprisingly, when wild type still could retrieve the normal blood glucose 2 hours after the glucose injection, both transgenic mice revealed glucose intolerance compared to wild type, and the blood glucose of TgH mice stayed above 400 mg/dl even 1 hour after glucose administration. Area under curve analysis of GTT result implicated that TgH has significant decreased glucose clearance ability (Fig 2.7 A). To figure out what led to this, insulin tolerance test and plasma insulin analysis during GTT were performed and surprisingly, TgH mice had much significantly decreased plasma insulin (Fig 2.7 B) from the beginning till 45 minutes after the glucose injection. Moreover, TgH didn’t showing any strong glucose stimulated insulin secretion response as the insulin level barely elevated after the glucose injection (Fig 2.7 B). However, TgL mice still obtained regular glucose response and had comparable insulin released in plasma as wild type (Fig 2.7 B). Insulin tolerance test on these mice revealed TgH mice has better insulin sensitivity as the glucose level decreased much faster and more dramatically, and stayed at low level even 90 minutes after insulin injection when compared to both wild type and TgL mice.
The overall random glucose, plasma insulin level, GTT, ITT and glucose stimulated insulin secretion analysis implicates that TgH mice had poorly maintained glucose homeostasis with constant hyperglycemia and extremely low insulin secretion. With insufficient insulin, the insulin responsive tissues exhibited increased insulin sensitivity as a result. This could be caused by insulin secretion disability of β-cells or hampered insulin production.
 C:UsersasusDesktopThesis新建文件夹Fig7.png

12Fig 2.7 TgH mice exhibited glucose intolerance and poor glucose stimulated insulin secretion response
Fig 2.7 TgH mice exhibited glucose intolerance and poor glucose stimulated insulin secretion response. (A) Glucose tolerance test was performed on 20 weeks old male mice, 0.8g glucose per kg of body weight was i.p injected and glucose were measured every 15 or 30 minutes after till 2 hours. Area Under Curve was calculated as quantification of the glucose tolerance test curve. (B) Blood samples were collected simultaneously during the glucose tolerance test on 20-week male mice before glucose injection, 15, 30 and 45 minutes after injection. insulin level was checked using the plasma isolated from the blood. (D) Insulin tolerance test was performed on 20 weeks old male mice, 0.75 Unite insulin per kg of body weight was used for injection, glucose level was measured every 15 minutes after till 90 minutes. The glucose levels were all normalized to the starting glucose level of each individual before injection. (*p<0.05 vs. wild type)

1.3.4 Highly overexpress miR-30d in β-cells induces cell death and inhibit compensate β-cell proliferation in response to high-fat diet

In order to figure out whether the insulin secretion or insulin production was affected by miR-30d, β-cell mass and average islet size were analyzed on the pancreas tissue slides after all the in vivo tests and measurements. Using anti-insulin antibody for slides immune-staining, β-cell area was clearly visualized under microscope. While the islets of wild types were largely expanded in response to high demand of insulin as a result of high-fat diet, transgenic mice were not having similar responses (Fig 2.8 A). The islets of TgL mice maintained normal morphology as under normal diet condition without proliferation. However, TgH islets exhibited tiny, shrank and irregular shape which implicated they were not healthy (Fig 2.8 A). Besides, in some of the TgH islets, the insulin positive area counted less than 50% of the individual islet area from the images. Consider under normal physiological condition, 70%-80% of the islets cells are β-cells, TgH β-cells were assumed suffering a large number loss. Based on the islets images, quantifications were done to further confirm this hypothesis. It was surprising that β-cell mass of TgH was about just 20% as much as wild types’ and islet size was about 50%. TgL mice also had down-regulated β-cell mass and islet size, but not as dramatic as TgH mice (Fig 2.8 B, C). Since transgenic mice had decreased β-cell population, the total insulin content which measuring the insulin storage within the whole pancreatic β-cells was also significantly below wild type level, about 20% as much as wild type for TgH mice while 70% for TgL mice (Fig 2.8D). Correspondingly, the total glucagon content of TgH mice significantly elevated as more α-cells proliferated in response to β-cell loss. TgL mice had increased glucagon content as well but not as much as TgH mice (Fig 2.8 E).
13Fig 2.8 miR-30d induced significant β-cell loss in TgH mice

Fig 2.8 miR-30d induced significant β-cell loss in TgH mice. (A) Pancreas tissues were collected from 20 weeks old male mice for fixation. Anti-insulin antibody and DAPI were used for paraffin slides immune-staining. Insulin (Red) and nucleus (Blue) images were taken at 20x objective and merged as overlay image. (B) 4x images were taken on insulin and nucleus stained slides. All the insulin positive area and pancreas area were counted using ImagePro software. β-cell mass (mg) was roughly calculated as insulin area to pancreas area ratio by the whole pancreas wet weight (mg). (C) 20x images were taken on insulin and nucleus stained slides. All the islets on slides were counted and measured and average size (µm2) was calculated as area sum up divided by the islet number. (D) Whole pancreas tissues were collected from 20 weeks old male mice and homogenized in acid ethanol. Insulin was determined using Elisa kit and collected supernatant. (E) Whole pancreas tissues were collected from 20 weeks old male mice and homogenized in acid ethanol. Glucagon was determined using Elisa kit and collected supernatant. (*p<0.05, *** p<0.005 vs. wild type)

1.3.5 Overexpression of miR-30d inhibited β-cell proliferation and induced β-cell apoptosis under high-fat diet

To understand if the decreased β-cell mass and islet size was because of inhibited β-cell compensate proliferation in response to high-fat diet or miR-30d induced some other factors to trigger the β-cell death, β-cell proliferation and apoptosis were both examined. For proliferation, high-fat diet fed mice were received BrdU i.p injection (10mg per g of body weight) for 7 consecutive days and pancreas were collected and fixed for preparing tissue slides. Anti-BrdU antibody, anti-Insulin antibody and DAPI were used to stain BrdU, insulin and nucleus respectively. Islets image were taken and BrdU positive cell numbers in insulin positive area were manually counted. Wild type mice exhibited vigorously BrdU uptaking in islets while TgL mice had less BrdU counts. Moreover, only a few BrdU positive cells were identified on TgH mouse islets (Fig 2.9 A). The quantification of BrdU positive cell ratio in β-cells showed TgH had significant decreased proliferation. However, TgL had comparable BrdU positive β-cell ratio as wild type which implicated that low overexpression of miR-30d did not affect the proliferation but only highly overexpressed would (Fig 2.9 B). To further confirm miR-30d’s proliferation inhibiting role under stress condition or certain stimulation, similar research conducted on 12 weeks old normal diet fed female mice which were pregnant. Islets during pregnancy was known to have adaptive proliferation in response to placental lactogen secretion [111]. BrdU incorporation in islets started to increase significantly from pregnancy day 10 and reached peak at day 14 [111]. In our case, TgH females had only half of BrdU positive cell ratio of wild type and TgL had lower but not significantly compare to wild type (Fig 2.9 C).
Furthermore, in situ TUNEL signal which represents the DNA fragments results from apoptotic signaling cascades was also detected. Since β-cells are proliferating still in this state yet, barely any positive TUNEL was discovered on wild type mouse pancreas. TgL mice had average less than five cells per islets that containing TUNEL while surprisingly tremendous amount of TUNEL covered most part of the TgH mouse islets (Fig 2.9 D). Quantification data showed TgH had 70-fold increment in TUNEL signal compare to wild type while TgL had about 7-fold higher (Fig 2.9 E). These results demonstrate that miR-30d was giving a “toxic” effects including inhibiting proliferation and triggering apoptosis on β-cells under stress condition when given a high “dose”. Low “dose” also had similar effects but in a much mild condition and didn’t cause significant changes. And among these two reasons leading the β-cell population loss, stimulation on apoptosis pathway plays major role.

14Fig 2.9 Overexpression of miR-30d inhibited β-cell proliferation and induced β-cell apoptosis under high-fat diet

Fig 2.9 Overexpression of miR-30d inhibited β-cell proliferation and induced β-cell apoptosis under high-fat diet. (A, B) 10mg/g BrdU was administrated for 7 consecutive days through i.p. injection on 20 weeks old high-fat diet fed mice. Pancreas was collected after and fixed for tissue slides. Insulin (Red) and BrdU (Green) was immune-stained, DAPI (Blue) was used for nucleus staining. Images were taken under 20x objectives and total at least 2000 β-cells per slide were manually counted for quantification. (C) 10mg/g BrdU was adiministrated for 7 days on 12 weeks old normal diet female mice since E.4.5. Pancreas tissues were collected and fixed at E10.5. Slides were stained for BrdU, DAPI and insulin. More than 2000 β-cell per slides were manually counted for quantification. (D, E) Pancreas slides from 20 weeks old high-fat diet fed mice were stained for TUNEL(Green) using in situ TUNEL kit, followed by immune-staining for Insulin (Red) and DAPI (Blue). Images were taken under 20x objective and total at least 2000 β-cells per slide were manually counted for quantification. (F) The expression of CASP3 was analyzed in wild type and TgH mouse islets by western blot. β-actin was internal control. (G) MIN6 cells was transfected with self-designed Puro [1], CRISPR-Cas9-C [1] and CRISPR-Cas9-miR30d plasmids. Cells were incubated in DMEM medium without any serum or growth hormones for 16 hours and treated with cytokine mixture (TNF-α, IL-6, IL-10) for 6 hours. Cell lysates were collected later to analyze the expression of phosphor- mTOR. β-actin was internal control. (H) CRISPR-Cas9-30d transgected MIN6 cells were seeded on coverslip and treated with DMEM for 16 hrs without serum or growth hormone followed by 6 hours of cytokine mixture treatmen with 4 hours BrdU incubation. Cells were fixed and stained for BrdU and nucleus later and BrdU positive cells were manually calculated (*p<0.05, ***p<0.005 vs. wild type)
To further confirm that cell proliferation was inhibited and apoptosis was increased, cell lysates were collected and western blot was performed to see if any important proliferation or apoptosis factors had change or not. Not surprisingly, Caspase 3(CASP3) had significantly elevated expression in TgH mice islets (Fig 2.9 F). CASP3 is one of the cysteine-aspartic acid protease family members [112] and sequentially activation of them plays a central role of cell apoptosis signaling cascade [113].  One other evidence was found in permanently miR30d knock-out MIN6 cell line using CRISPR-Cas9 systems to introduce miR-30d DNA cleavage techniques. MiR-30d presented less than half-fold in MIN6 cells after introduction (Data not shown). In these MIN6 cells, phosphorylated-mTOR (p-mTOR) was found significantly increased under cytokines treatment when knock down the miR-30d expression (Fig 2.9 G). mTOR, which is short of mechanistic target of rapamycin, was recognized as a member of the phosphatidylinositol 3-kinase-related kinase family [114] and functioned as a protein kinase that regulates cell proliferation, cell growth, cell survival protein synthesis and metabolism etc [115]. Similarly, when looking at the BrdU positive cells in this miR-30d knock out MIN6 cells, the ratio increased 3-fold compared to control group after cytokine mixture treatment. In overall, these results demonstrated that miR-30d induced cell apoptosis and blocked the proliferation in β-cells under high-fat diet feeding.

1.3.6 CCNE2 is one of the targets of miR-30d to regulate the cell cycle

To understand which target genes are negatively regulated by miR-30d in activating cell apoptosis and inhibiting cell proliferation, we predicted candidate target genes using miRNA target prediction programs. Among many candidates, CCNE2 shows to have two miR- 30d complementary sites at 237 bp and 1298 bp region on its 3’-UTR (Fig. 2.10 A). These two complementary sites are highly conserved among its homologs in various mammalian species including humans and mice, suggesting a critical role of miR-30d in regulating CCNE2 which may function in pancreatic cells (Fig. 2.10 A). CCNE2 has been reported as one of miR-30d-5p’s targets in lung cancer cells and affects tumor cell proliferation [116] while in MIN6 cells, CCNE2 protein expression had decreased when overexpressed miR-30d and when knock down it, CCNE2 protein increased in expression (Fig 2.10 B). However, CCNE2 messenger RNA level didn’t change significantly when overexpressed miR-30d under high glucose (G25mM) treatment (Fig 2.10 C). Silencing CCNE2 in MIN 6 cells seems had feedback effect on miR-30d level that miR-30d all decreased under both low and high glucose treatment (Fig 2.10 D) significance will be determined after experiment repeating). Consider CCNE2 is a cyclin protein which controls the cell cycle transition [117] [118] [119], and miR-30d also had negative regulation on β-cell proliferation, BrdU ratio in MIN6 cell was determined after silencing CCNE2 using siRNAs. Surprisingly, BrdU ratio didn’t change between control and CCNE2 silenced cells under either low and high, with or without cytokine mixture treatments (Fig 2.10 E, significance will be determined after experiment repeating). However, with reduced CCNE2 protein (Fig 2.9 F), MIN 6 cells exhibited less MafA and IRS2 expression under cytokine mixtures treatment, but without cytokine induced stress condition, there was no change (Fig 2.10 G). In summary, CCNE2 is one of the miR-30d’s targets in MIN6 cells and it may contribute to insulin function by affect MafA and IRS2. However how it regulates proliferation still worth further exploration.
15Fig 2.10 CCNE2 is one of miR-30d’s targets in MIN6 cells

Fig 2.10 CCNE2 is one of miR-30d’s targets in MIN6 cells. (A) Bioinformatics prediction of the interaction between miR-30d and the 3’-UTRs of CCNE2 of mouse species. (B) Overexpression of miR-30d down-regulates CCNE2 protein expression in MIN6. Knock down miR-30d up-regulates CCNE2 in MIN6. Both were under G25 Mm treatment for 16 hours before harvested lysate. (C) CCNE2 messenger RNA levels after overexpressing miR-30d in MIN6 cells. (D) miR-30d level after silencing ccne2 in MIN 6 cells. (E) BrdU positive cell ratio after silencing ccne2 in MIN 6 cells. Cells were treated with BrdU for 4 hours two days after transfection and then fixed for BrdU staining. (F) CCNE2 protein level decreased after si-ccne2 plasmid transfection under both G1Mm and G25Mm conditions. (G) Silencing of ccne2 de-activated the expression of IRS2. 48h after transfection with siRNA against ccne2 or Scrambler in MIN6 cells, cytokine mixtures were added or not added into the medium for 16 hours and cell lysates were prepared for Western blots to detect the expression of IRS2, MafA and Actin.

 

1.3.7 miR-30d promoted BNIP3 expression in β-cells

Consider miR-30d induced severe cell apoptosis in vivo under high-fat diet feeding, several classical apoptosis pathway related genes expression has been tested. Among them, BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3) coded genehad been identified as a one that had significantly 3.8-fold increased level in transgenic mice (Fig 2.11 A). BNIP3 has been known induces an atypical programmed cell death pathway through its BN3 domain or other protein interactions [120]. Furthermore, BNIP3 protein level had been determined in both transgenic mice and CRISPR-C-miR30d MIN6 cells, and it increased in transgenic mice lysates and inhibited in miR-30d knock out MIN6 cells (Fig 2.11 B).
16Fig 2.11 miR-30d promoted BNIP3 expression

Fig 2.11 miR-30d promoted BNIP3 expression. (A) BNIP3 m RNA level was determined from total RNA isolated from islets of 16 weeks old male mice using Taqman q-PCR. HPRT was used as internal control. (B) BNIP3 protein level was determined for proteins isolated from islets of 16 weeks old male mice (Left) or CRISPR-Cas9-miR-30d transfected MIN6 cells (Right). (*p<0.05 vs. wild type)

17Fig 2.11 miR-30d may inhibit β-cell function as well
Fig 2.11 miR-30d may inhibit β-cell function as well. (A, B) Pre-insulin and MafA m RNA level was determined from total RNA isolated from islets of 16 weeks old high-fat diet fed male mice using Taqman q-PCR. HPRT was used as internal control. (B) Insulin secretion assay was determined on CRISPR-Cas9-miR-30d transfected MIN6 cells under G2 mM (Low glucose) and G 20 mM (High glucose) respectively. Insulin was determined using Elisa and normalized to total insulin level of each individual. All data normalized to level of Puro[1] under low glucose condition. (*p<0.05, *** p<0.005 vs. wild type or Puro)

1.3.8 miR-30d may inhibit β-cell function as well

Since miR-30d highly inhibited the total insulin production in pancreas, it was not yet known if the decreased insulin production was because of significantly decreased β-cell population only or if miR-30d affected insulin transcription in each as well. To exclude this possibility, mRNA extracted from islets of high-fat diet fed mice was used for Pre-insulin expression determination. Surprisingly, Pre-insulin decreased in TgH mice and MafA also had inhibited expression, but only in TgH mice (Fig 2.12 A, B). Furthermore, glucose stimulate insulin secretion assay was performed on CRISPR-C-30d MIN6 cells, interestingly, miR-30d knocking out significantly improved insulin secretion under both low and high glucose conditions (Fig 2.12 C). These data implicated that miR-30d may be involved in specific β-cell function including insulin gene transcription and insulin secretion besides inducing cell apoptosis.

1.4 Discussion

Diabetes is one of the most common and complicate metabolic diseases, and type 2 diabetes is usually featured of defects in both β-cell function and insulin sensitivity [121]. The mechanism driven β-cell dysfunction had been widely studied, yet not completely known. Recently, a group of miRNAs was identified in human pancreatic β-cells using the next-generation sequencing technology, and the relation between these miRNAs and diabetes pathogenesis was revealed [122]. Some other studies on pancreas development and β-cell functions had implicated miRNAs’ pivotal roles in regulating the expression of a variety of genes critical for cell survival and insulin secretion [123]. Some plasma-based microarray data also discovered that many miRNAs were potential biomarkers which could indicate the progress of diabetes [124]. Among all these dysregulated miRNAs during diabetes, miR-30d was found differentially expressed in both diabetic islets and MIN6 cells under high glucose stimulation [90]. Further study showed that miR-30d regulated insulin gene transcription under stress condition. Based on these findings, we speculated that miR-30d might participate in diabetes progress.
Our results obtained from miR-30d transgenic mice showed that under normal physiological condition, overexpression miR-30d at different level didn’t induce any glucose homeostasis changes, including glucose tolerance, random blood glucose level or any sign of diabetes progress. Mouse model of obesity induced by high-fat diet feeding for at least 12 weeks presented surprisingly impaired glucose metabolism in transgenic mice, especially TgH mice across indexes of body weight, random glucose level, glucose tolerance. Furthermore, we found miR-30d significantly decreased total insulin production in pancreas by inhibiting obesity induced β-cell compensate proliferation and triggered widely and wild β-cell death, while knocking out miR-30d in MIN6 cells promoted cell proliferation even under stress condition as well as lessen the dying cell numbers. These findings suggested that miR-30d was involved in the progress of diabetes pathogenesis.
Previous study about miR-30d mainly known to be focused on its regulation of cancer progression. Several studies claiming that miR-30d is serving as an onco-microRNA by regulating cancer cell metastasis, proliferation, apoptosis as well as cell autophagy. miR-30d affects multiple autophagy involved genes including BECN1, BNIP3L, ATG12, ATG5 and ATG2 and suppresses the cell autophagy [88]. Another study reveals that miR-30d could target Beclin 1, resulting in the inhibition of the cisplatin-activated autophagic response, which protects human anaplastic thyroid carcinoma cells from apoptosis [89]. Both studies implicate that miR-30d assists cancer cells to escape autophagy triggered cell death and promote the cancer aggression. However, also in cancer cells, it is reported that miR-30d is negatively regulated by phosphoinositide 3-kinase (PI3K)/Akt signaling pathway and overexpression of miR-30d induces apoptosis and suppresses proliferation of renal cell carcinoma cells by destabilizing the mRNA of the oncoprotein metadherin (MTDH) [90]. These investigations implicated that miR-30d was highly possible to relate to apoptosis but with complex mechanisms. Our results were somehow consistent with these findings based on cancer, showing a negative role of miR-30d in pancreatic β-cell survival in response to stress condition, indicating similar molecular function network in diabetes and cancer.
Cycline E2 (CCNE2) was discovered targeted by miR-30d in result of inhibiting the non-small cell lung cancer (NSCLC) proliferation [116] and a study reported CCNE could be one of the factors used as screening markers which activate human β-cell proliferations [125]. Limited literature reported CCNE2’s role in β-cell, but it is widely known as a factor that specifically interact with CDK inhibitors and plays a role in cell cycle G1/S transition. Our results are consistent with previous findings in both cancer and β-cells, and more importantly, confirmed the important molecular relation between miR-30d and CCNE2 in β-cells.
While our results were showing that miR-30d had more significantly role in triggering apoptosis other than inhibiting proliferation, the direct target of miR-30d that induced the cell death pathway had yet been found. However, multiple cell death related factors had been found upregulated by miR-30d, including BNIP3. BNIP3 was known as a proapoptotic protein that could induce autophagy, apoptosis etc and dysregulated under stress such as hypoxia through hypoxia inducing factor-1 [126]. BNIP3 expression was implicated played a role in de-regulation of cell apoptosis in many types of cancers [127] [128] [129] [130] and also heart diseases [131, 132]. Since β-cell suffered from cell death during the later stage of T2D, miR-30d promoted BNIP3 up-expression may reveal a possibility of BNIP3’s new role in T2D pathogenesis. But how miR-30d up-regulated BNIP3 as an indirect target still needs to be explored. Interestingly, another BNIP3 like factor, BNIP3L was confirmed one of the targets of miR-30d in different human cancer cells [94] [133], however, BNIP3L wasn’t targeted in β-cells by miR-30d (Data not shown).
Downregulation of miR-30d expression by CRISPR-Cas9 system effectively increased the glucose induced insulin secretion in MIN6 cells revealed miR-30d’s role in regulating specific β-cell function more than monitoring the cell survival, which means miR-30d had a complex regulation network in β-cells. This role need to be further proved in vivo, and through which pathway did miR-30d affected the insulin secretion will be question to solve in the further.
Although more and more therapy or drugs are available that has improved the lives of all patients affected by diabetes, it is still one of the most challenging diseases in the world. Our findings suggest that miR-30d significantly triggered β-cell death under food induced obesity by affecting CCNE2 and BNIP3 and loss of miR-30d may induce insulin secretion. This implicate that miR-30d can be considered as a candidate for diabetes treatment and the therapeutic effects of miR-30d worth further exploration.
 
 

CHAPTER 2.

Identification of miR-30d’s targets in pancreatic β-cells

Yiping Mao, Shungang Zhang, Guiliang Tang, Xiaoqing Tang1

1The following chapter is part of the manuscript under preparation.

2.1 Introduction

MicroRNAs (miRNAs) are one of the three classes of small RNAs (including small interfering RNAs, microRNA and Piwi-interacting RNAs) which are about 20-24 nucleotides long. The discovery of miRNA biology emerged since gene lin-4 and let-7 had been identified controlling the timing in C. elegans but without coding any protein. Instead, they acted as a short RNA transcript that regulated timing involved gene expression on post-transcriptional level [60, 134, 135]. Since then, more and more miRNAs had been identified and have been shown to regulate variety of gene expression by targeting to their 3’ untranslated regions (UTRs) and inhibiting the translation initiation (in animals) or conducting mRNA cleavage (in plants) [136-138].
In animals, mature miRNA synthesis has been through several steps: transcribed from DNA by RNA polymerase II, primary miRNA (pri-miRNA) transcript has much longer sequence and is further processed to precursor miRNA (pre-miRNA) about 60 nucleotides long by enzyme Dorsa and DGCR8 protein complex [139]. Pre-miRNAs are exported out of the nucleus and are cleaved by RNase III domain-containing protein Dicer to about 22 nucleotides long mature miRNA duplex form [140]. One strand of the duplex will be transported to miRNA-induced silencing complex (RISC) by Dicer, and the other strand, termed miRNA* is usually degraded in the end [67]. In mammalian cells, RISC mainly consists of Dicer, Argonaute 2, transactivating response RNA-binding protein (TRBP) and protein activator of the interferon-induced protein (PACT) etc. In RISC, miRNA plays the main role of targeting the mRNA by uncomplimentary binding to the target 3’UTR. Many animal miRNAs are highly conserved between species (citation 34), especially when looking at the crucial ‘seed’ region of each miRNA, which are mostly taking responsibility of specific targeting [141].
The miRNA study came across from biosynthesis to function and target recognition. Since one miRNA targets a variety of gene transcripts through the seed region of miRNA binding to the 3’ UTR of mRNA, miRNAs have been predicted to target more than 60% of human genome [102]. There are many bioinformatics online tools that could predict miRNA: mRNA interactions. miRbase, as the biggest online registry, collects miRNAs information from various of species which were identified already in research or using computational methods [142]. Another famous RNA database is named Rfam, which provides miRNA sequences and helpful for both miRNA identification and target prediction [143]. Even with the availability of these online tools, there is still possibilities that the predictions are not correct when tested in vitro or in vivo because of cell type or species specificities. In this case, target-specific experimental validation is a necessary way in recognition the interactions. The methods include well-established techniques such as quantitative real-time PCR (qRT-PCR) [144], Northern blot analysis, western blot [145] and 5’-rapid amplification of cDNA ends (5’-RACE) [146].
Firstly, the miRNA and its target mRNA should be co-expressed in a specific cell type which is typically confirmed by Northern blot analysis or qRT-PCR [147]. Although most of miRNA targets are known regulated at both mRNA and protein level, some targets are manifested regulation at only protein level [148]. Therefore, a typical approach to validate the function of miRNA: mRNA interaction is to overexpression or knock down of a given miRNA in a cell and then followed by western blot analysis and qRT-PCR.
In recent decades, advancements in gene editing field have given us many options to change the expression of a specific miRNA. To gain of functions, miRNA mimic or specific recombinant adeno virus vectors could be used to selectively overexpress a miRNA. However, these approaches only allow transient overexpression and require performing the follow experiments within a short time. To achieve loss of function study of a particular miRNA, there are various methods available include antagomirs [149], short tandem target mimics (STTMs) [150], classic zinc finger nuclease (ZFN) [151] and transcription activator-like effector nucleases (TALENs) [152]. However, these methods could not perfectly silence miRNAs and had some disadvantages like off-target effects and not being able to completely knock out gene expression. Thus, researchers are constantly looking for techniques that could better silence genes. Recently, CRISPR/Cas9 system is turning to a trend topic in gene silencing or gene editing field.
CRISPR (Clustered regularly interspaced short palindromic repeats) and the nuclease Cas9 are one of the machineries that prokaryotes utilize to help protect themselves from virus invading [153]. CRISPR loci typically consists of conserved CRISPR associated (Cas) genes and a series of repetitive sequences separated by variable unique sequences called ‘spacers’ which match the foreign genes (protospacers) [154-156]. Firstly, the protospacer region of the viruses or plasmids introduces into the CRISPR loci as a new spacer by Cas protein. The spacer array is later transcribed to the CRISPR RNAs (crRNAs) which is modified by Cas endoribonucleases at the repeat sequences, from long precursor turned to short sequences. The small crRNAs direct a Cas ribonucleoprotein complex to cut the target double DNA strands in the viruses or plasmid genome [157]. Scientist has reengineered this system and utilized this to cut their interested target DNA gene sequences. Because CRISPR/Cas9 guided DNA cutting requires only 20nt long guide sequence in crRNAs that could match the target DNA and a short-conserved motif in the downstream of the crRNA binding region, called protospacer adjacent motif (PAM) whose sequence canonically is 5’-NGG-3’, the easy designable and high target DNA silencing efficiency made the CRIPSR/Cas9 system a new star of gene editing tool [158].
In this study, we picked synthesized multiple guide sequence of miR-30d gene, incorporated the plasmid into MIN6 cells and successfully and permanently knocked down miR-30d expression in MIN6 cells using this CRISPR/Cas9 system. We chose the line with maximum efficiency of gene knocking down and used it throughout the study to find targets of miR-30d in pancreatic β-cells.

2.2 Methods and Materials

Cell culture and generation of miR-30d knocked down lines

The murine insulinoma cell line MIN6 was cultured in Dulbecco’s modified Eagle’s medium DMEM (GIBCO) containing 4.5g/l glucose supplemented with 15 % fetal bovin serum, 50 μM β- mercaptoethanol, 1 % penicillin-streptomycin (Invitrogen) in a humidified incubator at 37°C and 5 % CO2. The media was changed every two days and cells were passaged after reaching a confluency of about 80 %. Therefore cells were washed twice with 1xPBS and trypsinized with 0.05 % Trypsin (GIBCO) at 37°C for 3 minutes, centrifuged for 5 min at 700rpm and the cell pellet re-suspended in media.

Transfection

For loss and gain of function studies, MIN6 cells were transfected 10 g of plasmid using the Amaxa Nucleofector II (Lonza) according to the manufacturer’s instructions. In brief, cells were trypsinyzed as previously described. Four million cells were centrifuged at 700 rpm for 5 min at 4 degree Celcius. The pellet was resuspended in 100 μl transfection solution and supplemented with plasmid DNA. The cells were transferred to a cuvette and electroporated. Afterwards, cells were re-suspended in media and seeded in 6- or 24-well plates and treated with puromycin for antibiotics screening.
For required analysis, cells were treated with low (1mM) or high (25mM) glucose without serum for 16 hours, then cell lysates or total RNA or cell based assays werer collected or performed and subjected to analysis by western blotting or real time RT-PCR or Elisa assay. For cytokine treatment, cells were treated with 10 ng/ml of cytokine mixture (TNF-α, IL-1 and IFNγ) in 25 mM glucose medium for specific time.

RNA Extraction

Total RNA was extracted as described in the manufactures’ instructions. In brief, isolated islets or cells were homogenized in TriZol (Life Technologies) reagent and incubated for 10 min at room temperature. Afterwards 200 l chloroform per ml TriZol was added and samples were vigorously and manually shook, followed by centrifugation at 13,000 rpm at 4 °C. The RNA containing upper aqueous phase was transferred to a new tube and RNA was precipitated by adding 500 l of isopropanol overnight at -20 °C. The next day, the whole liquid was transferred to column provided by miRNeasy kit, RNA was washed and dissolved in RNase free water followed the manufactures’ instructions. RNA concentrations were measured using the NanoDrop photometer. cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit from Applied Biosystems. Therfore 800 to 2000 ng of total RNA were reversed transcribed using a pool of random hexamers and microRNA specific primers for 40 min at 42 °C in the presence of dNTPs, RNase inhibitor and reverse transcriptase. Afterwards the reaction was terminated for 5 min at 75 °C. cDNA was usually stored at -20 °C.

Quantitative real-time PCR for mRNA Transcripts

Quantitative realtime PCR was used to measure differences in RNA expression, using the Power SYBR green PCR Master kit from Applied Biosystems. In brief, a master mix was prepared for 10 l reactions, containing 2x SYBR Green Master Mix and 600 nM gene specific primer mix. The PCR reaction was carried out in 8-tubes strip format, combining 8 l of the master mix and 2 μl cDNA diluted template and using the Applied Biosystems StepOne Real-Time PCR System using following program: 10 mins at 95 °C, 40 cycles of 95 °C for 15 secs and 60 °C for 1 min. All samples were run in duplicate and the relative expression levels were calculated using the 2(-ΔΔCt) Method[108], with Hypoxanthine guanine phosphoribosyl transferase (Hprt) mRNA as an internal standard.

RNA sequencing by HiSeq analysis

Total RNA from cells was isolated and checked for its RNA integrity number (RIN) using Bio-analyzer. cDNA library was constructed in University of Michigan DNA sequencing core facility. Libraries were then carefully sequenced using Illumina HiSeq platform and enumerated signals reflecting RNA expression levels were then measured.

Statistical Analysis

All results are expressed as mean ± Standard Error of Mean (SEM). Statistical significance is determined by unpaired Student’s t-test (two-tailed). A p-value of less than or equal to 0.05 was considered statistically significant (*, p < 0.05; **, p < 0.01; and ***, p <0.005).

2.3 Results

2.3.1 Identification of differentially expressed genes in miR-30d knockdown line using HiSeq analysis

In total 5 different guide RNAs were designed targeting different locations on miR-30d coding gene sequence with a PAM site in downstream. MIN6 cells were transfected with the constructed plasmid and screened by antibiotics incubation. RNA was collected from the survived cells to check miR-30d expression. Because of off-target effects, not all the construction was effective. The cell line with maximum knockdown expression of miR-30d (miR-30d KO) along with its negative control (Puro) were used for later analysis.
Total RNA was extracted from control and miR-30d KO cells, the quality of samples was analyzed using Bio-analyzer. Only the samples with RNA integrity number higher than 8 were chose to sequence the transcripts using Illumina HiSeq. The raw data had been analyzed and categorized. In total 23283 genes’ expression had been examined, differentially expressed transcripts were determined based on 3 criteria: Test status, False discovery rate (FDR) with q-value less than or equal to 0.05 and Fold change of the differential expression greater than or equal to 1.5 or less than or equal to 0.67. Results showed that about 6% genes (1422) out of the total 23283 genes were differentially expressed (Fig 3.1 A). Among these differentially expressed genes, 652 genes were upregulated with fold change greater than or equal to 1.5 and 770 genes were downregulated with fold change less than or equal to 0.67 (Fig 3.2 B). List of all differentially expressed genes were provided in Appendix A.
18Figure 3.1. Differentially expressed genes after knocking down miR-30d

Fig 3.1. Differentially expressed genes after knocking down miR-30d. (A, B) HiSeq analysis of total RNA samples collected from control (puro) and miR-30d knocked down (miR-30d KO) MIN6 cell lines revealed 6% of the total genes which is 23283 had expressed differentially. Among these differently expressed genes, 652 genes were up-regulated for at least more than or equal to 1.5-fold change and 770 genes were down-regulated to at least less than or equal to 0.67-fold change.

2.3.2 Functional enrichment analysis of differently expressed genes in miR-30d KO

Database for Annotation, visualization and Integrated Discovery (DAVID) bioinformatics analysis tool was used to categorized the differently expressed genes based on the gene function to further understand data results. Some other resource and online tools were applied in annotate the result, including Gene Ontology (GO), PANTHER, KEGG pathways, INTERPRO and SMART protein domains. Index for each category consists gene list, copy number of genes, p-values, FDR, Bonferroni and Benjamini corrections. The enrichment analysis showed that the differently expressed genes were largely correlated with cell membrane (29%), cell signaling (23%), ion binding (21%), ion channel activity (1.5%), secretion (1.4%) etc. which implicated that miR-30d might have role in regulating β-cell secretion activity. Other than that, it was revealed that knocking down miR-30d also had large effect on cell proliferation (4.7%) and cell apoptosis (3.9%) and cell cycle (4.2%) with significance. Some other cell homeostasis and metabolic related gene had been significantly changed as well including glycoprotein coding genes, genes regulate protein kinase cascade, cell morphogenesis etc. (Table 3.1)
The functional enrichment analysis proved that miR-30d’s multiple potential roles in MIN6 cells and further confirmed the in vivo findings in miR-30d OV mice that miR-30d regulates the cell proliferation, cell apoptosis and might involve in insulin secretion activity as well. Complete analysis results are provided in Appendix B.

Table 3.1 Functional Enrichment analysis of differently expressed genes in miR-30d KO cell.
2Table 3.1 Functional Enrichment analysis of differently expressed genes in miR-30d KO cell.
Table 3.2. List of predicted targets of miR-30d in MIN6 cells
3Table 3.2. List of predicted targets of miR-30d in MIN6 cells

 

2.3.3 Identification of novel targets of miR-30d

To narrow down the researching range, differently expressed genes of interested categories had been chosen to for further identify the potential targets of miR-30d, including genes involved in cell proliferation (67 genes), cell death (56 genes), cell cycle (59 genes), transcription factor activity (67 genes), regulation of secretion (25 genes) and ion-channel activity (52 genes). Since microRNAs’ function was to inhibit the mRNA translation in mammalian cells, candidates with increased fold change were only chosen for further validation. miRDB, miRWalk and miRanda, three microRNA target prediction databases were used to predict the targets for mmu-mir-30d based on the complementary binding site on 3’ UTR to miR-30d seed region sequence. All the candidates were screened for miR-30d binding sites (Fig 3.2) and 14 potential targets of miR-30d in MIN6 cells were found (Table 3.2). Among them, Ccne2 had already been confirmed as miR-30d’s actual target in different type of cells [116]. Although it still requires further validation of these candidates, like luciferase assay, q-PCR and western blot, these findings implicated the multi possible roles of miR-30d and provided some future directions of miR-30d study in β-cells.

2.4. Discussion

In order to analyze how microRNA delivers the function, identifying microRNA targets is always an important part of microRNA functional studies among all species, which requires systematic detailed approaches. In this part of study, we were able to identify some of the potential novel targets of miR-30d in pancreatic β-cells using high throughput sequencing technique and CRIPSR/Cas9 system.
CRISPR/Cas9 is widely used in genome editing research. Compared to common techniques to introduce miRNA loss of function, like antisense inhibitors, ZFN and TALEN, CRISPR/Cas9 system has improved competencies and could target a specific miRNA loss of function in both of in vivo and in vitro conditions despite the small size of miRNA [159] [160] [161] [162]. More importantly, it is able to deliver stable mutation(s) to knock down the miRNA expression under in vitro condition. In this case, we incorporated the use of this tool in MIN6 cell line, generated miR-30d loss of function stable cell line, in aims to understand the genome wide impact of miR-30d expression knockdown.

19Fig 2.3.2 Alignment details of miR-30d and 14 candidate targets using miRNA.org database
Fig 3.2 Alignment details of miR-30d and 14 candidate targets using miRNA.org database

Dr. Guiliang Tang kindly helped designed 5 different guide RNAs for targeting miR-30d coding gene, Shungang Zhang constructed each of them into vectors with chimeric gRNA and Cas9 protein coding sequence and transfected into MIN6 cells. Cells were screened using puromycin treatments and survived cells were analyzed miR-30d expression level to test the CRIPSR/Cas9 introduced knockout or knockdown. Interestingly, only 2 out of 5 lines showing significant miR-30d down-regulation (data not shown), considered off-target effects, and the knocking down was stable between passages. Single line with maximum reduction of miR-30d level (~60%) was picked for further studies.
The purpose of this study was using stable miR-30d knocked down cell line as a loss of function model to screen out the potential miR-30d targets since microRNA has direct inhibiting roles of their targets. Using high throughput sequencing technology combined with gene database based functional enrichment analysis, we were able to identify total more than 1400 differently expressed genes in knockdown cells out of total 23283 genes. Among the up-regulated 652 genes, interested gene function categories were chosen for further analysis including cell death, proliferation, cell cycle, ion-channel activity, transcription factor activity and secretion. Multiple miRNA database, like miRDB, miRWALK and miRanda were combined to the final screening of the candidates which should have complementary or uncomplimentary binding sequence at their 3’ UTR to miR-30d. And we found 14 genes met all the requirements, which implicated that their potential roles as miR-30d targets in MIN6 cells. As an uncompleted study, further procedures still needed to confirm these targets including using luciferase assay, qRT-PCR and western blot in both MIN6 cell and pancreatic β-cells.
These 14 candidate targets cover different functions in cells. Among them, Ccne2 has already been confirmed as miR-30d direct target in cancer cells regulating cell cycles [116] and also in β-cells (Chapter 1), no other genes have been learned in microRNA study. However, some of them already suggested regulates pancreatic β-cell function.
Chemokine (C-X-C motif) ligand 10 (CXCL10) is expressed in a variety of cells including human islets in response to IFN-γ [163], and increased level in serum of patients with T1D [164] and T2D [165]. Treating CXCL10 to human islets, the cells exhibited decreased viability, impaired insulin secretion with less insulin mRNA as a result of sustained activation of Akt, JNK and cleavage of PAK-2 which switched Akt signals from proliferation to apoptosis [166] [167]. Another strategy using CXCL10 DNA vaccination to neutralize CXCL10 in a spontaneous diabetes model of NOD mice was able to enhance the proliferation of pancreatic β-cells, thus suggested to be useful in maintaining β-cell mass against T1D [168]. Based on these studies, CXCL10 was revealed playing roles in regulating β-cell proliferation, apoptosis and secretion as well, which was consistent with the phenotypes of miR-30d OV and KO in vivo and in vitro and thus made it a strong candidate target of miR-30d in β-cells. Another candidate from the list we identified, Kif11, was found as a T2D risk gene. Genome-wide association studies on different large populations had revealed that single-nucleotide polymorphisms in IDE-KIF11-HHEX gene locus are associated with susceptibility to T2D [169] [170] [171]. Candidate gene Irf4 was recently implicated as required for insulin sensitization during obesity and endotoxemia [172]. And Serpine1 could in part induce insulin secretion under the inducement of porphyromonas gingivalis [173].
Most of the candidates we have found haven’t been studied in diabetic models or β-cells, but widely studied in other areas. The genes involved in cell proliferation, cell cycle and cell death mostly played roles in tumorigenesis, tumor cell migration and invasion. For example, Serpine1 was known for enhancing cell migration and apoptosis resistance in head and neck carcinoma [174], Hells, a chromatin remodeling gene, was identified could mediate the epigenetic deregulation genes which drive retinoblastoma [175] and also recognized as novel prognostic marker in renal cell carcinoma [176]. Upregulation of E2f8 promotes cell proliferation in breast cancer [177], lung cancer [178] as well as papillary thyroid cancer [179] through regulating cell cycle. In variety of microRNA studies, many microRNAs have been found involved in mediating cancer cell proliferation and death. miR-30d also had been revealed pivotal roles in tumorigenesis [180] [181] [182]. These candidates provide some insights about miR-30d’s unknown yet significant roles in pancreatic β-cells survival. However, experiments like luciferase reporter assay are needed to further validate the interaction.
Conclusively, this study carries a tremendous amount of information and meanings. Further studies on these identified potential targets of miR-30d will certainly enhance the complete understanding about the multi-functions of miR-30d and its interactions with multi-targets in pancreatic β-cells.

CHAPTER 3.

Determination of miR-30d function in Cre-STTM30fl/fl mice

Yiping Mao, Ramkumar Mohan, Jacob Schoenborn, Guiliang Tang, Xiaoqing Tang1

1The following chapter is part of the manuscript under preparation.

3.1 Introduction

 
To validate one gene’s importance as a therapeutic target, in vivo model is often necessary. Using gain-of-function transgenic rodent model is one of the ways, However, loss-of-function knockout or knockdown rodent model is more prevalently used and more convincing in illustrate the importance of the interested gene. While using continuous expressed vector to generate specific gene overexpressed mice, it is prevalent to apply drug induced Cre-LoxP recombination in generating gene knock out mouse which is headed in flexibility of the gene knock out timing and cell-type specificity (proposal 40). In here, we have generated a pancreatic β-cell specific miR-30 knockout mouse model to dissect the physiological function of miR-30d.
To study the miRNA function, much of the analysis has relied on gain-of-function approaches, for example miRNA overexpression [183, 184]. This method is informative, however has to be interpreted with caution. Since usually a strong constitutive promoter is required for overexpression approach, not only will the miRNA be mis-expressed, but at high level that the endogenous role of the miRNA might be misrepresented [185] [186]. In this case, conclusion drawn from these gain-of-function strategies often requires further confirmation in more standard loss-of-function mutation models. When talking about generating miRNA knockout animal model, either therapeutic methods to decrease miRNA or genetic manipulation to silence miRNA are available to choose. However, a challenge lies in identifying the phenotype in these models. Due to the small size of the gene that lower chances to find a mutation within the gene or miRNA redundancy that most miRNAs have highly related family members which could compensate the function loss of the miRNA and thus minimize the possible phenotypes, it is unfeasible to generating mutants of a miRNA with many family members [186] [187]. To address these issues, numbers of alternative methods have been developed to inhibit miRNA function in animals, including miRNA sponges, using modified oligonucleotides that are complimentary to miRNA sequence in animal cell culture [188] [189], artificial miRNA take use of RNAi machinery [190] and miRNA target mimicry [191]. Short tandem target mimicry (STTM) methodology was developed by Dr. Guiliang Tang which contains two copies of miRNA partially complementary sequences linked by a spacer. When introduced in vivo, STTM triggers target miRNA degradation thus generate the loss-of-function model [150]. More importantly, it simultaneously blocks all the members in a miRNA family through single introduction event [192] to exclude the possible miRNA redundancy effect.
In this study, we utilized STTM technology to block all miR-30 family members’ expression and inducible Cre recombinase system to manipulate the conditional silencing timing in mice for better control [193]. In our miR-30 knockout mice, STTM-30 is constantly inhibited in the control mice and only when Cre recombinase is active, the stop cassette between the two loxP sites will be cleavage out and STTM-30 thus is functional to inhibit miR-30 expression.
Similar models have been available for some other microRNA functional studies in pancreatic β-cells, like miR-200 family [97], miR-375 [194] and miR-7 [83] but not miR-30. Since miR-30d has been shown to regulate pancreatic β-cell proliferation and apoptosis under stress condition and also may affect glucose stimulated insulin secretion, using STTM-30 mice, miR-30’s function will be validated and some other metabolic phenotypes might be identified as well.

3.2 Methods and Materials

Animal Care and Treatment

Mice were maintained on a 12-hour light/dark cycle with ad libitum access to regular chow food or high fat diet (containing 60% kcal fat, Research Diets) in accordance to requirements. All experimental procedures were approved by Animal Care Facility of Michigan Technological University. All animals are on the C57BL/6 background except db/db mice (BKS. Cg-m+/+leprdb/J, stock no. 000642). STTM-30f/f mouse founders are generated from company (Applied Stem Cell). Ins-Cre mouse were crossed with STTM-30f/f to produce Cre;STTM30f/f mice.
 

Mouse Islet Isolation and Culture

All mice were anethesized via i.p injection of mixure of α-chloralose at dosage of 100 mg/kg and urethane at dosage of 1000 mg/kg. Pancreatic islets were isolated and purified by intra-ductal perfusion of collagenase V (0.6 mg/ml) following the protocol described [106]. The purified islets were cultured in RPMI 1640 medium supplemented with 10% FBS and 1% penicillin-streptomycin for 24 to 72 hours according to the experiments. All experiments were carried out in accordance with the approval of the Animal Care Committee at the Michigan Technological University.

RNA Extraction

Total RNA was extracted as described in the manufactures’ instructions. In brief, isolated islets or cells were homogenized in TriZol (Life Technologies) reagent and incubated for 10 min at room temperature. Afterwards 200 l chloroform per ml TriZol was added and samples were vigorously and manually shook, followed by centrifugation at 13,000 rpm at 4 °C. The RNA containing upper aqueous phase was transferred to a new tube and RNA was precipitated by adding 500 l of isopropanol overnight at -20 °C. The next day, the whole liquid was transferred to column provided by miRNeasy kit, RNA was washed and dissolved in RNase free water followed the manufactures’ instructions. RNA concentrations were measured using the NanoDrop photometer.

Quantitative real-time PCR for miRNA Transcripts

cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit from Applied Biosystems. Therfore 800 to 2000 ng of total RNA were reversed transcribed using a pool of random hexamers and microRNA specific primers for 40 min at 42 °C in the presence of dNTPs, RNase inhibitor and reverse transcriptase. Afterwards the reaction was terminated for 5 min at 75 °C. cDNA was usually stored at -20 °C.
Quantitative realtime PCR was used to measure differences in RNA expression, using the Power SYBR green PCR Master kit from Applied Biosystems. In brief, a master mix was prepared for 10 l reactions, containing 2x SYBR Green Master Mix and 600 nM gene specific primer mix. The PCR reaction was carried out in 8-tubes strip format, combining 8 l of the master mix and 2 μl cDNA diluted template and using the Applied Biosystems StepOne Real-Time PCR System using following program: 10 mins at 95 °C, 40 cycles of 95 °C for 15 secs and 60 °C for 1 min. All samples were run in duplicate and the relative expression levels were calculated using the 2(-ΔΔCt) Method [108], with Hypoxanthine guanine phosphoribosyl transferase (Hprt) mRNA as an internal standard.

Genotyping of Mouse Tail Biopsies

Mice were weaned at an age of 3 to 4 weeks and separated based on gender with not more than 5 mice per cage. The tail biopsies were digested in 600 μl tail lysis buffer TNES, supplemented with 35 μl Proteinase K (10 mg/ml) and incubated over night at 55 °C. Cell debris was spun down at 12,000 rpm for 10 min after mixing with 6M NaCl and supernatants were transferred to a new tube. Subsequently, genomic DNA was precipitated by adding 700 μl of Ethanol (100 %) at -80°C. Precipitated DNA was pelleted for 10 min at 13,000 rpm, washed once with 500 μl of Ethanol (70 %). The pellet was resuspended in nucleotiase free water and DNA concentration was determined using NanoDrop photometer.
Genotyping-PCR was performed in a 20 μl reaction, using a Taq-Polymerase (LifeTechnologies) and following the manufacturer’s instructions. Gene specific Primers are summarized in Table 4.1.
Table 4.1: Primer Sequences for Mouse Genotyping
4Table 4.1: Primer Sequences for Mouse Genotyping

allele Forward Primer (5’-3’) Reverse Primer (5’-3’)
STTM-30/eGFP TGATCATGGTTGAGGTCTGA GTGAGCCACTCACTGTGCAC
Cre GCGGTCTGGCAGTAAAAACTATC GTGAAACAGCATTGCTGTCACTT
Globin TAGATGTGCTTTACTAAGTCATCGCG GAGATCGAGCGGGCCCTCGATGGTAG
PolyA-Fu/Frt-R1 CTAGATAACTGATCATAATCAGCCATACCACAT CCGCGAAGTTCCTATACCTTTTG
425N/CAG-R GGTGATAGGTGGCAAGTGGTATTCCGTAAG CATATATGGGCTATGAACTAATGACCCCGT

Blood Glucose Measurement, In Vivo Tolerance Tests and in vivo Insulin Release

For glucose tolerance tests, mice were starved for 16h and interperitoneally (i.p.) injected with glucose in saline at 1.0 g/kg body weight. For an insulin tolerance test, mice were fasted for 6h i.p. injected with 0.75 units insulin per kg body weight. For all tolerance tests, plasma glucose levels were measured after 0, 15, 30, 45, 60, 90 and 120 min from tail vein blood. To measure the release of insulin in vivo, blood was drawn from the postorbital vein after 0, 15, 30 and 45 min. The blood was spun for 10 min at 6,000 rpm at 4°C and plasma was transferred to a new tube. Insulin was quantified in 20 μl plasma using an Insulin ELISA kit.

Insulin Quantification using an Elisa kit

Insulin was quantified in mouse plasma using the Mouse insulin Elisa kit or Ultrasensitive Elisa kit. Briefly, samples were collected and diluted respectively according to the sample source and 10 or 20 l of samples were transferred to the wells which coated with anti-insulin antibody at the bottom. Following the manufactures protocol, the sample were incubated with100 l enzyme conjugated buffer provided from the kit in room temperature at 800 rpm for 2 hours for insulin assay, and overnight at 4°C for glucagon assay. The wells were washed 5 times with wash buffer and dried. 200 l Substrate TMB were added into each well and react in room temperature for 15 mins followed by 50 l reaction stop buffer. The wells were read at 450nm. The OD values were normalized to the calibrators and the final insulin concentration results were recorded.

Statistical Methods

All results are expressed as mean ± Standard Error of Mean (SEM). Statistical significance is determined by unpaired Student’s t-test (two-tailed) and ANOVA analysis was performed for comparisons of three or more groups. A p-value of less than or equal to 0.05 was considered statistically significant (*, p ≤ 0.05; **, p ≤ 0.01; and ***, p ≤ 0.001).

3.3 Results

3.3.1 Generation of β-cell specific miR-30 KO mice (STTM-30fl/fl)

STTM-30 construct was designed and completed by Ruiwen Fan, and then used for the generation of the STTM30-shRNA transgenic mouse model. It involved several steps. Firstly, a genetically modified mouse line was generated, designated H11-3attP (H11P3), by knocking in three tandem attP sites (3attP), namely landing pad, into the mouse H11 locus (Fig 4.1 A). The second step was to integrate the STTM30 sequence into the H11P3 sites. It was accomplished by microinjecting integration cocktail into the pronuclei of heterozygous zygotes from H11P3 FVB X FVB WT mice. The integration cocktail consisted of plasmid pBT346-STTM-30 vector DNA and in vitro transcribed ϕC31 mRNA. Since the STTM30 transgene is adjacent to an attB site, the ϕC31 integrase enzyme will catalyze site-specific DNA integration between attB on the TARGATT donor vector and attP within the H11P3 modified locus in the mouse genome (Fig 4.1 B). Later, zygotes were injected with the integration cocktail were implanted into CD1 foster mice to produce offspring. Finally, STTM30 transgenic founder mice was identified by PCR-based genotyping (Fig 4.1 C, D).
20Fig 4.1 Transgene integration and genotyping of the STTM30 transgenic model

Fig 4.1 Transgene integration and genotyping of the STTM30 transgenic model. (A) genomic sequence with H11P3 landing pad, (B) founder with STTM30 transgene inserted at third attP site. (C) Genotyping STTM-30 founder mice, 1141#11A 1 to 11 (lanes 1 to 11). Primers: a, 425N/CAG-R; b, STTM30-F/eGFP-R3; c, polyA-Fu/Frt-R1; d, 100bp ladder DNA marker used in lanes “M”.
Once STTM-30fl/fl mice were obtained, they were crossed with Cre mice to generate miR-30d KO mice. Total RNAs were collected from the islets of miR-30d KO mice to check the miR-30 family member expression thus to confirm the knock out efficiency. From the results (Fig 4.2), both miR-30d and miR-30a from miR-30 family were down-regulated about one-fold in the islets, while other random miRNA like miR-9 didn’t show alter in expression. This confirmed the STTM-30-shRNA transgene and the Cre-loxP system worked out successfully. miR-30 KO mice would be used for further exploration of function of miR-30 family in pancreatic β-cell.
21Fig 4.2 Validation of down-regulated expression in miR-30 KO mice

Fig 4.2 Validation of down-regulated expression in miR-30 KO mice. Islets were harvested and hand-picked from both non-transgenic and miR-30 KO mice. Total RNA was extracted and miR-30a, miR-30d and miR-9 level were quantified using qRT-PCR. Results showed a significant decrease of miR-30a and miR-30d expression level from miR-30 family in miR-30 KO mice, while miR-9 selected as a random miRNA had no change. All expression of miRNAs was normalized to snor202 level and the data are shown as mean ±SD. (* with p < 0.05, compared to non-transgenic control).
22Fig 4.3 miR-30 KO mice maintained normal body weight and glucose level

Fig 4.3 miR-30 KO mice maintained normal body weight and glucose level. Mice were separated in gender at age of 4 weeks. Both non-transgenic control and miR-30 KO mice were treated with normal rodent diet with regular water access. Male mice showed no significant change in terms of (A) body weight and (B) random fed blood glucose level between control and knockout mice over the 10 weeks. Similarly, no difference was observed in female group regards to their (C) body weight and (D) random fed glucose level.

3.3.2 miR-30 knock out mice obtained normal body weight and glucose level

miR-30 KO mice were treated with normal diet first to observe the phenotype. To eliminate the gender bias, male and female were separated at 4 weeks old and started measurement at the age of 5 weeks. Body weight and blood glucose level were monitored weekly under fed conditions till age of 15 weeks. Results have shown no significant difference in their body weight or random fed glucose level between control and miR-30 KO mice groups in male group. Female miR-30 KO mice had slightly increased body weight over the whole measure period, however was not statistic significant. (Fig 4.3 A-D).
Male miR-30 KO mice were tested for glucose or insulin tolerance level at 15-16 weeks old. For glucose tolerance test, both non-transgenic and miR-30 KO were starved for 16 hours, followed by 10% glucose 10 μL per gram body weight administration. Blood glucose level then was measured before injection and then every 15 minutes after till 2 hours. Blood samples were also collected simultaneously when checking the glucose at 0, 15, 30, 45 minutes. For insulin tolerance test, mice were fasted for 6 hours and followed by 0.75 Unit per kilogram body weight insulin injection. Results showed actually miR-30 KO mice didn’t have tolerance difference compared to controls (Fig 4.4 A, B), and the glucose stimulated insulin secretion was comparable between miR-30 KO and control as well (Fig 4.4 C). Though there were not any difference in body weight, glucose and clearance when knockout miR-30, it was consistent with the known fact that overexpression miR-30d didn’t change any glucose metabolism in vivo under normal diet condition (Chapter 1).
23Fig 4.4 miR-30 KO didn’t change the glucose or insulin tolerance under normal diet condition.

Fig 4.4 miR-30 KO didn’t change the glucose or insulin tolerance under normal diet condition. At age of 15-16 weeks, male mice were subjected to glucose tolerance test and insulin tolerance test. (A) After 16 hours of starvation, glucose tolerance test was performed. Glucose was measured before glucose injection and every 15 minutes after injection till 2 hours. (B) Mice were fasted for 6 hours before insulin injection and glucose level was measure before injection and every 15 minutes after injection till 90 minutes for insulin sensitivity test. All glucose levels were normalized to the glucose level before injection at 0 minute and showed as percentage. (C) Plasma was collected simultaneously during glucose tolerance test at 0, 15, 30, 45-minute time point for checking plasma insulin level before and after glucose stimulation.

3.4 Discussion

 
In this part of study, we have successfully generated pancreatic β-cell specific miR-30 knockout mouse model using STTM and Cre-loxP recombination system. This created STTM-30 floxed mice can be crossed with drug induced Cre mice or constitutively active Cre mice which express Cre recombinase driven by insulin gene promoter, to trigger the stop cassette deletion between two loxP site to active downstream STTM-30, thus knock out miR-30 only in pancreatic β-cell. There are variable β-cell specific Cre mouse models, including (Ins2-cre)25Mgn, (Ins2-cre)23Herrand (Ins2-cre/ERT)1Dam [195] which all are driven by Ins2 promoter. Ins2 is generally known as rat-insulin promoter (RIP) and firstly used in creating mouse model in 1999 [196]. In year 2000, RIP has been reported having non-specific expression in mouse brain which interfered the later experiments [197] [198]. Methods like replace it with longer DNA sequence of this promoter had been tried to eliminate the expression of Ins2 in brain, but failed. In this case, we chose to use Ins1 promoter, or called mouse insulin promoter (MIP) drived β-cell specific Cre mouse model since its expression is less than Ins2 [198] although it requires tamoxifen induced activation [199]. Another study recently showed that this mouse model induces high expression of human growth hormone as a result of its own transgene [200] even under no tamoxifen induced circumstances. In this case, proper control group is required to distinguish the “side effect” brought by the transgene when using this model. Here, we were using (Ins1-cre) tm1.1 Thor and (Ins1-cre/ERT2) tm2.1 Thorto constitutive or inducible knockout miR-30 expression in pancreatic β-cell.
The mouse strain that was using was C57BL/6J background, known for developing diet-induced obesity and diabetes [201]. To study the glucose metabolism related physiological changes, routine body weight, glucose level, glucose tolerance and insulin sensitivity were monitored on miR-30 KO mice. It is well known that gender has some major influence in body metabolism that male and female had different physical responses to diet, environment etc [202] [203] [204]. Our data implicated female had different responses to miR-30 knockout on body weight as male though not significantly. However, the glucose level for both gender didn’t show any difference compared to control mice under normal diet condition. Tolerance tests were performed on male mice, and miR-30 knockout didn’t affect glucose clearance or insulin sensitivity under this normal circumstance as well. Though no phenotypes under normal food condition had been found, it was consistent with the findings on miR-30d overexpression mice that no random glucose level, glucose tolerance and β-cell mass had been changed in young age under physiological condition. It is possible when challenged with stress condition, such as high-fat diet or streptozotocin treatment, miR-30 KO mice will exhibit some improved glucose hemostasis or defects, and that’s also the future plan of this study.

REFERENCES

1. International, H.I.V.C.S., et al., The major genetic determinants of HIV-1 control affect HLA class I peptide presentation. Science, 2010. 330(6010): p. 1551-7.
2. Shaer, A., et al., Differentiation of Human-Induced Pluripotent Stem Cells Into Insulin-Producing Clusters by MicroRNA-7. Exp Clin Transplant, 2016. 14(5): p. 555-563.
3. Dobson, Nature of the urine in diabetes. Medical Observations and Inquiries, 1776. 5: p. 298-310.
4. Polonsky, K.S., The past 200 years in diabetes. N Engl J Med, 2012. 367(14): p. 1332-40.
5. Atlas, I.D., IDF Diabetes Atlas. 6th ed. 2013.
6. Robin, E.D., Claude Bernard. Pioneer of regulatory biology. JAMA, 1979. 242(12): p. 1283-4.
7. Banting, F.G., et al., Pancreatic Extracts in the Treatment of Diabetes Mellitus. Can Med Assoc J, 1922. 12(3): p. 141-6.
8. Banting, F.G., W.R. Campbell, and A.A. Fletcher, Further Clinical Experience with Insulin (Pancreatic Extracts) in the Treatment of Diabetes Mellitus. Br Med J, 1923. 1(3236): p. 8-12.
9. Saltiel, A.R. and C.R. Kahn, Insulin signalling and the regulation of glucose and lipid metabolism. Nature, 2001. 414(6865): p. 799-806.
10. Tiano, J.P. and F. Mauvais-Jarvis, Molecular mechanisms of estrogen receptors’ suppression of lipogenesis in pancreatic beta-cells. Endocrinology, 2012. 153(7): p. 2997-3005.
11. Araki, E., et al., Alternative pathway of insulin signalling in mice with targeted disruption of the IRS-1 gene. Nature, 1994. 372(6502): p. 186-90.
12. Himsworth, H., Diabetes mellitus: its differentiation into insulin-sensitive and insulin-insensitive types. Lancet, 1936. 227: p. 127-130.
13. Moller, D.E., New drug targets for type 2 diabetes and the metabolic syndrome. Nature, 2001. 414(6865): p. 821-7.
14. Doria, A., M.E. Patti, and C.R. Kahn, The emerging genetic architecture of type 2 diabetes. Cell Metab, 2008. 8(3): p. 186-200.
15. Schwartz, M.W., et al., Cooperation between brain and islet in glucose homeostasis and diabetes. Nature, 2013. 503(7474): p. 59-66.
16. Hu, F.B., et al., Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med, 2001. 345(11): p. 790-7.
17. Perry, R.J., et al., The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature, 2014. 510(7503): p. 84-91.
18. Del Prato, S., Role of glucotoxicity and lipotoxicity in the pathophysiology of Type 2 diabetes mellitus and emerging treatment strategies. Diabet Med, 2009. 26(12): p. 1185-92.
19. Gong, Z. and R.H. Muzumdar, Pancreatic function, type 2 diabetes, and metabolism in aging. Int J Endocrinol, 2012. 2012: p. 320482.
20. Deeb, S.S., et al., A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet, 1998. 20(3): p. 284-7.
21. Yang, J., et al., The association of Pro12Ala polymorphism in the peroxisome proliferator-activated receptor-gamma2 gene with the metabolic characteristics in Chinese women with polycystic ovary syndrome. Int J Clin Exp Pathol, 2013. 6(9): p. 1894-902.
22. Wolf, G., Role of fatty acids in the development of insulin resistance and type 2 diabetes mellitus. Nutr Rev, 2008. 66(10): p. 597-600.
23. Steiner, D.J., et al., Pancreatic islet plasticity: interspecies comparison of islet architecture and composition. Islets, 2010. 2(3): p. 135-45.
24. Bonner-Weir, S., et al., The pancreatic ductal epithelium serves as a potential pool of progenitor cells. Pediatr Diabetes, 2004. 5 Suppl 2: p. 16-22.
25. TATTIKOTA, S.G., Dissecting the microRNA pathway in the pancreatic β-cell during insulin resistance. 2014.
26. Weir, G.C. and S. Bonner-Weir, Islet beta cell mass in diabetes and how it relates to function, birth, and death. Ann N Y Acad Sci, 2013. 1281: p. 92-105.
27. Edlund, H., Pancreatic organogenesis–developmental mechanisms and implications for therapy. Nat Rev Genet, 2002. 3(7): p. 524-32.
28. Ziv, O., B. Glaser, and Y. Dor, The plastic pancreas Dev Cell, 2013. 26(1): p. 3-7.
29. Dor, Y., et al., Adult pancreatic beta-cells are formed by self-duplication rather than stem-cell differentiation. Nature, 2004. 429(6987): p. 41-6.
30. Xu, X., et al., Beta cells can be generated from endogenous progenitors in injured adult mouse pancreas. Cell, 2008. 132(2): p. 197-207.
31. Collombat, P., et al., The ectopic expression of Pax4 in the mouse pancreas converts progenitor cells into alpha and subsequently beta cells. Cell, 2009. 138(3): p. 449-62.
32. Al-Hasani, R., J.G. McCall, and M.R. Bruchas, Exposure to chronic mild stress prevents kappa opioid-mediated reinstatement of cocaine and nicotine place preference. Front Pharmacol, 2013. 4: p. 96.
33. Thorel, F., et al., Conversion of adult pancreatic alpha-cells to beta-cells after extreme beta-cell loss. Nature, 2010. 464(7292): p. 1149-54.
34. Chera, S., et al., Diabetes recovery by age-dependent conversion of pancreatic delta-cells into insulin producers. Nature, 2014. 514(7523): p. 503-7.
35. Spijker, H.S., et al., Conversion of mature human beta-cells into glucagon-producing alpha-cells. Diabetes, 2013. 62(7): p. 2471-80.
36. Weir, G.C., C. Aguayo-Mazzucato, and S. Bonner-Weir, beta-cell dedifferentiation in diabetes is important, but what is it? Islets, 2013. 5(5): p. 233-7.
37. Bell, G.I., et al., Molecular biology of mammalian glucose transporters. Diabetes Care, 1990. 13(3): p. 198-208.
38. Dong, H., et al., Immuno-isolation of pancreatic islet allografts using pegylated nanotherapy leads to long-term normoglycemia in full MHC mismatch recipient mice. PLoS One, 2012. 7(12): p. e50265.
39. Pipatpiboon, N., et al., DPP4-inhibitor improves neuronal insulin receptor function, brain mitochondrial function and cognitive function in rats with insulin resistance induced by high-fat diet consumption. Eur J Neurosci, 2013. 37(5): p. 839-49.
40. Koster, J.C., et al., Targeted overactivity of beta cell K(ATP) channels induces profound neonatal diabetes. Cell, 2000. 100(6): p. 645-54.
41. Gloyn, A.L., et al., Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N Engl J Med, 2004. 350(18): p. 1838-49.
42. Ashcroft, F.M., D.E. Harrison, and S.J. Ashcroft, Glucose induces closure of single potassium channels in isolated rat pancreatic beta-cells. Nature, 1984. 312(5993): p. 446-8.
43. Matschinsky, F.M., B. Glaser, and M.A. Magnuson, Pancreatic beta-cell glucokinase: closing the gap between theoretical concepts and experimental realities. Diabetes, 1998. 47(3): p. 307-15.
44. Mao, C.S., et al., Glucose entrainment of high-frequency plasma insulin oscillations in control and type 2 diabetic subjects. Diabetes, 1999. 48(4): p. 714-21.
45. Polonsky, K.S., et al., Abnormal patterns of insulin secretion in non-insulin-dependent diabetes mellitus. N Engl J Med, 1988. 318(19): p. 1231-9.
46. Steil, G.M., et al., Adaptation of beta-cell mass to substrate oversupply: enhanced function with normal gene expression. Am J Physiol Endocrinol Metab, 2001. 280(5): p. E788-96.
47. Weir, G.C. and S. Bonner-Weir, Five stages of evolving beta-cell dysfunction during progression to diabetes. Diabetes, 2004. 53 Suppl 3: p. S16-21.
48. Butler, A.E., et al., Beta-cell deficit and increased beta-cell apoptosis in humans with type 2 diabetes. Diabetes, 2003. 52(1): p. 102-10.
49. Rhodes, C.J., Type 2 diabetes-a matter of beta-cell life and death? Science, 2005. 307(5708): p. 380-4.
50. Teta, M., et al., Very slow turnover of beta-cells in aged adult mice. Diabetes, 2005. 54(9): p. 2557-67.
51. Kushner, J.A., The role of aging upon beta cell turnover. J Clin Invest, 2013. 123(3): p. 990-5.
52. Stolovich-Rain, M., et al., Pancreatic beta cells in very old mice retain capacity for compensatory proliferation. J Biol Chem, 2012. 287(33): p. 27407-14.
53. Heit, J.J., S.K. Karnik, and S.K. Kim, Intrinsic regulators of pancreatic beta-cell proliferation. Annu Rev Cell Dev Biol, 2006. 22: p. 311-38.
54. Bonner-Weir, S., et al., Compensatory growth of pancreatic beta-cells in adult rats after short-term glucose infusion. Diabetes, 1989. 38(1): p. 49-53.
55. Porat, S., et al., Control of pancreatic beta cell regeneration by glucose metabolism. Cell Metab, 2011. 13(4): p. 440-9.
56. Withers, D.J., et al., Disruption of IRS-2 causes type 2 diabetes in mice. Nature, 1998. 391(6670): p. 900-4.
57. Bernal-Mizrachi, E., et al., Islet beta cell expression of constitutively active Akt1/PKB alpha induces striking hypertrophy, hyperplasia, and hyperinsulinemia. J Clin Invest, 2001. 108(11): p. 1631-8.
58. Paris, M., et al., Specific and combined effects of insulin and glucose on functional pancreatic beta-cell mass in vivo in adult rats. Endocrinology, 2003. 144(6): p. 2717-27.
59. Lu, Y., et al., Amplification and overexpression of Hsa-miR-30b, Hsa-miR-30d and KHDRBS3 at 8q24.22-q24.23 in medulloblastoma. PLoS One, 2009. 4(7): p. e6159.
60. Wightman, B., I. Ha, and G. Ruvkun, Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 1993. 75(5): p. 855-62.
61. Bartel, D.P., MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004. 116(2): p. 281-97.
62. Kozomara, A. and S. Griffiths-Jones, miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res, 2014. 42(Database issue): p. D68-73.
63. Lewis, B.P., et al., Prediction of mammalian microRNA targets. Cell, 2003. 115(7): p. 787-98.
64. Lee, Y., et al., The nuclear RNase III Drosha initiates microRNA processing. Nature, 2003. 425(6956): p. 415-9.
65. Lund, E., et al., Nuclear export of microRNA precursors. Science, 2004. 303(5654): p. 95-8.
66. Bernstein, E., et al., Dicer is essential for mouse development. Nat Genet, 2003. 35(3): p. 215-7.
67. Schwarz, D.S., et al., Asymmetry in the assembly of the RNAi enzyme complex. Cell, 2003. 115(2): p. 199-208.
68. Wang, D., et al., Quantitative functions of Argonaute proteins in mammalian development. Genes Dev, 2012. 26(7): p. 693-704.
69. Fabian, M.R. and N. Sonenberg, The mechanics of miRNA-mediated gene silencing: a look under the hood of miRISC. Nat Struct Mol Biol, 2012. 19(6): p. 586-93.
70. Eulalio, A., et al., Deadenylation is a widespread effect of miRNA regulation. Rna-a Publication of the Rna Society, 2009. 15(1): p. 21-32.
71. Meijer, H.A., et al., Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Mediated Gene Regulation. Science, 2013. 340(6128): p. 82-85.
72. Winter, J., et al., Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol, 2009. 11(3): p. 228-34.
73. Jordan, S.D., et al., Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nat Cell Biol, 2011. 13(4): p. 434-46.
74. Kornfeld, J.W., et al., Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature, 2013. 494(7435): p. 111-5.
75. Trajkovski, M., et al., MicroRNAs 103 and 107 regulate insulin sensitivity. Nature, 2011. 474(7353): p. 649-53.
76. Trajkovski, M., et al., MyomiR-133 regulates brown fat differentiation through Prdm16. Nat Cell Biol, 2012. 14(12): p. 1330-5.
77. Viswanathan, S.R., G.Q. Daley, and R.I. Gregory, Selective blockade of microRNA processing by Lin28. Science, 2008. 320(5872): p. 97-100.
78. Viswanathan, S.R. and G.Q. Daley, Lin28: A microRNA regulator with a macro role. Cell, 2010. 140(4): p. 445-9.
79. Frost, R.J. and E.N. Olson, Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs. Proc Natl Acad Sci U S A, 2011. 108(52): p. 21075-80.
80. Zhu, H., et al., The Lin28/let-7 axis regulates glucose metabolism. Cell, 2011. 147(1): p. 81-94.
81. Poy, M.N., et al., A pancreatic islet-specific microRNA regulates insulin secretion. Nature, 2004. 432(7014): p. 226-30.
82. Kloosterman, W.P., et al., Targeted inhibition of miRNA maturation with morpholinos reveals a role for miR-375 in pancreatic islet development. PLoS Biol, 2007. 5(8): p. e203.
83. Wang, Y., et al., MicroRNA-7 regulates the mTOR pathway and proliferation in adult pancreatic beta-cells. Diabetes, 2013. 62(3): p. 887-95.
84. Kredo-Russo, S., et al., Regulation of pancreatic microRNA-7 expression. Exp Diabetes Res, 2012. 2012: p. 695214.
85. Latreille, M., et al., MicroRNA-7a regulates pancreatic beta cell function. J Clin Invest, 2014. 124(6): p. 2722-35.
86. Plaisance, V., et al., MicroRNA-9 controls the expression of Granuphilin/Slp4 and the secretory response of insulin-producing cells. J Biol Chem, 2006. 281(37): p. 26932-42.
87. Soni, M.S., et al., Downregulation of carnitine acyl-carnitine translocase by miRNAs 132 and 212 amplifies glucose-stimulated insulin secretion. Diabetes, 2014. 63(11): p. 3805-14.
88. Wijesekara, N., et al., miR-33a modulates ABCA1 expression, cholesterol accumulation, and insulin secretion in pancreatic islets. Diabetes, 2012. 61(3): p. 653-8.
89. Kim, J.W., et al., miRNA-30a-5p-mediated silencing of Beta2/NeuroD expression is an important initial event of glucotoxicity-induced beta cell dysfunction in rodent models. Diabetologia, 2013. 56(4): p. 847-55.
90. Zhao, X., et al., MicroRNA-30d induces insulin transcription factor MafA and insulin production by targeting mitogen-activated protein 4 kinase 4 (MAP4K4) in pancreatic beta-cells. J Biol Chem, 2012. 287(37): p. 31155-64.
91. Bouzakri, K. and J.R. Zierath, MAP4K4 gene silencing in human skeletal muscle prevents tumor necrosis factor-alpha-induced insulin resistance. J Biol Chem, 2007. 282(11): p. 7783-9.
92. Andrali, S.S., et al., Glucose regulation of insulin gene expression in pancreatic beta-cells. Biochem J, 2008. 415(1): p. 1-10.
93. Zhang, C., et al., MafA is a key regulator of glucose-stimulated insulin secretion. Mol Cell Biol, 2005. 25(12): p. 4969-76.
94. Yang, X., et al., mir-30d Regulates multiple genes in the autophagy pathway and impairs autophagy process in human cancer cells. Biochem Biophys Res Commun, 2013. 431(3): p. 617-22.
95. Zhang, Y., et al., Regulation of autophagy by miR-30d impacts sensitivity of anaplastic thyroid carcinoma to cisplatin. Biochem Pharmacol, 2014. 87(4): p. 562-70.
96. Wu, C., et al., MiR-30d induces apoptosis and is regulated by the Akt/FOXO pathway in renal cell carcinoma. Cell Signal, 2013. 25(5): p. 1212-21.
97. Belgardt, B.F., et al., The microRNA-200 family regulates pancreatic beta cell survival in type 2 diabetes. Nat Med, 2015. 21(6): p. 619-27.
98. Song, B., et al., Chop deletion reduces oxidative stress, improves beta cell function, and promotes cell survival in multiple mouse models of diabetes. J Clin Invest, 2008. 118(10): p. 3378-89.
99. Barlow, A.D., M.L. Nicholson, and T.P. Herbert, Evidence for rapamycin toxicity in pancreatic beta-cells and a review of the underlying molecular mechanisms. Diabetes, 2013. 62(8): p. 2674-82.
100. Robertson, R.P., et al., Beta-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. Diabetes, 2004. 53 Suppl 1: p. S119-24.
101. Volchuk, A. and D. Ron, The endoplasmic reticulum stress response in the pancreatic beta-cell. Diabetes Obes Metab, 2010. 12 Suppl 2: p. 48-57.
102. Friedman, R.C., et al., Most mammalian mRNAs are conserved targets of microRNAs. Genome Res, 2009. 19(1): p. 92-105.
103. Kameswaran, V., et al., Epigenetic regulation of the DLK1-MEG3 microRNA cluster in human type 2 diabetic islets. Cell Metab, 2014. 19(1): p. 135-45.
104. Xu, G., et al., Thioredoxin-interacting protein regulates insulin transcription through microRNA-204. Nat Med, 2013. 19(9): p. 1141-6.
105. Vidigal, J.A. and A. Ventura, The biological functions of miRNAs: lessons from in vivo studies. Trends Cell Biol, 2015. 25(3): p. 137-47.
106. Brissova, M., et al., Intraislet endothelial cells contribute to revascularization of transplanted pancreatic islets. Diabetes, 2004. 53(5): p. 1318-25.
107. Salaverria, I., et al., Translocations activating IRF4 identify a subtype of germinal center-derived B-cell lymphoma affecting predominantly children and young adults. Blood, 2011. 118(1): p. 139-47.
108. Livak, K.J. and T.D. Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 2001. 25(4): p. 402-8.
109. Davidson, A.E., et al., Autosomal-Dominant Corneal Endothelial Dystrophies CHED1 and PPCD1 Are Allelic Disorders Caused by Non-coding Mutations in the Promoter of OVOL2. Am J Hum Genet, 2016. 98(1): p. 75-89.
110. Kos, C.H., Cre/loxP system for generating tissue-specific knockout mouse models. Nutr Rev, 2004. 62(6 Pt 1): p. 243-6.
111. Parsons, J.A., T.C. Brelje, and R.L. Sorenson, Adaptation of islets of Langerhans to pregnancy: increased islet cell proliferation and insulin secretion correlates with the onset of placental lactogen secretion. Endocrinology, 1992. 130(3): p. 1459-66.
112. Alnemri, E.S., et al., Human ICE/CED-3 protease nomenclature. Cell, 1996. 87(2): p. 171.
113. Salvesen, G.S., Caspases: opening the boxes and interpreting the arrows. Cell Death Differ, 2002. 9(1): p. 3-5.
114. Mitra, A., et al., Dual mTOR Inhibition Is Required to Prevent TGF-beta-Mediated Fibrosis: Implications for Scleroderma. J Invest Dermatol, 2015. 135(11): p. 2873-6.
115. Lipton, J.O. and M. Sahin, The neurology of mTOR. Neuron, 2014. 84(2): p. 275-91.
116. Chen, D., et al., MicroRNA-30d-5p inhibits tumour cell proliferation and motility by directly targeting CCNE2 in non-small cell lung cancer. Cancer Lett, 2015. 362(2): p. 208-17.
117. Deng, J., et al., The loss of miR-26a-mediated post-transcriptional regulation of cyclin E2 in pancreatic cancer cell proliferation and decreased patient survival. PLoS One, 2013. 8(10): p. e76450.
118. Liang, W., et al., Down-regulation of SOSTDC1 promotes thyroid cancer cell proliferation via regulating cyclin A2 and cyclin E2. Oncotarget, 2015. 6(31): p. 31780-91.
119. Dapas, B., et al., Role of E2F1-cyclin E1-cyclin E2 circuit in human coronary smooth muscle cell proliferation and therapeutic potential of its downregulation by siRNAs. Mol Med, 2009. 15(9-10): p. 297-306.
120. Bocharov, E.V., et al., Unique dimeric structure of BNip3 transmembrane domain suggests membrane permeabilization as a cell death trigger. J Biol Chem, 2007. 282(22): p. 16256-66.
121. Kahn, S.E., Clinical review 135: The importance of beta-cell failure in the development and progression of type 2 diabetes. J Clin Endocrinol Metab, 2001. 86(9): p. 4047-58.
122. van de Bunt, M., et al., The miRNA profile of human pancreatic islets and beta-cells and relationship to type 2 diabetes pathogenesis. PLoS One, 2013. 8(1): p. e55272.
123. Shantikumar, S., A. Caporali, and C. Emanueli, Role of microRNAs in diabetes and its cardiovascular complications. Cardiovasc Res, 2012. 93(4): p. 583-93.
124. Zampetaki, A., et al., Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res, 2010. 107(6): p. 810-7.
125. Scharfmann, R., et al., Development of a conditionally immortalized human pancreatic beta cell line. J Clin Invest, 2014. 124(5): p. 2087-98.
126. Burton, T.R. and S.B. Gibson, The role of Bcl-2 family member BNIP3 in cell death and disease: NIPping at the heels of cell death. Cell Death Differ, 2009. 16(4): p. 515-23.
127. Shaida, N., et al., Expression of BNIP3 correlates with hypoxia-inducible factor (HIF)-1alpha, HIF-2alpha and the androgen receptor in prostate cancer and is regulated directly by hypoxia but not androgens in cell lines. Prostate, 2008. 68(3): p. 336-43.
128. Burton, T.R., et al., The pro-cell death Bcl-2 family member, BNIP3, is localized to the nucleus of human glial cells: Implications for glioblastoma multiforme tumor cell survival under hypoxia. Int J Cancer, 2006. 118(7): p. 1660-9.
129. Tan, E.Y., et al., BNIP3 as a progression marker in primary human breast cancer; opposing functions in in situ versus invasive cancer. Clin Cancer Res, 2007. 13(2 Pt 1): p. 467-74.
130. Giatromanolaki, A., et al., BNIP3 expression is linked with hypoxia-regulated protein expression and with poor prognosis in non-small cell lung cancer. Clin Cancer Res, 2004. 10(16): p. 5566-71.
131. Pitts, K.R., et al., Differentially regulated functional gene clusters identified during ischemia and reperfusion in isolated cardiac myocytes using coverslip hypoxia. J Pharmacol Toxicol Methods, 2008. 57(1): p. 42-51.
132. Regula, K.M., K. Ens, and L.A. Kirshenbaum, Inducible expression of BNIP3 provokes mitochondrial defects and hypoxia-mediated cell death of ventricular myocytes. Circ Res, 2002. 91(3): p. 226-31.
133. Yang, X.J., et al., Mir-30d increases intracellular survival of Helicobacter pylori through inhibition of autophagy pathway. World J Gastroenterol, 2016. 22(15): p. 3978-91.
134. Lee, R.C., R.L. Feinbaum, and V. Ambros, The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993. 75(5): p. 843-54.
135. Reinhart, B.J., et al., The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 2000. 403(6772): p. 901-6.
136. Brennecke, J., et al., Principles of microRNA-target recognition. PLoS Biol, 2005. 3(3): p. e85.
137. Lytle, J.R., T.A. Yario, and J.A. Steitz, Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proc Natl Acad Sci U S A, 2007. 104(23): p. 9667-72.
138. Ding, J., et al., Genome-wide search for miRNA-target interactions in Arabidopsis thaliana with an integrated approach. BMC Genomics, 2012. 13 Suppl 3: p. S3.
139. Carthew, R.W. and E.J. Sontheimer, Origins and Mechanisms of miRNAs and siRNAs. Cell, 2009. 136(4): p. 642-55.
140. Kim, V.N., J. Han, and M.C. Siomi, Biogenesis of small RNAs in animals. Nat Rev Mol Cell Biol, 2009. 10(2): p. 126-39.
141. Ruby, J.G., et al., Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell, 2006. 127(6): p. 1193-207.
142. Kozomara, A. and S. Griffiths-Jones, miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res, 2011. 39(Database issue): p. D152-7.
143. Gardner, P.P., et al., Rfam: Wikipedia, clans and the “decimal” release. Nucleic Acids Res, 2011. 39(Database issue): p. D141-5.
144. VanGuilder, H.D., K.E. Vrana, and W.M. Freeman, Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques, 2008. 44(5): p. 619-26.
145. Towbin, H., T. Staehelin, and J. Gordon, Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc Natl Acad Sci U S A, 1979. 76(9): p. 4350-4.
146. Anon, Rapid amplification of 5′ complementary DNA ends (5′ RACE). Nat Methods, 2005. 2(8): p. 629-30.
147. Kuhn, D.E., et al., Experimental validation of miRNA targets. Methods, 2008. 44(1): p. 47-54.
148. Baek, D., et al., The impact of microRNAs on protein output. Nature, 2008. 455(7209): p. 64-71.
149. Hutvagner, G., et al., Sequence-specific inhibition of small RNA function. PLoS Biol, 2004. 2(4): p. E98.
150. Yan, J., et al., Effective small RNA destruction by the expression of a short tandem target mimic in Arabidopsis. Plant Cell, 2012. 24(2): p. 415-27.
151. Kim, Y.K., et al., TALEN-based knockout library for human microRNAs. Nat Struct Mol Biol, 2013. 20(12): p. 1458-64.
152. Kim, Y.G., J. Cha, and S. Chandrasegaran, Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc Natl Acad Sci U S A, 1996. 93(3): p. 1156-60.
153. Barrangou, R., et al., CRISPR provides acquired resistance against viruses in prokaryotes. Science, 2007. 315(5819): p. 1709-12.
154. Barrangou, R. and L.A. Marraffini, CRISPR-Cas systems: Prokaryotes upgrade to adaptive immunity. Mol Cell, 2014. 54(2): p. 234-44.
155. Bondy-Denomy, J. and A.R. Davidson, To acquire or resist: the complex biological effects of CRISPR-Cas systems. Trends Microbiol, 2014. 22(4): p. 218-25.
156. Gasiunas, G., T. Sinkunas, and V. Siksnys, Molecular mechanisms of CRISPR-mediated microbial immunity. Cell Mol Life Sci, 2014. 71(3): p. 449-65.
157. Charpentier, E. and L.A. Marraffini, Harnessing CRISPR-Cas9 immunity for genetic engineering. Curr Opin Microbiol, 2014. 19: p. 114-9.
158. Boettcher, M. and M.T. McManus, Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR. Mol Cell, 2015. 58(4): p. 575-85.
159. Chang, H., et al., CRISPR/cas9, a novel genomic tool to knock down microRNA in vitro and in vivo. Sci Rep, 2016. 6: p. 22312.
160. Zhao, Y., et al., Sequence-specific inhibition of microRNA via CRISPR/CRISPRi system. Sci Rep, 2014. 4: p. 3943.
161. Ho, T.T., et al., Targeting non-coding RNAs with the CRISPR/Cas9 system in human cell lines. Nucleic Acids Res, 2015. 43(3): p. e17.
162. Jiang, Q., et al., Small indels induced by CRISPR/Cas9 in the 5′ region of microRNA lead to its depletion and Drosha processing retardance. RNA Biol, 2014. 11(10): p. 1243-9.
163. Cardozo, A.K., et al., IL-1beta and IFN-gamma induce the expression of diverse chemokines and IL-15 in human and rat pancreatic islet cells, and in islets from pre-diabetic NOD mice. Diabetologia, 2003. 46(2): p. 255-66.
164. Nicoletti, F., et al., Serum concentrations of the interferon-gamma-inducible chemokine IP-10/CXCL10 are augmented in both newly diagnosed Type I diabetes mellitus patients and subjects at risk of developing the disease. Diabetologia, 2002. 45(8): p. 1107-10.
165. Xu, H., et al., [Elevated plasma concentration of IP-10 in patients with type 2 diabetes mellitus]. Nihon Jinzo Gakkai Shi, 2005. 47(5): p. 524-30.
166. Paroni, F., E. Domsgen, and K. Maedler, CXCL10- a path to beta-cell death. Islets, 2009. 1(3): p. 256-9.
167. Schulthess, F.T., et al., CXCL10 impairs beta cell function and viability in diabetes through TLR4 signaling. Cell Metab, 2009. 9(2): p. 125-39.
168. Shigihara, T., et al., CXCL10 DNA vaccination prevents spontaneous diabetes through enhanced beta cell proliferation in NOD mice. J Immunol, 2005. 175(12): p. 8401-8.
169. Furukawa, Y., et al., Polymorphisms in the IDE-KIF11-HHEX gene locus are reproducibly associated with type 2 diabetes in a Japanese population. J Clin Endocrinol Metab, 2008. 93(1): p. 310-4.
170. Hu, C., et al., PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PLoS One, 2009. 4(10): p. e7643.
171. Qian, Y., et al., Genetic variants of IDE-KIF11-HHEX at 10q23.33 associated with type 2 diabetes risk: a fine-mapping study in Chinese population. PLoS One, 2012. 7(4): p. e35060.
172. Cavallari, J.F., et al., Muramyl Dipeptide-Based Postbiotics Mitigate Obesity-Induced Insulin Resistance via IRF4. Cell Metab, 2017. 25(5): p. 1063-1074 e3.
173. Bhat, U.G. and K. Watanabe, Serpine1 Mediates Porphyromonas gingivalis Induced Insulin Secretion in the Pancreatic Beta Cell Line MIN6. J Oral Biol (Northborough), 2015. 2(2).
174. Pavon, M.A., et al., Enhanced cell migration and apoptosis resistance may underlie the association between high SERPINE1 expression and poor outcome in head and neck carcinoma patients. Oncotarget, 2015. 6(30): p. 29016-33.
175. Benavente, C.A., et al., Chromatin remodelers HELLS and UHRF1 mediate the epigenetic deregulation of genes that drive retinoblastoma tumor progression. Oncotarget, 2014. 5(20): p. 9594-608.
176. Chen, D., et al., TOP2A, HELLS, ATAD2, and TET3 Are Novel Prognostic Markers in Renal Cell Carcinoma. Urology, 2017. 102: p. 265 e1-265 e7.
177. Ye, L., et al., Upregulation of E2F8 promotes cell proliferation and tumorigenicity in breast cancer by modulating G1/S phase transition. Oncotarget, 2016. 7(17): p. 23757-71.
178. Park, S.A., et al., E2F8 as a Novel Therapeutic Target for Lung Cancer. J Natl Cancer Inst, 2015. 107(9).
179. Sun, J., et al., E2F8, a direct target of miR-144, promotes papillary thyroid cancer progression via regulating cell cycle. J Exp Clin Cancer Res, 2017. 36(1): p. 40.
180. Zhang, P., et al., EZH2-miR-30d-KPNB1 pathway regulates malignant peripheral nerve sheath tumour cell survival and tumourigenesis. J Pathol, 2014. 232(3): p. 308-18.
181. Kobayashi, N., et al., Identification of miR-30d as a novel prognostic maker of prostate cancer. Oncotarget, 2012. 3(11): p. 1455-71.
182. Esposito, F., et al., Down-regulation of the miR-25 and miR-30d contributes to the development of anaplastic thyroid carcinoma targeting the polycomb protein EZH2. J Clin Endocrinol Metab, 2012. 97(5): p. E710-8.
183. Bushati, N. and S.M. Cohen, microRNA functions. Annu Rev Cell Dev Biol, 2007. 23: p. 175-205.
184. Garcia, D., A miRacle in plant development: role of microRNAs in cell differentiation and patterning. Semin Cell Dev Biol, 2008. 19(6): p. 586-95.
185. Reichel, M., J. Li, and A.A. Millar, Silencing the silencer: strategies to inhibit microRNA activity. Biotechnol Lett, 2011. 33(7): p. 1285-92.
186. Allen, R.S., et al., MicroR159 regulation of most conserved targets in Arabidopsis has negligible phenotypic effects. Silence, 2010. 1(1): p. 18.
187. Sieber, P., et al., Redundancy and specialization among plant microRNAs: role of the MIR164 family in developmental robustness. Development, 2007. 134(6): p. 1051-60.
188. Orom, U.A., S. Kauppinen, and A.H. Lund, LNA-modified oligonucleotides mediate specific inhibition of microRNA function. Gene, 2006. 372: p. 137-41.
189. Davis, S., et al., Improved targeting of miRNA with antisense oligonucleotides. Nucleic Acids Res, 2006. 34(8): p. 2294-304.
190. Vaistij, F.E., et al., Suppression of microRNA accumulation via RNA interference in Arabidopsis thaliana. Plant Mol Biol, 2010. 73(4-5): p. 391-7.
191. Todesco, M., et al., A collection of target mimics for comprehensive analysis of microRNA function in Arabidopsis thaliana. PLoS Genet, 2010. 6(7): p. e1001031.
192. Tang, G., et al., Construction of short tandem target mimic (STTM) to block the functions of plant and animal microRNAs. Methods, 2012. 58(2): p. 118-25.
193. Feil, S., N. Valtcheva, and R. Feil, Inducible Cre mice. Methods Mol Biol, 2009. 530: p. 343-63.
194. El Ouaamari, A., et al., miR-375 targets 3′-phosphoinositide-dependent protein kinase-1 and regulates glucose-induced biological responses in pancreatic beta-cells. Diabetes, 2008. 57(10): p. 2708-17.
195. Magnuson, M.A. and A.B. Osipovich, Pancreas-specific Cre driver lines and considerations for their prudent use. Cell Metab, 2013. 18(1): p. 9-20.
196. Ray, M.K., et al., Development of a transgenic mouse model using rat insulin promoter to drive the expression of CRE recombinase in a tissue-specific manner. Int J Pancreatol, 1999. 25(3): p. 157-63.
197. Song, J., et al., Brain expression of Cre recombinase driven by pancreas-specific promoters. Genesis, 2010. 48(11): p. 628-34.
198. Wicksteed, B., et al., Conditional gene targeting in mouse pancreatic ss-Cells: analysis of ectopic Cre transgene expression in the brain. Diabetes, 2010. 59(12): p. 3090-8.
199. Feil, R., et al., Ligand-activated site-specific recombination in mice. Proc Natl Acad Sci U S A, 1996. 93(20): p. 10887-90.
200. Oropeza, D., et al., Phenotypic Characterization of MIP-CreERT1Lphi Mice With Transgene-Driven Islet Expression of Human Growth Hormone. Diabetes, 2015. 64(11): p. 3798-807.
201. Surwit, R.S., et al., Diet-induced type II diabetes in C57BL/6J mice. Diabetes, 1988. 37(9): p. 1163-7.
202. Galipeau, D., S. Verma, and J.H. McNeill, Female rats are protected against fructose-induced changes in metabolism and blood pressure. Am J Physiol Heart Circ Physiol, 2002. 283(6): p. H2478-84.
203. Hevener, A., et al., Female rats do not exhibit free fatty acid-induced insulin resistance. Diabetes, 2002. 51(6): p. 1907-12.
204. Galipeau, D.M., L. Yao, and J.H. McNeill, Relationship among hyperinsulinemia, insulin resistance, and hypertension is dependent on sex. Am J Physiol Heart Circ Physiol, 2002. 283(2): p. H562-7.
205. Qi, L., et al., Heterogeneity of neuroblastoma cell lines in insulin-like growth factor 1 receptor/Akt pathway-mediated cell proliferative responses. Cancer Sci, 2013. 104(9): p. 1162-71.

Appendix

A. List of all 1422 differently expressed genes in miR-30d KO cell line from HiSeq analysis

Gene Description Fold Change
Sst somatostatin 68.76011249
Serpinb1a serine (or cysteine) peptidase inhibitor, clade B, member 1a 12.24745427
Dppa4 developmental pluripotency associated 4 10.61961825
Serpinb1c serine (or cysteine) peptidase inhibitor, clade B, member 1c 10.43971194
Gm14139 predicted gene 14139 8.841554237
Fam163a family with sequence similarity 163, member A 8.769958404
Gip gastric inhibitory polypeptide 7.439058279
5830473C10Rik RIKEN cDNA 5830473C10 gene 7.203260105
S100a6 S100 calcium binding protein A6 (calcyclin) 6.959820026
Tspan8 tetraspanin 8 6.037159846
Vdr vitamin D receptor 5.570705312
Gimap6 GTPase, IMAP family member 6 5.275215386
Mybpc1 myosin binding protein C, slow-type 5.163788817
Rxfp2 relaxin/insulin-like family peptide receptor 2 5.022236613
Pla2g5 phospholipase A2, group V 4.912985564
Tfpi tissue factor pathway inhibitor 4.911827857
Ppy pancreatic polypeptide 4.880875661
Slfn3 schlafen 3 4.811644315
Sparcl1 SPARC-like 1 4.767986001
Hfm1 HFM1, ATP-dependent DNA helicase homolog (S. cerevisiae) 4.712785437
Mesp1 mesoderm posterior 1 4.698303789
Abca13 ATP-binding cassette, sub-family A (ABC1), member 13 4.66944149
Igfbp5 insulin-like growth factor binding protein 5 4.668794213
Ulbp1 UL16 binding protein 1 4.647452114
Rhox2h reproductive homeobox 2H 4.619414484
Ggt6 gamma-glutamyltransferase 6 4.571539229
Egr2 early growth response 2 4.570620384
Clrn3 clarin 3 4.48149473
Adrb3 adrenergic receptor, beta 3 4.446129594
Cd19 CD19 antigen 4.396300494
Ifih1 interferon induced with helicase C domain 1 4.35650138
Plau plasminogen activator, urokinase 4.268265739
Sult1d1 sulfotransferase family 1D, member 1 4.213911456
Cyp26b1 cytochrome P450, family 26, subfamily b, polypeptide 1 4.162845456
Enpp3 ectonucleotide pyrophosphatase/phosphodiesterase 3 4.093514484
Hdgfrp3 hepatoma-derived growth factor, related protein 3 4.009243384
Ppp1r27 protein phosphatase 1, regulatory subunit 27 3.960273315
Rbp2 retinol binding protein 2, cellular 3.940585489
Slc6a19 solute carrier family 6 (neurotransmitter transporter), member 19 3.835620538
Rhox2g reproductive homeobox 2G 3.810393519
Aldh1a3 aldehyde dehydrogenase family 1, subfamily A3 3.799501112
Olfr1372-ps1 olfactory receptor 1372, pseudogene 1 3.773857636
Arhgap36 Rho GTPase activating protein 36 3.740108181
Mug1 murinoglobulin 1 3.725773573
Tns1 tensin 1 3.715406336
AI467606 expressed sequence AI467606 3.653122165
Spn sialophorin 3.592180887
A730085A09Rik KN motif and ankyrin repeat domains 4, opposite strand 3.591757627
Gpr133 adhesion G protein-coupled receptor D1 3.590114856
Fev FEV (ETS oncogene family) 3.57819001
Acot5 acyl-CoA thioesterase 5 3.540173336
Fbxw15 F-box and WD-40 domain protein 15 3.501419696
Coch coagulation factor C homolog (Limulus polyphemus) 3.500157884
Trim12c tripartite motif-containing 12C 3.464098613
Sectm1a secreted and transmembrane 1A 3.459419572
Slfn4 schlafen 4 3.455321614
C530028O21Rik PILR alpha associated neural protein 3.438787578
4930502E18Rik RIKEN cDNA 4930502E18 gene 3.376199136
Stat4 signal transducer and activator of transcription 4 3.367971663
Cat catalase 3.356179784
Ppp1r1b protein phosphatase 1, regulatory (inhibitor) subunit 1B 3.346748175
Chrna3 cholinergic receptor, nicotinic, alpha polypeptide 3 3.343988764
Zfp811 zinc finger protein 811 3.311970907
Nr1h3 nuclear receptor subfamily 1, group H, member 3 3.277350564
Usp25 ubiquitin specific peptidase 25 3.266940093
9130024F11Rik RIKEN cDNA 9130024F11 gene 3.262934439
Plk2 polo-like kinase 2 3.249077147
Tle6 transducin-like enhancer of split 6, homolog of Drosophila E(spl) 3.193326349
2010005H15Rik RIKEN cDNA 2010005H15 gene 3.184197893
Gm11549 predicted gene 11549 3.174259334
4930555B11Rik RIKEN cDNA 4930555B11 gene 3.144781975
Fndc5 fibronectin type III domain containing 5 3.107533621
Msn moesin 3.10464863
Mfap5 microfibrillar associated protein 5 3.101099895
Gm1965 predicted gene 1965 3.098585978
6430548G04 dopamine beta hydroxylase, opposite strand 3.074006041
Dock8 dedicator of cytokinesis 8 3.072621372
Spink4 serine peptidase inhibitor, Kazal type 4 3.049494259
Prlhr prolactin releasing hormone receptor 3.026939836
Dlk1 delta-like 1 homolog (Drosophila) 3.022935088
Cd209c CD209c antigen 3.017220221
Il4 interleukin 4 3.016613783
St6galnac2 ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N-acetylgalactosaminide alpha-2,6-sialyltransferase 2 3.001887685
Serinc2 serine incorporator 2 2.964070988
Fetub fetuin beta 2.957175775
Psmb8 proteasome (prosome, macropain) subunit, beta type 8 (large multifunctional peptidase 7) 2.907582243
Nqo1 NAD(P)H dehydrogenase, quinone 1 2.899170137
Kcnq2 potassium voltage-gated channel, subfamily Q, member 2 2.890101178
1810011O10Rik RIKEN cDNA 1810011O10 gene 2.868646107
Psd4 pleckstrin and Sec7 domain containing 4 2.866559053
Neurod4 neurogenic differentiation 4 2.850075228
Aqp3 aquaporin 3 2.844115393
Npl N-acetylneuraminate pyruvate lyase 2.830761102
Csn3 casein kappa 2.807410441
Slfn8 schlafen 8 2.799404569
Sult1c2 sulfotransferase family, cytosolic, 1C, member 2 2.799171731
Cldn2 claudin 2 2.790879818
Syt16 synaptotagmin XVI 2.774369609
Fam151a family with sequence simliarity 151, member A 2.76266379
Syde2 synapse defective 1, Rho GTPase, homolog 2 (C. elegans) 2.759238186
Samd9l sterile alpha motif domain containing 9-like 2.758473269
Nlrp10 NLR family, pyrin domain containing 10 2.744798038
Fmnl2 formin-like 2 2.739817883
Rasl10b RAS-like, family 10, member B 2.734031756
Klra5 killer cell lectin-like receptor, subfamily A, member 5 2.731891147
Col28a1 collagen, type XXVIII, alpha 1 2.722986821
Fosb FBJ osteosarcoma oncogene B 2.722062137
Cox7b2 cytochrome c oxidase subunit VIIb2 2.718310014
Flrt3 fibronectin leucine rich transmembrane protein 3 2.716369992
Oasl2 2′-5′ oligoadenylate synthetase-like 2 2.713415535
Dsp desmoplakin 2.709036816
Ahsg alpha-2-HS-glycoprotein 2.699140357
Cd34 CD34 antigen 2.695401147
Slfn9 schlafen 9 2.694784675
Adamtsl2 ADAMTS-like 2 2.684605312
Mndal myeloid nuclear differentiation antigen like 2.68274513
Anpep alanyl (membrane) aminopeptidase 2.65663495
5730559C18Rik RIKEN cDNA 5730559C18 gene 2.654978172
Uba7 ubiquitin-like modifier activating enzyme 7 2.633606862
Clvs2 clavesin 2 2.615777809
Spsb1 splA/ryanodine receptor domain and SOCS box containing 1 2.608752359
Apol9a apolipoprotein L 9a 2.598970035
Pmp22 peripheral myelin protein 22 2.582162867
Cyp4x1 cytochrome P450, family 4, subfamily x, polypeptide 1 2.5664604
Cyp4v3 cytochrome P450, family 4, subfamily v, polypeptide 3 2.559372256
Anks4b ankyrin repeat and sterile alpha motif domain containing 4B 2.55839673
Zeb2 zinc finger E-box binding homeobox 2 2.534989901
Sh3bgrl2 SH3 domain binding glutamic acid-rich protein like 2 2.530881571
Ralyl RALY RNA binding protein-like 2.527410492
BC028528 cDNA sequence BC028528 2.5189282
Pdzd3 PDZ domain containing 3 2.517811016
Gm16576 predicted gene 16576 2.493254234
Elovl3 elongation of very long chain fatty acids (FEN1/Elo2, SUR4/Elo3, yeast)-like 3 2.49021447
Casp1 caspase 1 2.487471507
Tmprss2 transmembrane protease, serine 2 2.486506151
Gfra3 glial cell line derived neurotrophic factor family receptor alpha 3 2.484611007
BC021767 cingulin pseudogene 2.482682891
Fgfr1 fibroblast growth factor receptor 1 2.473356159
Mest mesoderm specific transcript 2.471967884
Vwf Von Willebrand factor homolog 2.463261854
Slc14a2 solute carrier family 14 (urea transporter), member 2 2.460821478
Nfatc1 nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 1 2.456135239
Fmnl1 formin-like 1 2.448044868
Gm10578 predicted gene 10578 2.438425677
Tdh L-threonine dehydrogenase 2.437749696
Cxcr4 chemokine (C-X-C motif) receptor 4 2.42749782
Vwa5a von Willebrand factor A domain containing 5A 2.417490131
Wscd1 WSC domain containing 1 2.416652438
Crhbp corticotropin releasing hormone binding protein 2.395737764
Mfi2 antigen p97 (melanoma associated) identified by monoclonal antibodies 133.2 and 96.5 2.389286735
4930519F09Rik pyruvate dehydrogenase E1 alpha 1 pseudogene 2.388955532
Car8 carbonic anhydrase 8 2.380822391
Slc30a3 solute carrier family 30 (zinc transporter), member 3 2.371072867
Gsto1 glutathione S-transferase omega 1 2.365491538
Gbp7 guanylate binding protein 7 2.343687146
Pou2f2 POU domain, class 2, transcription factor 2 2.337246666
Ceacam10 carcinoembryonic antigen-related cell adhesion molecule 10 2.334736928
Fam154b stablizer of axonemal microtubules 2 2.334526556
Enpp1 ectonucleotide pyrophosphatase/phosphodiesterase 1 2.329402587
Gm12522 predicted gene 12522 2.319831468
Ccnjl cyclin J-like 2.316569555
Slc2a2 solute carrier family 2 (facilitated glucose transporter), member 2 2.305117034
Fos FBJ osteosarcoma oncogene 2.30438217
Rab19 RAB19, member RAS oncogene family 2.304350224
Cib3 calcium and integrin binding family member 3 2.303791253
Tm6sf1 transmembrane 6 superfamily member 1 2.299005632
Cxcl10 chemokine (C-X-C motif) ligand 10 2.296441448
Frmpd4 FERM and PDZ domain containing 4 2.295661612
Ccno cyclin O 2.28613405
Csmd3 CUB and Sushi multiple domains 3 2.286070666
Satb2 special AT-rich sequence binding protein 2 2.272090027
Slc38a8 solute carrier family 38, member 8 2.268769432
Capg capping protein (actin filament), gelsolin-like 2.264213498
2310008N11Rik RIKEN cDNA 2310008N11 gene 2.26255051
Arl5c ADP-ribosylation factor-like 5C 2.257365452
Fndc1 fibronectin type III domain containing 1 2.256943026
Gpr124 adhesion G protein-coupled receptor A3 2.252098613
Irgm2 immunity-related GTPase family M member 2 2.246781766
Sgcd sarcoglycan, delta (dystrophin-associated glycoprotein) 2.234063201
Slc25a13 solute carrier family 25 (mitochondrial carrier, adenine nucleotide translocator), member 13 2.228001185
Ces1d carboxylesterase 1D 2.220800811
Rbp4 retinol binding protein 4, plasma 2.218446867
Cldn4 claudin 4 2.206423916
Cntnap5b contactin associated protein-like 5B 2.197952182
Pik3cg phosphoinositide-3-kinase, catalytic, gamma polypeptide 2.192185679
Prrt2 proline-rich transmembrane protein 2 2.191091906
Pde6b phosphodiesterase 6B, cGMP, rod receptor, beta polypeptide 2.180380617
Tgm2 transglutaminase 2, C polypeptide 2.174584846
2010001M09Rik marginal zone B and B1 cell-specific protein 1 2.174238193
Slc17a7 solute carrier family 17 (sodium-dependent inorganic phosphate cotransporter), member 7 2.172174492
Olfr1259 olfactory receptor 1259 2.170398567
Tnnt1 troponin T1, skeletal, slow 2.168278391
Zfp703 zinc finger protein 703 2.153450259
Ifi203 interferon activated gene 203 2.147443241
Sept1 septin 1 2.137078644
Ucp2 uncoupling protein 2 (mitochondrial, proton carrier) 2.133615188
Slc41a2 solute carrier family 41, member 2 2.131723024
Mr1 major histocompatibility complex, class I-related 2.1309105
Magea8 melanoma antigen, family A, 8 2.130615114
Zfp185 zinc finger protein 185 2.130186876
Plekhb1 pleckstrin homology domain containing, family B (evectins) member 1 2.130053992
Phex phosphate regulating endopeptidase homolog, X-linked 2.12989159
C130026I21Rik RIKEN cDNA C130026I21 gene 2.120096598
Kctd12b potassium channel tetramerisation domain containing 12b 2.115721892
Sult6b1 sulfotransferase family, cytosolic, 6B, member 1 2.106138045
Slc16a5 solute carrier family 16 (monocarboxylic acid transporters), member 5 2.100437707
Kctd16 potassium channel tetramerisation domain containing 16 2.100204774
Psmb9 proteasome (prosome, macropain) subunit, beta type 9 (large multifunctional peptidase 2) 2.091068032
Cav2 caveolin 2 2.090763676
F5 coagulation factor V 2.0892136
Nacc2 nucleus accumbens associated 2, BEN and BTB (POZ) domain containing 2.084180172
Arhgap29 Rho GTPase activating protein 29 2.066343355
Fbxw24 F-box and WD-40 domain protein 24 2.066028277
3930402G23Rik RIKEN cDNA 3930402G23 gene 2.064095898
Npy neuropeptide Y 2.061465047
Emilin1 elastin microfibril interfacer 1 2.061450758
Baiap3 BAI1-associated protein 3 2.053763417
Stard8 START domain containing 8 2.05353566
Tgfb1i1 transforming growth factor beta 1 induced transcript 1 2.053151377
Olfr1258 olfactory receptor 1258 2.049369323
Tmem117 transmembrane protein 117 2.047310611
Itpr1 inositol 1,4,5-trisphosphate receptor 1 2.046970059
Gpr119 G-protein coupled receptor 119 2.043567646
Epb4.1l4a erythrocyte protein band 4.1 like 4a 2.042548025
BC060267 EF-hand calcium binding domain 12 2.036977437
Hkdc1 hexokinase domain containing 1 2.036596253
Pram1 PML-RAR alpha-regulated adaptor molecule 1 2.035269724
Pde6a phosphodiesterase 6A, cGMP-specific, rod, alpha 2.034606783
AI662270 expressed sequence AI662270 2.030112954
Dact1 dapper homolog 1, antagonist of beta-catenin (xenopus) 2.029972242
Clic5 chloride intracellular channel 5 2.028987532
Npr1 natriuretic peptide receptor 1 2.026415476
Tfr2 transferrin receptor 2 2.025657131
Tlr3 toll-like receptor 3 2.024772755
Gcgr glucagon receptor 2.024337727
Fgd2 FYVE, RhoGEF and PH domain containing 2 2.023033205
Chrnb4 cholinergic receptor, nicotinic, beta polypeptide 4 2.017473887
Ankrd34c ankyrin repeat domain 34C 2.01599212
Rprml reprimo-like 2.015503097
Acot11 acyl-CoA thioesterase 11 2.015028159
Ccdc13 coiled-coil domain containing 13 2.006887854
Zfp345 zinc finger protein 345 2.00619244
Gm1141 predicted gene 1141 2.004705064
Slc25a33 solute carrier family 25, member 33 2.003857614
Gimap8 GTPase, IMAP family member 8 2.003829835
Cbln2 cerebellin 2 precursor protein 2.003246561
Adam5 a disintegrin and metallopeptidase domain 5 1.993728554
Tcea3 transcription elongation factor A (SII), 3 1.991322654
Fbxo48 F-box protein 48 1.987595251
Rasl11a RAS-like, family 11, member A 1.98546785
Cyp2j9 cytochrome P450, family 2, subfamily j, polypeptide 9 1.983730443
A530032D15Rik RIKEN cDNA A530032D15Rik gene 1.983295985
Scn9a sodium channel, voltage-gated, type IX, alpha 1.9781187
Txnip thioredoxin interacting protein 1.977130364
Tram2 translocating chain-associating membrane protein 2 1.977061843
2410004P03Rik RIKEN cDNA 2410004P03 gene 1.976743937
Casc5 cancer susceptibility candidate 5 1.974899174
Frmd4b FERM domain containing 4B 1.971392442
Slc39a8 solute carrier family 39 (metal ion transporter), member 8 1.969835287
Scnn1g sodium channel, nonvoltage-gated 1 gamma 1.963471966
Fmo5 flavin containing monooxygenase 5 1.959544044
Mmd2 monocyte to macrophage differentiation-associated 2 1.959265622
Srgap1 SLIT-ROBO Rho GTPase activating protein 1 1.957889033
Tmc3 transmembrane channel-like gene family 3 1.952243488
Als2cl ALS2 C-terminal like 1.951865984
Alox12 arachidonate 12-lipoxygenase 1.950994891
Mesp2 mesoderm posterior 2 1.948140867
Galnt14 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 14 1.947536005
C2 complement component 2 (within H-2S) 1.944864978
Zbtb32 zinc finger and BTB domain containing 32 1.93964012
Nhlh2 nescient helix loop helix 2 1.937647316
Irf7 interferon regulatory factor 7 1.935558618
Cpne7 copine VII 1.933165295
F730035M05Rik RIKEN cDNA F730035M05 gene 1.926688045
3830403N18Rik RIKEN cDNA 3830403N18 gene 1.924188317
Abtb2 ankyrin repeat and BTB (POZ) domain containing 2 1.922788393
Gpbar1 G protein-coupled bile acid receptor 1 1.921599925
Acvr1c activin A receptor, type IC 1.919628317
Egr1 early growth response 1 1.916659419
Nes nestin 1.912951106
Sstr5 somatostatin receptor 5 1.909906484
A330049N07Rik RIKEN cDNA A330049N07 gene 1.90492606
Rtp4 receptor transporter protein 4 1.900813516
Rapgef5 Rap guanine nucleotide exchange factor (GEF) 5 1.900397218
Lcor ligand dependent nuclear receptor corepressor 1.894615379
Il17rd interleukin 17 receptor D 1.892792156
Kcnh4 potassium voltage-gated channel, subfamily H (eag-related), member 4 1.888137722
Kcnk9 potassium channel, subfamily K, member 9 1.887178647
LOC100503280 predicted gene, 38425 1.885691932
0610009B14Rik pleckstrin homology domain containing, family D (with coiled-coil domains) member 1, opposite strand 1.882390572
2810442I21Rik Egfr long non-coding downstream RNA 1.88137182
Pydc3 pyrin domain containing 3 1.879481872
Cd74 CD74 antigen (invariant polypeptide of major histocompatibility complex, class II antigen-associated) 1.874055917
Gldc glycine decarboxylase 1.873768861
Zfp641 zinc finger protein 641 1.873122171
Prr15l proline rich 15-like 1.870669898
Fam83b family with sequence similarity 83, member B 1.869700255
Gen1 Gen homolog 1, endonuclease (Drosophila) 1.869267449
Fmo1 flavin containing monooxygenase 1 1.867091978
Wdr93 WD repeat domain 93 1.865780151
Npas4 neuronal PAS domain protein 4 1.864718686
Rgs8 regulator of G-protein signaling 8 1.858159056
Htr5b 5-hydroxytryptamine (serotonin) receptor 5B 1.855245361
Nefl neurofilament, light polypeptide 1.854153904
9130230L23Rik RIKEN cDNA 9130230L23 gene 1.848770315
Apol10b apolipoprotein L 10B 1.84636656
Ccdc15 coiled-coil domain containing 15 1.843646423
Jun jun proto-oncogene 1.843526302
Dll1 delta-like 1 (Drosophila) 1.842755929
Fmn1 formin 1 1.838769957
Plscr4 phospholipid scramblase 4 1.836148828
Dmbx1 diencephalon/mesencephalon homeobox 1 1.831979043
Trim36 tripartite motif-containing 36 1.829599656
6330403K07Rik RIKEN cDNA 6330403K07 gene 1.826216742
Pycard PYD and CARD domain containing 1.825644674
Epor erythropoietin receptor 1.825047483
Plcxd3 phosphatidylinositol-specific phospholipase C, X domain containing 3 1.820687094
Lpar1 lysophosphatidic acid receptor 1 1.819552906
H2-M3 histocompatibility 2, M region locus 3 1.818003529
D630039A03Rik RIKEN cDNA D630039A03 gene 1.814152832
Chrm4 cholinergic receptor, muscarinic 4 1.80193129
Muc1 mucin 1, transmembrane 1.798053565
Kctd12 potassium channel tetramerisation domain containing 12 1.797364484
Itpkb inositol 1,4,5-trisphosphate 3-kinase B 1.795604977
Trim21 tripartite motif-containing 21 1.791401849
Ism1 isthmin 1 homolog (zebrafish) 1.789990586
Npas1 neuronal PAS domain protein 1 1.788328784
Riiad1 regulatory subunit of type II PKA R-subunit (RIIa) domain containing 1 1.781233932
Zbtb46 zinc finger and BTB domain containing 46 1.781116643
Igsf5 immunoglobulin superfamily, member 5 1.780722857
Myb myeloblastosis oncogene 1.777169147
Lrrc15 leucine rich repeat containing 15 1.774126646
Meg3 maternally expressed 3 1.772921919
Inadl InaD-like (Drosophila) 1.766008149
Nnat neuronatin 1.765975098
Cd68 CD68 antigen 1.763499296
Gapdhs glyceraldehyde-3-phosphate dehydrogenase, spermatogenic 1.760879272
Depdc1a DEP domain containing 1a 1.760691318
Cdh1 cadherin 1 1.757244574
Pgm2l1 phosphoglucomutase 2-like 1 1.756499298
Syt14 synaptotagmin XIV 1.75599532
Nags N-acetylglutamate synthase 1.754753036
Sult5a1 sulfotransferase family 5A, member 1 1.753408322
Tnfrsf26 tumor necrosis factor receptor superfamily, member 26 1.751807195
Ccdc138 coiled-coil domain containing 138 1.751003539
Itga2 integrin alpha 2 1.750683151
Zdhhc23 zinc finger, DHHC domain containing 23 1.749850901
Spink2 serine peptidase inhibitor, Kazal type 2 1.746872121
Ncapg2 non-SMC condensin II complex, subunit G2 1.746337012
Itgb8 integrin beta 8 1.74443761
Pvrl4 poliovirus receptor-related 4 1.743592617
Arc activity regulated cytoskeletal-associated protein 1.74114216
Gpr165 G protein-coupled receptor 165 1.73753134
Cit citron 1.736766736
AI182371 expressed sequence AI182371 1.736269624
Espl1 extra spindle pole bodies 1, separase 1.73620945
Tppp3 tubulin polymerization-promoting protein family member 3 1.734602387
Pif1 PIF1 5′-to-3′ DNA helicase homolog (S. cerevisiae) 1.734190035
Ppp1r32 protein phosphatase 1, regulatory subunit 32 1.731643563
Igfbp4 insulin-like growth factor binding protein 4 1.731549943
C130083M11Rik RIKEN cDNA C130083M11 gene 1.731353119
Pkp3 plakophilin 3 1.731260715
Has1 hyaluronan synthase1 1.729830883
Capn9 calpain 9 1.72369438
Sytl4 synaptotagmin-like 4 1.721623887
Zim1 zinc finger, imprinted 1 1.720188899
Gpr155 G protein-coupled receptor 155 1.719552305
AI506816 expressed sequence AI506816 1.717359411
Tacr3 tachykinin receptor 3 1.716797642
Cd22 CD22 antigen 1.71619918
Igtp interferon gamma induced GTPase 1.714782977
Phf16 jade family PHD finger 3 1.714072343
Cenpl centromere protein L 1.713967793
H2-T10 histocompatibility 2, T region locus 10 1.713898888
Kcnq4 potassium voltage-gated channel, subfamily Q, member 4 1.713819295
Pygl liver glycogen phosphorylase 1.713219496
Parp14 poly (ADP-ribose) polymerase family, member 14 1.712904834
Cbl Casitas B-lineage lymphoma 1.712076302
Pax5 paired box 5 1.711506771
Cdca7l cell division cycle associated 7 like 1.711281384
S100z S100 calcium binding protein, zeta 1.71119361
Oas1d 2′-5′ oligoadenylate synthetase 1D 1.710958776
Hist2h2be histone cluster 2, H2be 1.710557973
Cyp27b1 cytochrome P450, family 27, subfamily b, polypeptide 1 1.707093406
Gm14393 predicted gene 14393 1.705817139
Serpine1 serine (or cysteine) peptidase inhibitor, clade E, member 1 1.704526466
Rph3a rabphilin 3A 1.70394173
Trpa1 transient receptor potential cation channel, subfamily A, member 1 1.703425675
C130074G19Rik RIKEN cDNA C130074G19 gene 1.703122256
Aif1l allograft inflammatory factor 1-like 1.701556433
Pak3 p21 protein (Cdc42/Rac)-activated kinase 3 1.701157833
Myo18b myosin XVIIIb 1.700990401
E2f8 E2F transcription factor 8 1.699972019
Ptgs1 prostaglandin-endoperoxide synthase 1 1.699511354
Oas1b 2′-5′ oligoadenylate synthetase 1B 1.698496211
Tnfrsf14 tumor necrosis factor receptor superfamily, member 14 (herpesvirus entry mediator) 1.694760019
Irf4 interferon regulatory factor 4 1.691390757
Cacna1h calcium channel, voltage-dependent, T type, alpha 1H subunit 1.690046569
Itpripl1 inositol 1,4,5-triphosphate receptor interacting protein-like 1 1.689604989
Ppl periplakin 1.686565093
Itpr3 inositol 1,4,5-triphosphate receptor 3 1.686537036
Crybb1 crystallin, beta B1 1.682669688
Dnahc11 dynein, axonemal, heavy chain 11 1.681584178
Nmi N-myc (and STAT) interactor 1.68110286
Barx2 BarH-like homeobox 2 1.680908274
Slc6a13 solute carrier family 6 (neurotransmitter transporter, GABA), member 13 1.68056693
Naip5 NLR family, apoptosis inhibitory protein 5 1.679951985
Enkur enkurin, TRPC channel interacting protein 1.679510715
Eno4 enolase 4 1.677079407
Apbb1ip amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein 1.67527159
Prr11 proline rich 11 1.674976669
1810041L15Rik RIKEN cDNA 1810041L15 gene 1.673951815
Clspn claspin 1.673469202
Eri2 exoribonuclease 2 1.672638876
Atp1b2 ATPase, Na+/K+ transporting, beta 2 polypeptide 1.6690417
Rian RNA imprinted and accumulated in nucleus 1.668007759
Kif14 kinesin family member 14 1.667086542
Tmem217 transmembrane protein 217 1.665372608
Zfp69 zinc finger protein 69 1.663553195
Cdh7 cadherin 7, type 2 1.661454748
Aldh1a1 aldehyde dehydrogenase family 1, subfamily A1 1.66120256
Bcmo1 beta-carotene oxygenase 1 1.661127717
Tap2 transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) 1.660816866
Nr5a1 nuclear receptor subfamily 5, group A, member 1 1.660517584
Dnahc2 dynein, axonemal, heavy chain 2 1.66008487
Col6a6 collagen, type VI, alpha 6 1.657511638
Card10 caspase recruitment domain family, member 10 1.655901663
Egr4 early growth response 4 1.655665237
4930558J18Rik RIKEN cDNA 4930558J18 gene 1.654479023
6330545A04Rik REM2 and RAB-like small GTPase 1 1.65417515
Grem1 gremlin 1 1.654018075
5730590G19Rik TOPBP1-interacting checkpoint and replication regulator 1.652818148
2810047C21Rik1 RIKEN cDNA 2810047C21 gene 1 1.65113605
Fam46c family with sequence similarity 46, member C 1.651077682
Npnt nephronectin 1.650979264
Glyatl3 glycine-N-acyltransferase-like 3 1.650670312
Nlrc5 NLR family, CARD domain containing 5 1.650654294
Nfatc2 nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 2 1.650150946
Sacs sacsin 1.64588667
Tec tec protein tyrosine kinase 1.645502251
Hdc histidine decarboxylase 1.644865933
Kntc1 kinetochore associated 1 1.644447557
Foxn3 forkhead box N3 1.644011055
E2f7 E2F transcription factor 7 1.643817344
Dnajc22 DnaJ (Hsp40) homolog, subfamily C, member 22 1.643273936
Tmie transmembrane inner ear 1.641281841
Nusap1 nucleolar and spindle associated protein 1 1.640184376
Mks1 Meckel syndrome, type 1 1.637979158
Sass6 spindle assembly 6 homolog (C. elegans) 1.636570781
E330016A19Rik Mab-21 domain containing 1 1.635744024
Casp7 caspase 7 1.633807488
Nptx1 neuronal pentraxin 1 1.633711231
Fcer1g Fc receptor, IgE, high affinity I, gamma polypeptide 1.632402698
Fras1 Fraser syndrome 1 homolog (human) 1.631042076
Il18rap interleukin 18 receptor accessory protein 1.630817112
Gabrq gamma-aminobutyric acid [205] A receptor, subunit theta 1.627309878
Crtac1 cartilage acidic protein 1 1.626751631
Serpinb5 serine (or cysteine) peptidase inhibitor, clade B, member 5 1.625755157
Serping1 serine (or cysteine) peptidase inhibitor, clade G, member 1 1.624733391
Mov10 Moloney leukemia virus 10 1.62361548
Atf7ip activating transcription factor 7 interacting protein 1.623411794
Trim56 tripartite motif-containing 56 1.623400542
Plcl1 phospholipase C-like 1 1.623166505
Six1 sine oculis-related homeobox 1 1.620186622
S100a11 S100 calcium binding protein A11 1.619063983
Mapk15 mitogen-activated protein kinase 15 1.618467056
Col6a3 collagen, type VI, alpha 3 1.617936515
Saa3 serum amyloid A 3 1.617914086
Phactr3 phosphatase and actin regulator 3 1.617405026
Mfsd4 major facilitator superfamily domain containing 4 1.617374757
Ccne2 cyclin E2 1.6161398
Ltbp4 latent transforming growth factor beta binding protein 4 1.615855288
Eps8l1 EPS8-like 1 1.614777064
Ier5l immediate early response 5-like 1.613093428
Stx1b syntaxin 1B 1.612612712
Slc26a10 solute carrier family 26, member 10 1.611623782
Cd44 CD44 antigen 1.610397681
Zfp871 zinc finger protein 871 1.60982515
Barhl1 BarH-like 1 (Drosophila) 1.609625426
Rttn rotatin 1.609596418
Hmgb2 high mobility group box 2 1.607645149
Ttf2 transcription termination factor, RNA polymerase II 1.607564919
Map3k13 mitogen-activated protein kinase kinase kinase 13 1.607019015
Lgi1 leucine-rich repeat LGI family, member 1 1.606897605
Itgal integrin alpha L 1.606459934
Bok BCL2-related ovarian killer 1.605189918
Aspm asp (abnormal spindle)-like, microcephaly associated (Drosophila) 1.601106196
Tmem158 transmembrane protein 158 1.600863168
Pla2g16 phospholipase A2, group XVI 1.600243002
Kif11 kinesin family member 11 1.598954626
Plek2 pleckstrin 2 1.598457072
4933433C11Rik RIKEN cDNA 4933433C11 gene 1.597143565
Cd200 CD200 antigen 1.595843304
Gm5779 ribosomal protein, large, P0 pseudogene 1.595588909
Agt angiotensinogen (serpin peptidase inhibitor, clade A, member 8) 1.594944254
Elf4 E74-like factor 4 (ets domain transcription factor) 1.594610418
Herc6 hect domain and RLD 6 1.593683341
Dnmt3b DNA methyltransferase 3B 1.593583925
Rsad2 radical S-adenosyl methionine domain containing 2 1.591668617
Tph1 tryptophan hydroxylase 1 1.590974817
Nrg1 neuregulin 1 1.589850379
Alox5ap arachidonate 5-lipoxygenase activating protein 1.589718145
Necab3 N-terminal EF-hand calcium binding protein 3 1.589243293
Esrp2 epithelial splicing regulatory protein 2 1.588748761
4930422G04Rik zinc finger, GRF-type containing 1 1.587395917
Fam92b family with sequence similarity 92, member B 1.587257286
Sp100 nuclear antigen Sp100 1.586955859
Emid1 EMI domain containing 1 1.58690086
4831426I19Rik spectrin repeat containing, nuclear envelope family member 3 1.586595102
Rgs16 regulator of G-protein signaling 16 1.586498327
Ccdc8 coiled-coil domain containing 8 1.585047418
Eps8 epidermal growth factor receptor pathway substrate 8 1.584900202
Mdh1b malate dehydrogenase 1B, NAD (soluble) 1.584504767
Atad5 ATPase family, AAA domain containing 5 1.583894233
Plagl1 pleiomorphic adenoma gene-like 1 1.579899731
Zfp773 zinc finger protein 773 1.578560987
Pnpt1 polyribonucleotide nucleotidyltransferase 1 1.578412187
Tnfsf13b tumor necrosis factor (ligand) superfamily, member 13b 1.577272575
Mctp2 multiple C2 domains, transmembrane 2 1.577057213
Mycl1 v-myc myelocytomatosis viral oncogene homolog, lung carcinoma derived (avian) 1.576943532
Hells helicase, lymphoid specific 1.57630641
Dclre1b DNA cross-link repair 1B, PSO2 homolog (S. cerevisiae) 1.574844081
Ddx11 DEAD/H (Asp-Glu-Ala-Asp/His) box helicase 11 1.572593722
Nr1d1 nuclear receptor subfamily 1, group D, member 1 1.572470552
Glyat glycine-N-acyltransferase 1.571915864
Bcl2l11 BCL2-like 11 (apoptosis facilitator) 1.571148994
Met met proto-oncogene 1.56931395
D8Ertd82e DNA segment, Chr 8, ERATO Doi 82, expressed 1.568395057
H2-T23 histocompatibility 2, T region locus 23 1.567642944
Pik3c2g phosphatidylinositol 3-kinase, C2 domain containing, gamma polypeptide 1.567473443
Kif18a kinesin family member 18A 1.567215966
Zbtb26 zinc finger and BTB domain containing 26 1.565475576
Ifit2 interferon-induced protein with tetratricopeptide repeats 2 1.56540396
Cx3cl1 chemokine (C-X3-C motif) ligand 1 1.565369239
Pabpc5 poly(A) binding protein, cytoplasmic 5 1.565006881
Synpo synaptopodin 1.564640268
Dapp1 dual adaptor for phosphotyrosine and 3-phosphoinositides 1 1.562782494
2810417H13Rik RIKEN cDNA 2810417H13 gene 1.561013485
Mybl1 myeloblastosis oncogene-like 1 1.560151359
Insrr insulin receptor-related receptor 1.559127596
Rasal1 RAS protein activator like 1 (GAP1 like) 1.558061305
Pttg1 pituitary tumor-transforming gene 1 1.558007307
Pyroxd2 pyridine nucleotide-disulphide oxidoreductase domain 2 1.557957632
Icam5 intercellular adhesion molecule 5, telencephalin 1.557842087
Hjurp Holliday junction recognition protein 1.557497665
B3gnt7 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 7 1.557120938
Hpca hippocalcin 1.555685034
Zfp551 zinc fingr protein 551 1.555390681
Zfp442 zinc finger protein 442 1.555308746
Ncaph non-SMC condensin I complex, subunit H 1.555013386
Junb jun B proto-oncogene 1.554409906
Eda2r ectodysplasin A2 receptor 1.554112562
C1galt1 core 1 synthase, glycoprotein-N-acetylgalactosamine 3-beta-galactosyltransferase, 1 1.554107176
Lnpep leucyl/cystinyl aminopeptidase 1.55275477
Rbm15 RNA binding motif protein 15 1.552600869
Parp9 poly (ADP-ribose) polymerase family, member 9 1.552128497
Aff2 AF4/FMR2 family, member 2 1.551510005
Oplah 5-oxoprolinase (ATP-hydrolysing) 1.551102472
Arhgap27 Rho GTPase activating protein 27 1.550438177
Prex1 phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1 1.550216808
Ttk Ttk protein kinase 1.549523892
Ccrl2 chemokine (C-C motif) receptor-like 2 1.547949064
Iqgap2 IQ motif containing GTPase activating protein 2 1.546832519
Recql4 RecQ protein-like 4 1.546574145
Zwilch zwilch kinetochore protein 1.546023233
Mybl2 myeloblastosis oncogene-like 2 1.545729637
Lrp2bp Lrp2 binding protein 1.545528223
Arhgap11a Rho GTPase activating protein 11A 1.544071971
Ankrd45 ankyrin repeat domain 45 1.543992773
Baz1a bromodomain adjacent to zinc finger domain 1A 1.542731506
1700001L05Rik RIKEN cDNA 1700001L05 gene 1.542480232
Gsdmd gasdermin D 1.54238401
Col9a2 collagen, type IX, alpha 2 1.53804104
Agbl2 ATP/GTP binding protein-like 2 1.537512351
Gas2l3 growth arrest-specific 2 like 3 1.537307746
Mapk13 mitogen-activated protein kinase 13 1.536768657
Fancd2 Fanconi anemia, complementation group D2 1.536442738
Rbm43 RNA binding motif protein 43 1.53570169
Hmmr hyaluronan mediated motility receptor (RHAMM) 1.53533875
5330437I02Rik O-acyltransferase like 1.534939721
Kif20b kinesin family member 20B 1.533580609
1500004A13Rik RIKEN cDNA 1500004A13 gene 1.533202228
Irak4 interleukin-1 receptor-associated kinase 4 1.531255472
Rasd2 RASD family, member 2 1.530652724
Dtl denticleless homolog (Drosophila) 1.53000355
Ncapg non-SMC condensin I complex, subunit G 1.529938859
Fam134b family with sequence similarity 134, member B 1.528747352
Syne2 spectrin repeat containing, nuclear envelope 2 1.528388174
Pard3 par-3 family cell polarity regulator 1.528243043
Mtap1a microtubule-associated protein 1 A 1.527840563
Cacna1g calcium channel, voltage-dependent, T type, alpha 1G subunit 1.52778126
Bub1 budding uninhibited by benzimidazoles 1 homolog (S. cerevisiae) 1.527609715
Coro1a coronin, actin binding protein 1A 1.526906795
Meis2 Meis homeobox 2 1.526873986
Vrk1 vaccinia related kinase 1 1.526478215
Pola1 polymerase (DNA directed), alpha 1 1.526287773
Dlgap3 discs, large (Drosophila) homolog-associated protein 3 1.526253919
Trim68 tripartite motif-containing 68 1.526207372
Ttc12 tetratricopeptide repeat domain 12 1.525522015
Fam72a family with sequence similarity 72, member A 1.523863839
Icosl icos ligand 1.523589235
Dtx3l deltex 3-like (Drosophila) 1.523180591
Itpka inositol 1,4,5-trisphosphate 3-kinase A 1.522780499
Nrgn neurogranin 1.522575744
Smtn smoothelin 1.522570467
Elk4 ELK4, member of ETS oncogene family 1.522326697
Atad2 ATPase family, AAA domain containing 2 1.521671561
Cep192 centrosomal protein 192 1.521477501
4930588N13Rik testis expressed 26 1.520977699
Pxmp2 peroxisomal membrane protein 2 1.520197746
Ncapd2 non-SMC condensin I complex, subunit D2 1.519845844
Wdr76 WD repeat domain 76 1.519166503
Stac2 SH3 and cysteine rich domain 2 1.518885376
Defb41 defensin beta 41 1.517398482
Cdh24 cadherin-like 24 1.514654767
1700048O20Rik RIKEN cDNA 1700048O20 gene 1.514600174
Espnl espin-like 1.514412265
Rab39 RAB39, member RAS oncogene family 1.51427056
Selenbp1 selenium binding protein 1 1.513979846
Per3 period circadian clock 3 1.513715417
Mki67 antigen identified by monoclonal antibody Ki 67 1.513610498
Ankle1 ankyrin repeat and LEM domain containing 1 1.513353477
Polq polymerase (DNA directed), theta 1.513254876
Mdc1 mediator of DNA damage checkpoint 1 1.513077621
Dusp1 dual specificity phosphatase 1 1.513065036
Rps6ka5 ribosomal protein S6 kinase, polypeptide 5 1.512767213
Fanci Fanconi anemia, complementation group I 1.51272527
Tal1 T cell acute lymphocytic leukemia 1 1.512026055
Wdr62 WD repeat domain 62 1.511651945
Dtx2 deltex 2 homolog (Drosophila) 1.511293641
D430020J02Rik RIKEN cDNA D430020J02 gene 1.511124995
Lonrf3 LON peptidase N-terminal domain and ring finger 3 1.510819176
Lfng LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase 1.510241221
Gabpb2 GA repeat binding protein, beta 2 1.510030825
Cttnbp2nl CTTNBP2 N-terminal like 1.509582915
Pclo piccolo (presynaptic cytomatrix protein) 1.508686448
Prc1 protein regulator of cytokinesis 1 1.507835454
Fanca Fanconi anemia, complementation group A 1.507343268
Zbtb37 zinc finger and BTB domain containing 37 1.506978673
Anln anillin, actin binding protein 1.506023206
Ovol2 ovo-like 2 (Drosophila) 1.505520132
Gpx2 glutathione peroxidase 2 1.505259268
Tspyl5 testis-specific protein, Y-encoded-like 5 1.504758535
Gm13889 predicted gene 13889 1.504595833
Meox1 mesenchyme homeobox 1 1.503504306
Baiap2 brain-specific angiogenesis inhibitor 1-associated protein 2 1.503424063
Bspry B-box and SPRY domain containing 1.503370917
Alms1 Alstrom syndrome 1 1.50309063
Ctsh cathepsin H 1.500735782
Mbl2 mannose-binding lectin (protein C) 2 1.500699374
Pparg peroxisome proliferator activated receptor gamma 1.50019392
Onecut2 one cut domain, family member 2 1.500059784
Abcc3 ATP-binding cassette, sub-family C (CFTR/MRP), member 3 0.66666505
Fam195a family with sequence similarity 195, member A 0.666508879
Ppm1j protein phosphatase 1J 0.666503797
C2cd4a C2 calcium-dependent domain containing 4A 0.666200342
Izumo4 IZUMO family member 4 0.665312018
Sik1 salt inducible kinase 1 0.665034919
2210016F16Rik RIKEN cDNA 2210016F16 gene 0.664613268
Acot13 acyl-CoA thioesterase 13 0.66406299
Prkar1a protein kinase, cAMP dependent regulatory, type I, alpha 0.663852209
Haghl hydroxyacylglutathione hydrolase-like 0.663668635
Calca calcitonin/calcitonin-related polypeptide, alpha 0.663565139
5730528L13Rik Myb/SANT-like DNA-binding domain containing 3 0.663464878
Kcnab2 potassium voltage-gated channel, shaker-related subfamily, beta member 2 0.663361413
Phgdh 3-phosphoglycerate dehydrogenase 0.662927038
Dtx1 deltex 1 homolog (Drosophila) 0.662269352
Leprel4 prolyl 3-hydroxylase family member 4 (non-enzymatic) 0.661910013
Mcfd2 multiple coagulation factor deficiency 2 0.661733398
Il11 interleukin 11 0.661628828
Tm7sf2 transmembrane 7 superfamily member 2 0.661488509
Bcar3 breast cancer anti-estrogen resistance 3 0.661408275
Serpinb6a serine (or cysteine) peptidase inhibitor, clade B, member 6a 0.661128219
Hist1h1d histone cluster 1, H1d 0.660781866
Inpp4b inositol polyphosphate-4-phosphatase, type II 0.660725532
Fam71e1 family with sequence similarity 71, member E1 0.660592273
Ripk2 receptor (TNFRSF)-interacting serine-threonine kinase 2 0.660390833
Slc25a29 solute carrier family 25 (mitochondrial carrier, palmitoylcarnitine transporter), member 29 0.659842679
H2-M10.1 histocompatibility 2, M region locus 10.1 0.659539056
Cyb5r1 cytochrome b5 reductase 1 0.658411756
Isca1 iron-sulfur cluster assembly 1 homolog (S. cerevisiae) 0.658398065
Mdga1 MAM domain containing glycosylphosphatidylinositol anchor 1 0.658381179
Tmsb4x thymosin, beta 4, X chromosome 0.658331438
Vopp1 vesicular, overexpressed in cancer, prosurvival protein 1 0.658128407
Tgif1 TGFB-induced factor homeobox 1 0.657832869
2310034G01Rik RIKEN cDNA 2310034G01 gene 0.65768287
Cdhr2 cadherin-related family member 2 0.657628624
Clu clusterin 0.657572559
Igf2 insulin-like growth factor 2 0.657462721
Eid2 EP300 interacting inhibitor of differentiation 2 0.65705999
Gstm6 glutathione S-transferase, mu 6 0.657038584
Rab24 RAB24, member RAS oncogene family 0.656603798
Lmf1 lipase maturation factor 1 0.656302576
Fabp3 fatty acid binding protein 3, muscle and heart 0.655851456
Pdzk1ip1 PDZK1 interacting protein 1 0.655426541
Lhfpl2 lipoma HMGIC fusion partner-like 2 0.654710036
Ropn1l ropporin 1-like 0.654418755
Hhip Hedgehog-interacting protein 0.654131684
Rps6ka6 ribosomal protein S6 kinase polypeptide 6 0.654017434
Elovl6 ELOVL family member 6, elongation of long chain fatty acids (yeast) 0.653599144
Uqcrb ubiquinol-cytochrome c reductase binding protein 0.653485441
Apold1 apolipoprotein L domain containing 1 0.652986015
Map3k8 mitogen-activated protein kinase kinase kinase 8 0.652907718
Cabp1 calcium binding protein 1 0.652895046
Shisa4 shisa family member 4 0.652850697
Bicc1 bicaudal C homolog 1 (Drosophila) 0.652500992
Muted biogenesis of lysosomal organelles complex-1, subunit 5, muted 0.652389289
Tex15 testis expressed gene 15 0.65159164
Idua iduronidase, alpha-L- 0.651298135
Atp4a ATPase, H+/K+ exchanging, gastric, alpha polypeptide 0.651127962
Gde1 glycerophosphodiester phosphodiesterase 1 0.651043118
Aldh5a1 aldhehyde dehydrogenase family 5, subfamily A1 0.650923092
Bpgm 2,3-bisphosphoglycerate mutase 0.650748055
Ell2 elongation factor RNA polymerase II 2 0.650546461
Prdx6 peroxiredoxin 6 0.650288132
Kazn kazrin, periplakin interacting protein 0.650033962
Mef2b myocyte enhancer factor 2B 0.649945205
Ryr2 ryanodine receptor 2, cardiac 0.649493503
Folr1 folate receptor 1 (adult) 0.649479998
2210408F21Rik RIKEN cDNA 2210408F21 gene 0.648997128
Txndc5 thioredoxin domain containing 5 0.64874706
Slc46a1 solute carrier family 46, member 1 0.6485317
Slc3a2 solute carrier family 3 (activators of dibasic and neutral amino acid transport), member 2 0.648374834
Sfxn1 sideroflexin 1 0.648061664
4921530D09Rik coiled-coil domain containing 183 0.647472579
Nceh1 neutral cholesterol ester hydrolase 1 0.647380583
Kcnk1 potassium channel, subfamily K, member 1 0.64710557
Mrpl36 mitochondrial ribosomal protein L36 0.647010935
Slc35f4 solute carrier family 35, member F4 0.646917211
Mfsd1 major facilitator superfamily domain containing 1 0.646064003
2310014L17Rik ring finger protein 225 0.645575172
Pcbd2 pterin 4 alpha carbinolamine dehydratase/dimerization cofactor of hepatocyte nuclear factor 1 alpha (TCF1) 2 0.645424389
Sdr42e1 short chain dehydrogenase/reductase family 42E, member 1 0.645150207
Entpd2 ectonucleoside triphosphate diphosphohydrolase 2 0.64503395
Atp1a3 ATPase, Na+/K+ transporting, alpha 3 polypeptide 0.644988347
Cdh6 cadherin 6 0.644978064
Gm4262 predicted gene 4262 0.644731778
Drp2 dystrophin related protein 2 0.643951973
Col16a1 collagen, type XVI, alpha 1 0.642837056
Psg23 pregnancy-specific glycoprotein 23 0.642613413
Ptprj protein tyrosine phosphatase, receptor type, J 0.642533242
Derl3 Der1-like domain family, member 3 0.6423756
Porcn porcupine homolog (Drosophila) 0.642254055
Cpm carboxypeptidase M 0.642150338
Gm15706 predicted gene 15706 0.642070224
Ffar2 free fatty acid receptor 2 0.642047527
Svop SV2 related protein 0.641183409
Stk35 serine/threonine kinase 35 0.64088393
Txndc15 thioredoxin domain containing 15 0.640684503
Slc39a11 solute carrier family 39 (metal ion transporter), member 11 0.640462497
Tmed6 transmembrane emp24 protein transport domain containing 6 0.639686526
6530402F18Rik RIKEN cDNA 6530402F18 gene 0.639641301
Pycr1 pyrroline-5-carboxylate reductase 1 0.639484812
Dscam Down syndrome cell adhesion molecule 0.639334565
Adamts6 a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondin type 1 motif, 6 0.639047024
Syngr3 synaptogyrin 3 0.63884684
Auh AU RNA binding protein/enoyl-coenzyme A hydratase 0.638067519
2610029I01Rik thiosulfate sulfurtransferase (rhodanese)-like domain containing 3 0.637871179
Sncb synuclein, beta 0.637439356
Cplx2 complexin 2 0.637263528
Lrrc8b leucine rich repeat containing 8 family, member B 0.63700341
Car13 carbonic anhydrase 13 0.6367893
Ier3 immediate early response 3 0.636413789
Palld palladin, cytoskeletal associated protein 0.636029241
Wnt9a wingless-type MMTV integration site family, member 9A 0.635583246
Tmem91 transmembrane protein 91 0.635389432
Cetn4 centrin 4 0.634603771
Clec11a C-type lectin domain family 11, member a 0.634561105
2810405K02Rik family with sequence similarity 213, member B 0.634530756
Thrb thyroid hormone receptor beta 0.634055925
Tmem221 transmembrane protein 221 0.633943864
Trnp1 TMF1-regulated nuclear protein 1 0.633855108
Pfkp phosphofructokinase, platelet 0.633021322
Naglu alpha-N-acetylglucosaminidase (Sanfilippo disease IIIB) 0.632691448
Mreg melanoregulin 0.632323174
Gm20594 predicted gene, 20594 0.632193453
Lman2 lectin, mannose-binding 2 0.632132108
Serpina1b serine (or cysteine) preptidase inhibitor, clade A, member 1B 0.631820214
Cd248 CD248 antigen, endosialin 0.631665201
Dhrs7 dehydrogenase/reductase (SDR family) member 7 0.631557502
B930041F14Rik RIKEN cDNA B930041F14 gene 0.631168889
Smad3 SMAD family member 3 0.630986918
Fam69b family with sequence similarity 69, member B 0.630968549
Ddit3 DNA-damage inducible transcript 3 0.63085922
Gp5 glycoprotein 5 (platelet) 0.63026218
Lrrc29 leucine rich repeat containing 29 0.630214564
Arg1 arginase, liver 0.629981339
T2 brachyury 2 0.629633846
Fignl2 fidgetin-like 2 0.629392547
Tceal3 transcription elongation factor A (SII)-like 3 0.628905431
Dusp5 dual specificity phosphatase 5 0.628837431
Wdr25 WD repeat domain 25 0.628696658
Tnfaip2 tumor necrosis factor, alpha-induced protein 2 0.6283747
Lypd1 Ly6/Plaur domain containing 1 0.628290208
Fam189a1 family with sequence similarity 189, member A1 0.628234902
Slc5a1 solute carrier family 5 (sodium/glucose cotransporter), member 1 0.626967689
Frzb frizzled-related protein 0.626773461
Snca synuclein, alpha 0.625920356
Boc biregional cell adhesion molecule-related/down-regulated by oncogenes (Cdon) binding protein 0.625777634
Amph amphiphysin 0.625677878
Gmpr guanosine monophosphate reductase 0.625547785
P2rx4 purinergic receptor P2X, ligand-gated ion channel 4 0.62519667
Mycn v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian) 0.625116505
Col20a1 collagen, type XX, alpha 1 0.624833626
Iqsec3 IQ motif and Sec7 domain 3 0.624743547
Nfkbiz nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor, zeta 0.624279934
Car12 carbonic anyhydrase 12 0.624198156
Ak4 adenylate kinase 4 0.623976673
Asb5 ankyrin repeat and SOCs box-containing 5 0.623789857
Nrxn3 neurexin III 0.623147674
Rhou ras homolog gene family, member U 0.623054384
Limch1 LIM and calponin homology domains 1 0.622803087
Kcnt2 potassium channel, subfamily T, member 2 0.62238621
Tmed9 transmembrane emp24 protein transport domain containing 9 0.620950426
Fam84b family with sequence similarity 84, member B 0.620824759
Rab6b RAB6B, member RAS oncogene family 0.619705638
Nfil3 nuclear factor, interleukin 3, regulated 0.619465568
Iapp islet amyloid polypeptide 0.619416621
Slc7a3 solute carrier family 7 (cationic amino acid transporter, y+ system), member 3 0.61939129
Rab20 RAB20, member RAS oncogene family 0.619150483
Dctd dCMP deaminase 0.618812395
Zfhx4 zinc finger homeodomain 4 0.61845563
Sema4d sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D 0.617107186
Gm11127 predicted gene 11127 0.616756534
Ptp4a3 protein tyrosine phosphatase 4a3 0.61671806
Ankrd34b ankyrin repeat domain 34B 0.616590685
Gna15 guanine nucleotide binding protein, alpha 15 0.616486839
Magee2 melanoma antigen, family E, 2 0.616478292
Rad51ap2 RAD51 associated protein 2 0.616130987
Ctsl cathepsin L 0.615838941
Slc41a3 solute carrier family 41, member 3 0.615448908
1110007C09Rik RIKEN cDNA 1110007C09 gene 0.615166992
B4galt7 xylosylprotein beta1,4-galactosyltransferase, polypeptide 7 (galactosyltransferase I) 0.615048464
Ak3 adenylate kinase 3 0.614299026
AA467197 expressed sequence AA467197 0.614189179
Tmed3 transmembrane emp24 domain containing 3 0.614179388
Gpt2 glutamic pyruvate transaminase (alanine aminotransferase) 2 0.613619825
Slc7a5 solute carrier family 7 (cationic amino acid transporter, y+ system), member 5 0.613559006
Aicda activation-induced cytidine deaminase 0.613439937
Wipf3 WAS/WASL interacting protein family, member 3 0.612792268
S1pr4 sphingosine-1-phosphate receptor 4 0.612180077
Synpo2 synaptopodin 2 0.611828408
Ndufs6 NADH dehydrogenase (ubiquinone) Fe-S protein 6 0.611574009
Sorcs2 sortilin-related VPS10 domain containing receptor 2 0.611364633
Cyp46a1 cytochrome P450, family 46, subfamily a, polypeptide 1 0.610762758
Syn3 synapsin III 0.610391594
Fxyd6 FXYD domain-containing ion transport regulator 6 0.60998979
Cpne5 copine V 0.609582335
Dmrtc1a DMRT-like family C1a 0.609417148
Mblac2 metallo-beta-lactamase domain containing 2 0.60926932
Asns asparagine synthetase 0.609195842
Satb1 special AT-rich sequence binding protein 1 0.608940427
Tmc7 transmembrane channel-like gene family 7 0.60877457
Sdf2l1 stromal cell-derived factor 2-like 1 0.608632804
Rspo1 R-spondin homolog (Xenopus laevis) 0.608210657
Bglap-rs1 bone gamma-carboxyglutamate protein 3 0.607772793
Dusp26 dual specificity phosphatase 26 (putative) 0.607432919
Pid1 phosphotyrosine interaction domain containing 1 0.607390816
Higd2a HIG1 domain family, member 2A 0.607326826
Podn podocan 0.607201812
Odc1 ornithine decarboxylase, structural 1 0.606954384
Fam194a glutamate rich 6 0.606371982
Pm20d2 peptidase M20 domain containing 2 0.605349822
AI593442 expressed sequence AI593442 0.605250806
Fam193b family with sequence similarity 193, member B 0.604494031
Slc2a9 solute carrier family 2 (facilitated glucose transporter), member 9 0.604445429
Vsig8 V-set and immunoglobulin domain containing 8 0.603969248
Scn1b sodium channel, voltage-gated, type I, beta 0.603825672
Arhgef37 Rho guanine nucleotide exchange factor (GEF) 37 0.603713514
Gnmt glycine N-methyltransferase 0.603511012
2210411K11Rik transmembrane protein 238 0.603404767
Pnma3 paraneoplastic antigen MA3 0.602969946
Sil1 endoplasmic reticulum chaperone SIL1 homolog (S. cerevisiae) 0.602593493
9030617O03Rik RIKEN cDNA 9030617O03 gene 0.602492004
Csf1 colony stimulating factor 1 (macrophage) 0.602298679
Tm4sf4 transmembrane 4 superfamily member 4 0.601842962
Dusp15 dual specificity phosphatase-like 15 0.60101504
Cpz carboxypeptidase Z 0.600936726
C1qa complement component 1, q subcomponent, alpha polypeptide 0.600071368
Dse dermatan sulfate epimerase 0.598062846
Cthrc1 collagen triple helix repeat containing 1 0.59795466
Prkcb protein kinase C, beta 0.597397042
Vldlr very low density lipoprotein receptor 0.596539676
Ero1lb ERO1-like beta (S. cerevisiae) 0.596476002
Nucb2 nucleobindin 2 0.595914807
Sepw1 selenoprotein W, muscle 1 0.595913981
Crem cAMP responsive element modulator 0.595553492
Lingo4 leucine rich repeat and Ig domain containing 4 0.594716909
Rab11fip5 RAB11 family interacting protein 5 (class I) 0.594257454
AI118078 expressed sequence AI118078 0.594184551
Lst1 leukocyte specific transcript 1 0.593805351
Sat1 spermidine/spermine N1-acetyl transferase 1 0.592872177
Cebpb CCAAT/enhancer binding protein (C/EBP), beta 0.592755891
Amz1 archaelysin family metallopeptidase 1 0.592631822
Pck2 phosphoenolpyruvate carboxykinase 2 (mitochondrial) 0.591943752
Hs3st1 heparan sulfate (glucosamine) 3-O-sulfotransferase 1 0.591674654
A330023F24Rik RIKEN cDNA A330023F24 gene 0.591468811
Psph phosphoserine phosphatase 0.59044517
Lamc2 laminin, gamma 2 0.590255302
Bcl2l14 BCL2-like 14 (apoptosis facilitator) 0.586517476
Fam123c APC membrane recruitment 3 0.586477229
Cd82 CD82 antigen 0.586286605
Krt222 keratin 222 0.586016829
Defb1 defensin beta 1 0.585127934
Iars isoleucine-tRNA synthetase 0.584662514
Trp53i11 transformation related protein 53 inducible protein 11 0.58457539
Ppp1r9a protein phosphatase 1, regulatory (inhibitor) subunit 9A 0.583582287
Gm13003 predicted gene 13003 0.583328715
Grik2 glutamate receptor, ionotropic, kainate 2 (beta 2) 0.583004935
Susd2 sushi domain containing 2 0.582798068
Aldh1l2 aldehyde dehydrogenase 1 family, member L2 0.582142802
Jag1 jagged 1 0.580579686
Fank1 fibronectin type 3 and ankyrin repeat domains 1 0.58048794
Creb3l1 cAMP responsive element binding protein 3-like 1 0.579377273
Gm5124 nucleolar and coiled-body phosphoprotein 1 pseudogene 0.579004313
Cdh18 cadherin 18 0.578752329
Slc7a11 solute carrier family 7 (cationic amino acid transporter, y+ system), member 11 0.578645229
Epm2a epilepsy, progressive myoclonic epilepsy, type 2 gene alpha 0.577614955
Vstm2b V-set and transmembrane domain containing 2B 0.577367978
Ptgs2 prostaglandin-endoperoxide synthase 2 0.576376332
Pla2g12a phospholipase A2, group XIIA 0.575694367
B630019K06Rik novel protein similar to F-box and leucine-rich repeat protein 17 (Fbxl17) 0.575579055
Immp2l IMP2 inner mitochondrial membrane peptidase-like (S. cerevisiae) 0.575353288
Fam78b family with sequence similarity 78, member B 0.57418717
Clptm1l CLPTM1-like 0.573751927
Adcy5 adenylate cyclase 5 0.573696253
Rbp3 retinol binding protein 3, interstitial 0.573445784
Cplx1 complexin 1 0.572534684
Nr0b2 nuclear receptor subfamily 0, group B, member 2 0.570872313
Gys2 glycogen synthase 2 0.570685179
Cldn23 claudin 23 0.569565237
Zfp454 zinc finger protein 454 0.569365507
Kcnk16 potassium channel, subfamily K, member 16 0.568890147
Pecam1 platelet/endothelial cell adhesion molecule 1 0.568511722
Arhgap6 Rho GTPase activating protein 6 0.568297392
Lcn2 lipocalin 2 0.568285575
Slc34a1 solute carrier family 34 (sodium phosphate), member 1 0.567894561
Cdk18 cyclin-dependent kinase 18 0.56783237
Grid1 glutamate receptor, ionotropic, delta 1 0.567551022
Hpn hepsin 0.567516011
Megf11 multiple EGF-like-domains 11 0.567455828
Grid2ip glutamate receptor, ionotropic, delta 2 (Grid2) interacting protein 1 0.566654001
Pcdh7 protocadherin 7 0.566609619
Gpr162 G protein-coupled receptor 162 0.566259008
Tmsb10 thymosin, beta 10 0.566181298
Guca2a guanylate cyclase activator 2a (guanylin) 0.565745849
Hk1 hexokinase 1 0.565292319
Rell2 RELT-like 2 0.564604683
Lamb3 laminin, beta 3 0.564549505
Ccl28 chemokine (C-C motif) ligand 28 0.564410996
Col18a1 collagen, type XVIII, alpha 1 0.564340972
Gpr3 G-protein coupled receptor 3 0.563398255
Gmds GDP-mannose 4, 6-dehydratase 0.562927097
Slc16a1 solute carrier family 16 (monocarboxylic acid transporters), member 1 0.56267158
Pdxk-ps pyridoxal (pyridoxine, vitamin B6) kinase, pseudogene 0.56246569
Ttyh1 tweety homolog 1 (Drosophila) 0.562038553
Cxx1c CAAX box 1C 0.561655731
Wnk2 WNK lysine deficient protein kinase 2 0.561497304
Bcat1 branched chain aminotransferase 1, cytosolic 0.561199255
A730090N16Rik RIKEN cDNA A730090N16 gene 0.560582262
Pnma5 paraneoplastic antigen family 5 0.560041639
Fut1 fucosyltransferase 1 0.55856884
Fbxw4 F-box and WD-40 domain protein 4 0.556352054
Nfam1 Nfat activating molecule with ITAM motif 1 0.556114169
Sh2b2 SH2B adaptor protein 2 0.555228296
Sphk1 sphingosine kinase 1 0.554074931
Fam123a APC membrane recruitment 2 0.554028079
1700045I19Rik 0.553489556
Pamr1 peptidase domain containing associated with muscle regeneration 1 0.552938143
Ccl2 chemokine (C-C motif) ligand 2 0.552376175
Krtap17-1 keratin associated protein 17-1 0.552342866
4933408B17Rik RIKEN cDNA 4933408B17 gene 0.55179336
Ankrd34a ankyrin repeat domain 34A 0.551667158
Thsd7a thrombospondin, type I, domain containing 7A 0.551327321
Nipal2 NIPA-like domain containing 2 0.550709347
Prss23 protease, serine 23 0.550497532
Slc45a1 solute carrier family 45, member 1 0.550012381
Crispld2 cysteine-rich secretory protein LCCL domain containing 2 0.54981684
Flt4 FMS-like tyrosine kinase 4 0.549187997
Tspan17 tetraspanin 17 0.54857052
Cxcl12 chemokine (C-X-C motif) ligand 12 0.547801834
Dio1 deiodinase, iodothyronine, type I 0.547304263
Cpne4 copine IV 0.546705578
Ggh gamma-glutamyl hydrolase 0.546059854
Cdx2 caudal type homeobox 2 0.545939126
Serpinf1 serine (or cysteine) peptidase inhibitor, clade F, member 1 0.544727648
Phldb2 pleckstrin homology-like domain, family B, member 2 0.544172516
Pcp4 Purkinje cell protein 4 0.543816941
Cox6b2 cytochrome c oxidase subunit VIb polypeptide 2 0.543773594
Hyal1 hyaluronoglucosaminidase 1 0.543254829
5133401N09Rik idnK gluconokinase homolog (E. coli) 0.543190819
Fstl5 follistatin-like 5 0.542914906
Mocos molybdenum cofactor sulfurase 0.54287276
Adam23 a disintegrin and metallopeptidase domain 23 0.542698942
Cdcp1 CUB domain containing protein 1 0.542156399
Hist2h3c1 histone cluster 2, H3c1 0.541841951
Brsk2 BR serine/threonine kinase 2 0.538870681
Scn2a1 sodium channel, voltage-gated, type II, alpha 1 0.538228988
Dtx4 deltex 4 homolog (Drosophila) 0.537662963
Prokr1 prokineticin receptor 1 0.537657
Meig1 meiosis expressed gene 1 0.536780065
Ptpn18 protein tyrosine phosphatase, non-receptor type 18 0.53611968
Apcdd1 adenomatosis polyposis coli down-regulated 1 0.535966599
0610009L18Rik RIKEN cDNA 0610009L18 gene 0.535608589
Klhl35 kelch-like 35 0.535568495
Slc9a3r2 solute carrier family 9 (sodium/hydrogen exchanger), member 3 regulator 2 0.534320769
Ppp1r3f protein phosphatase 1, regulatory (inhibitor) subunit 3F 0.533553928
Slc1a4 solute carrier family 1 (glutamate/neutral amino acid transporter), member 4 0.533152072
Tspan9 tetraspanin 9 0.533036784
Sestd1 SEC14 and spectrin domains 1 0.532297981
Cdc42ep3 CDC42 effector protein (Rho GTPase binding) 3 0.530315933
Foxd2 forkhead box D2 0.530313728
Me3 malic enzyme 3, NADP(+)-dependent, mitochondrial 0.530060522
Panx1 pannexin 1 0.52951006
Gm600 nuclear GTPase, germinal center associated 0.528037758
Bdnf brain derived neurotrophic factor 0.527127915
9430020K01Rik RIKEN cDNA 9430020K01 gene 0.527010642
Chrna4 cholinergic receptor, nicotinic, alpha polypeptide 4 0.526979593
Syt5 synaptotagmin V 0.526529037
Gm9079 transmembrane emp24 domain trafficking protein 2 pseudogene 0.526383072
Gprc5a G protein-coupled receptor, family C, group 5, member A 0.526181708
Micall2 MICAL-like 2 0.525621794
E030010A14Rik transmembrane protein 252 0.524739755
Sv2c synaptic vesicle glycoprotein 2c 0.524254411
Ccdc147 cilia and flagella associated protein 58 0.523667872
Lix1 limb expression 1 homolog (chicken) 0.522908345
Bves blood vessel epicardial substance 0.522777516
Adssl1 adenylosuccinate synthetase like 1 0.521119086
Chchd10 coiled-coil-helix-coiled-coil-helix domain containing 10 0.521050099
Anxa3 annexin A3 0.520419527
Popdc3 popeye domain containing 3 0.520177896
Tgm5 transglutaminase 5 0.519549819
Gabra4 gamma-aminobutyric acid [205] A receptor, subunit alpha 4 0.519549459
Chgb chromogranin B 0.519527852
Dnm3 dynamin 3 0.51948176
Flrt2 fibronectin leucine rich transmembrane protein 2 0.51940615
Efcab4b calcium release activated channel regulator 2A 0.518635908
Thrsp thyroid hormone responsive 0.518465178
Pde3a phosphodiesterase 3A, cGMP inhibited 0.518316778
5530401A14Rik RIKEN cDNA 5530401A14 gene 0.518248521
Sox13 SRY (sex determining region Y)-box 13 0.517735088
Mospd1 motile sperm domain containing 1 0.517302475
Gm4013 predicted gene 4013 0.51652497
Ecel1 endothelin converting enzyme-like 1 0.516088003
Snai1 snail family zinc finger 1 0.515059854
Usp11 ubiquitin specific peptidase 11 0.51437342
Dok3 docking protein 3 0.512516373
Rpgr retinitis pigmentosa GTPase regulator 0.512153082
Tmem179 transmembrane protein 179 0.51041513
2810055F11Rik L-3-hydroxyproline dehydratase (trans-) 0.510198655
Olfm2 olfactomedin 2 0.509691783
Rassf4 Ras association (RalGDS/AF-6) domain family member 4 0.509590045
Mfsd6l major facilitator superfamily domain containing 6-like 0.509334024
Fzd1 frizzled homolog 1 (Drosophila) 0.508591762
Slc9a9 solute carrier family 9 (sodium/hydrogen exchanger), member 9 0.508573784
Jam2 junction adhesion molecule 2 0.508018521
Fbp2 fructose bisphosphatase 2 0.506136352
Vangl1 vang-like 1 (van gogh, Drosophila) 0.505867689
Cth cystathionase (cystathionine gamma-lyase) 0.505849807
Lif leukemia inhibitory factor 0.505571134
Tmcc3 transmembrane and coiled coil domains 3 0.505191055
Eda ectodysplasin-A 0.505125227
B4galt2 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 2 0.504863401
Adam11 a disintegrin and metallopeptidase domain 11 0.503758437
Slc24a3 solute carrier family 24 (sodium/potassium/calcium exchanger), member 3 0.502928773
Six2 sine oculis-related homeobox 2 0.502121707
Fxyd1 FXYD domain-containing ion transport regulator 1 0.500755754
Arhgap22 Rho GTPase activating protein 22 0.500706469
Reln reelin 0.500616935
Gm6484 predicted gene 6484 0.500075559
Lrig1 leucine-rich repeats and immunoglobulin-like domains 1 0.50006412
Kcnk10 potassium channel, subfamily K, member 10 0.499310794
Cct6b chaperonin containing Tcp1, subunit 6b (zeta) 0.498480854
Proz protein Z, vitamin K-dependent plasma glycoprotein 0.497652294
D630003M21Rik RIKEN cDNA D630003M21 gene 0.497648845
Spns2 spinster homolog 2 0.495624703
Igsf3 immunoglobulin superfamily, member 3 0.494266146
Sorl1 sortilin-related receptor, LDLR class A repeats-containing 0.494252442
Etnk2 ethanolamine kinase 2 0.493718291
B230319C09Rik RIKEN cDNA B230319C09 gene 0.493451432
Egfr epidermal growth factor receptor 0.493270187
Kcnc2 potassium voltage gated channel, Shaw-related subfamily, member 2 0.493133443
Slc2a13 solute carrier family 2 (facilitated glucose transporter), member 13 0.493109516
A730017C20Rik RIKEN cDNA A730017C20 gene 0.492521975
4933406I18Rik RIKEN cDNA 4933406I18 gene 0.491512496
Csf1r colony stimulating factor 1 receptor 0.490528886
Gdpd5 glycerophosphodiester phosphodiesterase domain containing 5 0.489072415
Sytl1 synaptotagmin-like 1 0.48833057
Gpr39 G protein-coupled receptor 39 0.488053091
Sdc4 syndecan 4 0.488053091
Cck cholecystokinin 0.487917793
Arl4c ADP-ribosylation factor-like 4C 0.487721677
Prickle2 prickle homolog 2 (Drosophila) 0.487137179
Trib3 tribbles homolog 3 (Drosophila) 0.486202764
Echdc3 enoyl Coenzyme A hydratase domain containing 3 0.485687412
Kcnd2 potassium voltage-gated channel, Shal-related family, member 2 0.485468637
Rbfox1 RNA binding protein, fox-1 homolog (C. elegans) 1 0.485098627
Kcnt1 potassium channel, subfamily T, member 1 0.484023836
Olfml3 olfactomedin-like 3 0.482636858
Fam189a2 family with sequence similarity 189, member A2 0.482456241
Inha inhibin alpha 0.482402738
Pgr progesterone receptor 0.481130446
C77370 expressed sequence C77370 0.48083706
Tmprss6 transmembrane serine protease 6 0.479814945
Chac1 ChaC, cation transport regulator 1 0.479050615
Mgam maltase-glucoamylase 0.479043974
Gpr27 G protein-coupled receptor 27 0.478791684
Pon3 paraoxonase 3 0.478585967
Kcnip4 Kv channel interacting protein 4 0.478231147
Pkd2l1 polycystic kidney disease 2-like 1 0.477717622
Wif1 Wnt inhibitory factor 1 0.474944223
Gm5458 predicted gene 5458 0.474835597
D630032N06Rik amyloid beta precursor protein (cytoplasmic tail) binding protein 2, opposite strand 0.47430928
Cpn1 carboxypeptidase N, polypeptide 1 0.47341916
Cyp4f39 cytochrome P450, family 4, subfamily f, polypeptide 39 0.472645361
Mfhas1 malignant fibrous histiocytoma amplified sequence 1 0.472347327
Cgref1 cell growth regulator with EF hand domain 1 0.472065842
Xlr3c X-linked lymphocyte-regulated 3C 0.472042938
1500009C09Rik RIKEN cDNA 1500009C09 gene 0.471941518
Tmeff1 transmembrane protein with EGF-like and two follistatin-like domains 1 0.471470693
Dok6 docking protein 6 0.471196262
Fxyd2 FXYD domain-containing ion transport regulator 2 0.468382799
Fbln5 fibulin 5 0.468123144
Rbms1 RNA binding motif, single stranded interacting protein 1 0.468038788
Spon2 spondin 2, extracellular matrix protein 0.467993371
Hist1h4h histone cluster 1, H4h 0.467095678
Acsl6 acyl-CoA synthetase long-chain family member 6 0.46671379
Fam184b family with sequence similarity 184, member B 0.465231278
Fbp1 fructose bisphosphatase 1 0.464792921
Epha5 Eph receptor A5 0.463326145
Lrp3 low density lipoprotein receptor-related protein 3 0.462940922
Lsamp limbic system-associated membrane protein 0.462187453
Fa2h fatty acid 2-hydroxylase 0.461448004
4833424O15Rik lipid phosphate phosphatase-related protein type 5 0.46017994
Gm16197 tumor protein p53 pathway corepressor 1 0.460135286
Amdhd2 amidohydrolase domain containing 2 0.459810081
Gap43 growth associated protein 43 0.458886732
Apof apolipoprotein F 0.458517912
Stc2 stanniocalcin 2 0.457949367
Ncan neurocan 0.457108959
Pgr15l G protein-coupled receptor 15-like 0.455173982
Rgs20 regulator of G-protein signaling 20 0.454650549
Moxd1 monooxygenase, DBH-like 1 0.45462849
Pmepa1 prostate transmembrane protein, androgen induced 1 0.45342945
Chrd chordin 0.452735393
Tspyl3 TSPY-like 3 0.452663222
Cacng3 calcium channel, voltage-dependent, gamma subunit 3 0.452390331
Creb3l3 cAMP responsive element binding protein 3-like 3 0.451444331
Gsg1l GSG1-like 0.450681458
Btbd11 BTB (POZ) domain containing 11 0.450531536
Prkd3 protein kinase D3 0.4500228
Sorcs1 sortilin-related VPS10 domain containing receptor 1 0.449598772
Hs3st5 heparan sulfate (glucosamine) 3-O-sulfotransferase 5 0.448571541
Kcp kielin/chordin-like protein 0.448419214
1700003M07Rik RIKEN cDNA 1700003M07 gene 0.448211012
Rassf10 Ras association (RalGDS/AF-6) domain family (N-terminal) member 10 0.447338862
Serpina1a serine (or cysteine) peptidase inhibitor, clade A, member 1A 0.447131162
Tpd52l1 tumor protein D52-like 1 0.445979716
Gpr125 adhesion G protein-coupled receptor A2 0.445707765
Chrna7 cholinergic receptor, nicotinic, alpha polypeptide 7 0.445272371
Sox12 SRY (sex determining region Y)-box 12 0.445201389
Eif4ebp1 eukaryotic translation initiation factor 4E binding protein 1 0.445053291
Serpina3n serine (or cysteine) peptidase inhibitor, clade A, member 3N 0.44452609
Ptpla 3-hydroxyacyl-CoA dehydratase 1 0.443642659
Tox thymocyte selection-associated high mobility group box 0.443461265
Matn2 matrilin 2 0.442632104
Serpina10 serine (or cysteine) peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 10 0.442524734
Ttr transthyretin 0.441082369
Rprm reprimo, TP53 dependent G2 arrest mediator candidate 0.440569033
Sod3 superoxide dismutase 3, extracellular 0.440059345
Pdyn prodynorphin 0.438910899
Spp2 secreted phosphoprotein 2 0.436683523
Wdr64 WD repeat domain 64 0.436256944
Cntnap2 contactin associated protein-like 2 0.433802418
4931408A02Rik eva-1 homolog C (C. elegans) 0.433757317
2900011O08Rik RIKEN cDNA 2900011O08 gene 0.433141407
Spry1 sprouty homolog 1 (Drosophila) 0.433108382
Atf5 activating transcription factor 5 0.432373497
Scrt2 scratch homolog 2, zinc finger protein (Drosophila) 0.429872284
Pax2 paired box 2 0.429377947
Ror2 receptor tyrosine kinase-like orphan receptor 2 0.427581103
Tgfb2 transforming growth factor, beta 2 0.42699468
Rbp7 retinol binding protein 7, cellular 0.425788828
Serpina1e serine (or cysteine) peptidase inhibitor, clade A, member 1E 0.423974676
Clybl citrate lyase beta like 0.423962921
Slc2a6 solute carrier family 2 (facilitated glucose transporter), member 6 0.423390264
9430083A17Rik RIKEN cDNA 9430083A17 gene 0.422882862
Btbd16 BTB (POZ) domain containing 16 0.422806658
Col11a1 collagen, type XI, alpha 1 0.422507835
Gabrg2 gamma-aminobutyric acid [205] A receptor, subunit gamma 2 0.422378996
Slc6a9 solute carrier family 6 (neurotransmitter transporter, glycine), member 9 0.422115585
Wnt4 wingless-type MMTV integration site family, member 4 0.420984786
Phlda3 pleckstrin homology-like domain, family A, member 3 0.420704746
9530008L14Rik androgen dependent TFPI regulating protein 0.419185291
Ano3 anoctamin 3 0.419042942
Lass4 ceramide synthase 4 0.418630694
Opn3 opsin 3 0.417981212
Ms4a10 membrane-spanning 4-domains, subfamily A, member 10 0.416691045
Abcb1a ATP-binding cassette, sub-family B (MDR/TAP), member 1A 0.4144299
Adcy8 adenylate cyclase 8 0.413910284
Metrnl meteorin, glial cell differentiation regulator-like 0.413726708
Tnfrsf21 tumor necrosis factor receptor superfamily, member 21 0.413680827
Plcxd1 phosphatidylinositol-specific phospholipase C, X domain containing 1 0.410302543
Hapln1 hyaluronan and proteoglycan link protein 1 0.40804778
A630023A22Rik RIKEN cDNA A630023A22 gene 0.407157815
Clec2l C-type lectin domain family 2, member L 0.406483867
Alpl alkaline phosphatase, liver/bone/kidney 0.406236
Lama4 laminin, alpha 4 0.405310653
Ugt3a2 UDP glycosyltransferases 3 family, polypeptide A2 0.405024196
Gm12409 predicted gene 12409 0.401299148
Ptprk protein tyrosine phosphatase, receptor type, K 0.401015526
Ltb lymphotoxin B 0.399966202
Bend4 BEN domain containing 4 0.398331092
2200002K05Rik V-set and transmembrane domain containing 5 0.398173745
Sept4 septin 4 0.396469008
Mgat4a mannoside acetylglucosaminyltransferase 4, isoenzyme A 0.395944466
Impa2 inositol (myo)-1(or 4)-monophosphatase 2 0.395387728
Ntng2 netrin G2 0.394872828
Ngfr nerve growth factor receptor (TNFR superfamily, member 16) 0.393618518
Aard alanine and arginine rich domain containing protein 0.391346921
Nxf7 nuclear RNA export factor 7 0.390829155
Dpep1 dipeptidase 1 (renal) 0.389855124
Pcsk6 proprotein convertase subtilisin/kexin type 6 0.389193629
Stx11 syntaxin 11 0.388097219
3632451O06Rik RIKEN cDNA 3632451O06 gene 0.387935847
Anxa10 annexin A10 0.386387412
B4galnt4 beta-1,4-N-acetyl-galactosaminyl transferase 4 0.385525983
Neurl3 neuralized E3 ubiquitin protein ligase 3 0.385042607
Adm2 adrenomedullin 2 0.384258745
Jdp2 Jun dimerization protein 2 0.383851449
Ephb6 Eph receptor B6 0.383032841
Gm266 predicted gene 266 0.382454493
Slc1a1 solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 1 0.37803193
Hsd17b13 hydroxysteroid (17-beta) dehydrogenase 13 0.374238529
Pon2 paraoxonase 2 0.373826306
Gm9767 predicted gene 9767 0.371473191
Reep1 receptor accessory protein 1 0.370098194
Sgk1 serum/glucocorticoid regulated kinase 1 0.370028937
C87198 expressed sequence C87198 0.368031205
Ephb2 Eph receptor B2 0.367585051
Smyd3 SET and MYND domain containing 3 0.367320164
Tmem35 transmembrane protein 35 0.366887587
Map6d1 MAP6 domain containing 1 0.365035834
Lats2 large tumor suppressor 2 0.364085704
Lor loricrin 0.358585734
Rbm11 RNA binding motif protein 11 0.357161876
Oit1 oncoprotein induced transcript 1 0.357003469
Lrfn5 leucine rich repeat and fibronectin type III domain containing 5 0.356328554
Fgf21 fibroblast growth factor 21 0.356108802
Cntnap4 contactin associated protein-like 4 0.352325304
Lta lymphotoxin A 0.352300884
Fcer2a Fc receptor, IgE, low affinity II, alpha polypeptide 0.35200553
Pik3r5 phosphoinositide-3-kinase, regulatory subunit 5, p101 0.351771376
Igfbp7 insulin-like growth factor binding protein 7 0.351678733
St6galnac3 ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N-acetylgalactosaminide alpha-2,6-sialyltransferase 3 0.350396435
Mdfic MyoD family inhibitor domain containing 0.347474738
Twist2 twist basic helix-loop-helix transcription factor 2 0.345151103
Atp8a2 ATPase, aminophospholipid transporter-like, class I, type 8A, member 2 0.345045853
Grid2 glutamate receptor, ionotropic, delta 2 0.344730296
Nupr1 nuclear protein transcription regulator 1 0.343790122
St7 suppression of tumorigenicity 7 0.341103154
Col5a3 collagen, type V, alpha 3 0.340640057
Serpine2 serine (or cysteine) peptidase inhibitor, clade E, member 2 0.340022
Nrn1 neuritin 1 0.339807594
Matn1 matrilin 1, cartilage matrix protein 0.338057369
Cldn14 claudin 14 0.335131686
2310007L24Rik proline rich 29 0.334416978
Nptx2 neuronal pentraxin 2 0.332913666
Camk1d calcium/calmodulin-dependent protein kinase ID 0.330604897
Spred1 sprouty protein with EVH-1 domain 1, related sequence 0.329723816
Chsy3 chondroitin sulfate synthase 3 0.328963631
Timp2 tissue inhibitor of metalloproteinase 2 0.325524944
Sdc3 syndecan 3 0.325292621
Tex13 testis expressed gene 13 0.322352259
Kcna4 potassium voltage-gated channel, shaker-related subfamily, member 4 0.321377314
Nhlrc1 NHL repeat containing 1 0.32113237
Srgap3 SLIT-ROBO Rho GTPase activating protein 3 0.320107859
P4ha2 procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha II polypeptide 0.316579557
Asb4 ankyrin repeat and SOCS box-containing 4 0.315536761
Ccnb1ip1 cyclin B1 interacting protein 1 0.314669663
Gm5148 predicted gene 5148 0.31390937
Rin2 Ras and Rab interactor 2 0.312897058
Samd5 sterile alpha motif domain containing 5 0.309111664
C3 complement component 3 0.30745134
Extl1 exostoses (multiple)-like 1 0.306916901
Plcg2 phospholipase C, gamma 2 0.306345168
Marveld1 MARVEL (membrane-associating) domain containing 1 0.305647358
Thnsl2 threonine synthase-like 2 (bacterial) 0.304075187
Ank1 ankyrin 1, erythroid 0.300809076
Lyzl4 lysozyme-like 4 0.299504586
Slc26a4 solute carrier family 26, member 4 0.299454766
Galnt5 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 5 0.298408389
Nlgn1 neuroligin 1 0.29794749
Hebp1 heme binding protein 1 0.29691667
Clstn2 calsyntenin 2 0.296330699
Qrich2 glutamine rich 2 0.296324537
Dennd2c DENN/MADD domain containing 2C 0.295180638
Neto1 neuropilin (NRP) and tolloid (TLL)-like 1 0.293062443
Vegfc vascular endothelial growth factor C 0.290800166
Fibcd1 fibrinogen C domain containing 1 0.289488913
Dync1i1 dynein cytoplasmic 1 intermediate chain 1 0.28774046
Emp1 epithelial membrane protein 1 0.286798623
Lrmp lymphoid-restricted membrane protein 0.286067994
Gpr135 G protein-coupled receptor 135 0.285072346
Aldh1l1 aldehyde dehydrogenase 1 family, member L1 0.281432335
Accn1 acid-sensing (proton-gated) ion channel 2 0.278307173
Rrad Ras-related associated with diabetes 0.276338791
Pros1 protein S (alpha) 0.275167121
Tpbg trophoblast glycoprotein 0.275117535
Adarb2 adenosine deaminase, RNA-specific, B2 0.273601871
Mrc2 mannose receptor, C type 2 0.272464467
Srpk3 serine/arginine-rich protein specific kinase 3 0.270663077
Gnai1 guanine nucleotide binding protein (G protein), alpha inhibiting 1 0.267592577
Slit1 slit homolog 1 (Drosophila) 0.265900796
Stmn2 stathmin-like 2 0.265331892
Ptprg protein tyrosine phosphatase, receptor type, G 0.264131817
Rasgef1c RasGEF domain family, member 1C 0.262541974
Sct secretin 0.262174632
Ccl20 chemokine (C-C motif) ligand 20 0.261951204
Gfra1 glial cell line derived neurotrophic factor family receptor alpha 1 0.259376558
Kcnab1 potassium voltage-gated channel, shaker-related subfamily, beta member 1 0.25893825
Olfr5 olfactory receptor 5 0.258226685
Lbp lipopolysaccharide binding protein 0.257922583
Cd55 CD55 molecule, decay accelerating factor for complement 0.257797468
Ppargc1a peroxisome proliferative activated receptor, gamma, coactivator 1 alpha 0.251646424
Ncam2 neural cell adhesion molecule 2 0.251583637
Diras2 DIRAS family, GTP-binding RAS-like 2 0.246448824
Gm2694 predicted gene 2694 0.246083529
Slitrk5 SLIT and NTRK-like family, member 5 0.24433628
5033411D12Rik succinyl-CoA glutarate-CoA transferase 0.243659774
Ntn1 netrin 1 0.242154547
Ndst4 N-deacetylase/N-sulfotransferase (heparin glucosaminyl) 4 0.240441862
Pdzrn3 PDZ domain containing RING finger 3 0.239420734
Arrb1 arrestin, beta 1 0.239312888
Ankrd55 ankyrin repeat domain 55 0.236663644
Ppfibp2 PTPRF interacting protein, binding protein 2 (liprin beta 2) 0.236489822
Vnn3 vanin 3 0.236059099
Csf2ra colony stimulating factor 2 receptor, alpha, low-affinity (granulocyte-macrophage) 0.235650394
Cdh23 cadherin 23 (otocadherin) 0.234196269
Shh sonic hedgehog 0.233030387
Slc22a4 solute carrier family 22 (organic cation transporter), member 4 0.231516994
Rtn1 reticulon 1 0.231040872
Tlr4 toll-like receptor 4 0.228888994
Efcab6 EF-hand calcium binding domain 6 0.227533362
Mgmt O-6-methylguanine-DNA methyltransferase 0.225198637
Apba2 amyloid beta (A4) precursor protein-binding, family A, member 2 0.224514418
Tmc1 transmembrane channel-like gene family 1 0.224319975
Gm11747 predicted gene 11747 0.220312003
Slc2a3 solute carrier family 2 (facilitated glucose transporter), member 3 0.218160225
C1qtnf1 C1q and tumor necrosis factor related protein 1 0.217230713
Hpx hemopexin 0.213534339
Enpep glutamyl aminopeptidase 0.212767544
Ugt1a9 UDP glucuronosyltransferase 1 family, polypeptide A9 0.212466899
Hist2h3c2 histone cluster 2, H3c2 0.209087685
Cyb5r2 cytochrome b5 reductase 2 0.205178291
Syt17 synaptotagmin XVII 0.203363124
Cartpt CART prepropeptide 0.199434928
Cxcl1 chemokine (C-X-C motif) ligand 1 0.198065567
Basp1 brain abundant, membrane attached signal protein 1 0.198053211
Dlgap2 discs, large (Drosophila) homolog-associated protein 2 0.195493155
Ngf nerve growth factor 0.194590071
Egfl6 EGF-like-domain, multiple 6 0.194556354
Dapl1 death associated protein-like 1 0.193472443
Clec2d C-type lectin domain family 2, member d 0.190972922
Gm1078 SH3 domain binding kinase family, member 3 0.190153975
Foxp2 forkhead box P2 0.188326771
Tceal5 transcription elongation factor A (SII)-like 5 0.18412788
Galnt13 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13 0.183760679
Parm1 prostate androgen-regulated mucin-like protein 1 0.183429807
C1rl complement component 1, r subcomponent-like 0.180918269
Nmnat2 nicotinamide nucleotide adenylyltransferase 2 0.176579528
Ptpn14 protein tyrosine phosphatase, non-receptor type 14 0.17610283
Maf avian musculoaponeurotic fibrosarcoma (v-maf) AS42 oncogene homolog 0.171884338
Serpina3i serine (or cysteine) peptidase inhibitor, clade A, member 3I 0.168474249
Scn1a sodium channel, voltage-gated, type I, alpha 0.1674184
Olfr1349 olfactory receptor 1349 0.166193977
Itih1 inter-alpha trypsin inhibitor, heavy chain 1 0.163363773
Kcnmb4 potassium large conductance calcium-activated channel, subfamily M, beta member 4 0.16256854
Pde5a phosphodiesterase 5A, cGMP-specific 0.162434501
Tbx3 T-box 3 0.156079179
Cbln1 cerebellin 1 precursor protein 0.153776825
Bhlhe22 basic helix-loop-helix family, member e22 0.148392491
Nov nephroblastoma overexpressed gene 0.14473944
Tmem45a transmembrane protein 45a 0.144715364
Rab27b RAB27B, member RAS oncogene family 0.143085561
Usp13 ubiquitin specific peptidase 13 (isopeptidase T-3) 0.142237128
Slitrk3 SLIT and NTRK-like family, member 3 0.140822523
Gabra3 gamma-aminobutyric acid [205] A receptor, subunit alpha 3 0.139113082
1700040L02Rik RIKEN cDNA 1700040L02 gene 0.13895407
Fnbp1l formin binding protein 1-like 0.134583178
Mal2 mal, T cell differentiation protein 2 0.134264518
Trpc6 transient receptor potential cation channel, subfamily C, member 6 0.132430745
Slc6a5 solute carrier family 6 (neurotransmitter transporter, glycine), member 5 0.128878186
Cdh10 cadherin 10 0.126992922
Tceal6 transcription elongation factor A (SII)-like 6 0.125285382
Car2 carbonic anhydrase 2 0.124819912
Syk spleen tyrosine kinase 0.121910982
Slc22a21 solute carrier family 22 (organic cation transporter), member 21 0.118594581
Klhl13 kelch-like 13 0.117206948
Htra1 HtrA serine peptidase 1 0.116332008
1700058G18Rik potassium large conductance calcium-activated channel, subfamily M, beta member 4, opposite strand 1 0.113522487
Zcchc3 zinc finger, CCHC domain containing 3 0.112879859
Gng3 guanine nucleotide binding protein (G protein), gamma 3 0.110592848
Epdr1 ependymin related protein 1 (zebrafish) 0.109393582
A830018L16Rik RIKEN cDNA A830018L16 gene 0.107526935
Cadm3 cell adhesion molecule 3 0.104908974
Lrch2 leucine-rich repeats and calponin homology (CH) domain containing 2 0.102882535
Evc Ellis van Creveld gene syndrome 0.100611701
Morc1 microrchidia 1 0.0985958
Tac1 tachykinin 1 0.096133295
Kit kit oncogene 0.088750558
Evc2 Ellis van Creveld syndrome 2 0.076413428
Hecw2 HECT, C2 and WW domain containing E3 ubiquitin protein ligase 2 0.074567459
Tram1l1 translocation associated membrane protein 1-like 1 0.06836634
2810011L19Rik Tcl1 upstream neural differentiation associated RNA 0.066992342
Luzp2 leucine zipper protein 2 0.056050071
Olfm3 olfactomedin 3 0.051639541
Tshz2 teashirt zinc finger family member 2 0.04888718
Mef2c myocyte enhancer factor 2C 0.040850569
Thbs1 thrombospondin 1 0.031492672
Cntn5 contactin 5 0.018557421
Mirlet7c-2 microRNA let7c-2 0
Snord89 small nucleolar RNA, C/D box 89 0

B.

Table of functional enrichment analysis of differently expressed gene in miR-30d KO line

#Category Term Count % P value Fold Enrichment Bonferroni Benjamini FDR
SP_PIR_KEYWORDS membrane 500 35.49 5.5904E-13 1.299942087 2.7335E-10 6.83374E-11 8.02458E-10
GOTERM_CC_FAT GO:0031224~intrinsic to membrane 450 31.94 0.019956867 1.072646299 0.999411868 0.226246563 24.26064199
SP_PIR_KEYWORDS transmembrane 412 29.24 0.002030492 1.126376858 0.629877066 0.04056752 2.875652529
SP_PIR_KEYWORDS glycoprotein 405 28.74 1.29332E-26 1.610725742 6.32431E-24 6.32431E-24 1.8566E-23
UP_SEQ_FEATURE glycosylation site:N-linked (GlcNAc…) 387 27.47 1.98913E-20 1.51283123 5.77842E-17 5.77842E-17 3.56893E-17
UP_SEQ_FEATURE transmembrane region 386 27.40 4.62671E-08 1.263488386 0.000134397 2.24007E-05 8.30133E-05
SP_PIR_KEYWORDS signal 328 23.28 1.5675E-19 1.581198783 7.66509E-17 3.83254E-17 2.25021E-16
UP_SEQ_FEATURE signal peptide 328 23.28 1.41416E-15 1.490338262 4.19276E-12 2.09632E-12 2.58682E-12
GOTERM_MF_FAT GO:0043167~ion binding 307 21.79 0.005222163 1.130822142 0.989432269 0.194799764 7.803105021
GOTERM_MF_FAT GO:0043169~cation binding 296 21.01 0.022487987 1.104055668 0.999999997 0.397578859 29.73705373
GOTERM_MF_FAT GO:0046872~metal ion binding 294 20.87 0.020878538 1.106564885 0.999999989 0.399096153 27.92035923
UP_SEQ_FEATURE topological domain:Cytoplasmic 288 20.44 4.33091E-10 1.394730669 1.25813E-06 3.14532E-07 7.7706E-07
GOTERM_CC_FAT GO:0005886~plasma membrane 269 19.09 3.77131E-07 1.304914375 0.000139152 4.6386E-05 0.000519845
SP_PIR_KEYWORDS disulfide bond 265 18.81 1.11614E-13 1.536716877 5.45614E-11 1.81871E-11 1.60172E-10
UP_SEQ_FEATURE disulfide bond 253 17.96 6.16143E-10 1.431755098 1.78989E-06 3.57979E-07 1.1055E-06
UP_SEQ_FEATURE topological domain:Extracellular 237 16.82 2.60387E-10 1.467680301 7.56422E-07 2.52141E-07 4.67191E-07
GOTERM_CC_FAT GO:0005576~extracellular region 190 13.48 1.21468E-11 1.594298599 4.48218E-09 4.48218E-09 1.67435E-08
SP_PIR_KEYWORDS Secreted 163 11.57 1.43606E-10 1.64349481 7.02232E-08 1.40446E-08 2.06151E-07
GOTERM_CC_FAT GO:0044459~plasma membrane part 160 11.36 1.10762E-05 1.381208184 0.004078807 0.000817096 0.015266644
SP_PIR_KEYWORDS cell membrane 153 10.86 0.000959065 1.278801523 0.374502324 0.025730279 1.367990105
SP_PIR_KEYWORDS transport 134 9.51 0.010206005 1.221230635 0.993371513 0.145092356 13.69336697
GOTERM_CC_FAT GO:0044421~extracellular region part 103 7.31 3.92575E-10 1.875951513 1.4486E-07 7.24301E-08 5.41135E-07
GOTERM_MF_FAT GO:0005509~calcium ion binding 95 6.74 1.48853E-06 1.638832632 0.001292701 0.000323332 0.002309709
GOTERM_BP_FAT GO:0007242~intracellular signaling cascade 95 6.74 4.89843E-05 1.50563069 0.136160886 0.009710462 0.088128692
SP_PIR_KEYWORDS calcium 84 5.96 6.97341E-06 1.64524654 0.003404202 0.00042616 0.01001012
GOTERM_BP_FAT GO:0006811~ion transport 83 5.89 2.42856E-06 1.690495605 0.007230288 0.001208694 0.004371005
GOTERM_BP_FAT GO:0007155~cell adhesion 72 5.11 3.8314E-07 1.861168024 0.001144167 0.000381535 0.0006896
GOTERM_BP_FAT GO:0022610~biological adhesion 72 5.11 4.07803E-07 1.857856337 0.001217772 0.000304582 0.000733989
UP_SEQ_FEATURE mutagenesis site 69 4.90 0.02556917 1.284853028 1 0.938384117 37.16979563
GOTERM_BP_FAT GO:0042127~regulation of cell proliferation 67 4.76 2.95497E-06 1.805961445 0.008790601 0.001260557 0.005318429
GOTERM_MF_FAT GO:0003700~transcription factor activity 67 4.76 0.043134167 1.251132672 1 0.542491477 49.54909588
GOTERM_CC_FAT GO:0005615~extracellular space 64 4.54 1.00657E-05 1.765567879 0.003707359 0.000928131 0.013873857
SP_PIR_KEYWORDS ion transport 64 4.54 4.67673E-05 1.687521137 0.022610216 0.001903997 0.067115226
GOTERM_BP_FAT GO:0006812~cation transport 63 4.47 1.08564E-05 1.77398224 0.031918717 0.004046693 0.019538377
GOTERM_BP_FAT GO:0030001~metal ion transport 59 4.19 1.43235E-06 1.935734051 0.004270714 0.000855606 0.002578008
GOTERM_CC_FAT GO:0030054~cell junction 59 4.19 2.1924E-05 1.769617885 0.008057401 0.001347431 0.030216301
GOTERM_BP_FAT GO:0007049~cell cycle 59 4.19 0.007812757 1.400318249 1 0.212699118 13.16597352
GOTERM_CC_FAT GO:0031226~intrinsic to plasma membrane 58 4.12 0.002528612 1.481201902 0.607115498 0.060382724 3.429718258
GOTERM_BP_FAT GO:0042592~homeostatic process 57 4.05 0.007240597 1.415395974 1 0.208226304 12.26027218
GOTERM_CC_FAT GO:0042995~cell projection 57 4.05 0.00911327 1.397437381 0.965891659 0.13130126 11.85573859
GOTERM_CC_FAT GO:0005887~integral to plasma membrane 56 3.97 0.00280153 1.486684629 0.644850147 0.06265228 3.793297108
GOTERM_BP_FAT GO:0043067~regulation of programmed cell death 56 3.97 0.004785632 1.450160085 0.999999404 0.162187055 8.271943889
GOTERM_BP_FAT GO:0010941~regulation of cell death 56 3.97 0.005337415 1.442432767 0.999999886 0.173345097 9.183009011
SP_PIR_KEYWORDS lipoprotein 56 3.97 0.016582445 1.361262276 0.9997189 0.193602882 21.34058885
SP_PIR_KEYWORDS cell junction 54 3.83 2.68012E-06 1.972317235 0.001309721 0.000187208 0.003847339
GOTERM_BP_FAT GO:0006955~immune response 54 3.83 0.00027018 1.66260392 0.553987447 0.030576933 0.485174428
GOTERM_MF_FAT GO:0022838~substrate specific channel activity 53 3.76 2.68913E-07 2.133357567 0.000233658 0.000116836 0.000417267
GOTERM_MF_FAT GO:0015267~channel activity 53 3.76 4.19245E-07 2.104133491 0.000364257 0.000121434 0.000650533
GOTERM_MF_FAT GO:0022803~passive transmembrane transporter activity 53 3.76 4.19245E-07 2.104133491 0.000364257 0.000121434 0.000650533
GOTERM_BP_FAT GO:0042981~regulation of apoptosis 53 3.76 0.013269904 1.389846013 1 0.260952584 21.37180332
GOTERM_MF_FAT GO:0005216~ion channel activity 52 3.69 2.43557E-07 2.159077345 0.000211629 0.000211629 0.000377923
GOTERM_MF_FAT GO:0043565~sequence-specific DNA binding 52 3.69 0.021849267 1.35524819 0.999999995 0.396613702 29.02127579
GOTERM_BP_FAT GO:0007610~behavior 51 3.62 4.01531E-05 1.826127515 0.113061627 0.009186755 0.072245626
SP_PIR_KEYWORDS cell adhesion 50 3.55 2.28842E-05 1.883889756 0.011128143 0.001016803 0.032846159
GOTERM_BP_FAT GO:0055085~transmembrane transport 50 3.55 0.001511682 1.576260962 0.989114512 0.086440248 2.686153399
GOTERM_BP_FAT GO:0009611~response to wounding 47 3.34 1.35368E-05 1.964193776 0.039641194 0.004484172 0.024361717
SP_PIR_KEYWORDS ionic channel 46 3.26 1.00329E-06 2.188065976 0.000490489 8.17649E-05 0.001440251
INTERPRO IPR013032:EGF-like region, conserved site 45 3.19 2.684E-06 2.134519868 0.004314596 0.00215963 0.004497972
SP_PIR_KEYWORDS Signal-anchor 45 3.19 0.002337963 1.590840239 0.681649539 0.043068215 3.304335055
GOTERM_BP_FAT GO:0006952~defense response 45 3.19 0.01097918 1.456634014 1 0.240346917 18.02072646
GOTERM_CC_FAT GO:0045202~synapse 44 3.12 3.26616E-05 1.944407728 0.011979979 0.001720278 0.045012091
GOTERM_MF_FAT GO:0022836~gated channel activity 43 3.05 1.55084E-06 2.217442767 0.001346771 0.0002695 0.002406382
GOTERM_CC_FAT GO:0031012~extracellular matrix 43 3.05 3.29747E-05 1.961712328 0.012094142 0.001519828 0.045443554
GOTERM_BP_FAT GO:0006928~cell motion 43 3.05 0.000806477 1.699097648 0.910249817 0.064771682 1.441646167
GOTERM_MF_FAT GO:0046873~metal ion transmembrane transporter activity 41 2.91 1.95846E-05 2.048689505 0.016875139 0.002125132 0.030384705
GOTERM_BP_FAT GO:0007267~cell-cell signaling 40 2.84 4.39154E-05 2.000220807 0.122977085 0.009329222 0.079012532
GOTERM_CC_FAT GO:0005578~proteinaceous extracellular matrix 40 2.84 0.000132462 1.898579937 0.047706279 0.004876248 0.18243442
GOTERM_BP_FAT GO:0044093~positive regulation of molecular function 40 2.84 0.000141584 1.895634099 0.344976136 0.019945247 0.254526703
GOTERM_CC_FAT GO:0009986~cell surface 40 2.84 0.00023188 1.848781119 0.082014442 0.007105769 0.319155498
GOTERM_BP_FAT GO:0032989~cellular component morphogenesis 40 2.84 0.002160012 1.652604086 0.998436919 0.096025954 3.817181452
GOTERM_BP_FAT GO:0048878~chemical homeostasis 40 2.84 0.004172709 1.589216532 0.999996251 0.151594515 7.249811674
GOTERM_BP_FAT GO:0030182~neuron differentiation 40 2.84 0.017024219 1.453794572 1 0.289681832 26.58573724
GOTERM_MF_FAT GO:0005261~cation channel activity 39 2.77 2.64353E-06 2.278784254 0.002294594 0.000382799 0.004101843
GOTERM_BP_FAT GO:0008284~positive regulation of cell proliferation 39 2.77 6.11612E-05 1.991417019 0.167028109 0.010692738 0.110025052
GOTERM_BP_FAT GO:0000902~cell morphogenesis 38 2.70 0.000704752 1.78336839 0.878341926 0.061839814 1.260891833
GOTERM_MF_FAT GO:0046983~protein dimerization activity 38 2.70 0.003836096 1.619552248 0.964562252 0.169356283 5.789496831
GOTERM_BP_FAT GO:0019725~cellular homeostasis 38 2.70 0.004437043 1.606591348 0.999998304 0.156631342 7.691936954
GOTERM_BP_FAT GO:0015672~monovalent inorganic cation transport 37 2.63 0.000953716 1.770822547 0.942217099 0.070495995 1.702720015
GOTERM_BP_FAT GO:0030030~cell projection organization 37 2.63 0.00235347 1.682003861 0.999124274 0.101180993 4.152262189
GOTERM_MF_FAT GO:0008289~lipid binding 37 2.63 0.008379863 1.554078359 0.999333043 0.253613241 12.24109011
SP_PIR_KEYWORDS transmembrane protein 37 2.63 0.027227536 1.431756215 0.999998628 0.240797983 32.71824001
GOTERM_BP_FAT GO:0010647~positive regulation of cell communication 36 2.56 6.39714E-08 2.762209686 0.000191128 0.000191128 0.00011514
SP_PIR_KEYWORDS cleavage on pair of basic residues 36 2.56 2.22229E-05 2.16568167 0.010808282 0.001086121 0.03189705
GOTERM_MF_FAT GO:0004857~enzyme inhibitor activity 36 2.56 2.55567E-05 2.14677491 0.02196427 0.002464633 0.039648578
GOTERM_BP_FAT GO:0060429~epithelium development 36 2.56 0.000238873 1.926411922 0.51023856 0.028149677 0.429067903
INTERPRO IPR006210:EGF-like 35 2.48 8.87659E-07 2.535253483 0.001428997 0.001428997 0.001487601
SP_PIR_KEYWORDS egf-like domain 35 2.48 1.21509E-05 2.257273312 0.005924193 0.000659983 0.017441602
SMART SM00181:EGF 35 2.48 2.33695E-05 2.165488782 0.007032817 0.007032817 0.031255799
GOTERM_BP_FAT GO:0001775~cell activation 35 2.48 7.66609E-05 2.063235894 0.20472833 0.011984008 0.137889722
GOTERM_BP_FAT GO:0051674~localization of cell 35 2.48 0.001142795 1.787169119 0.967176979 0.074712308 2.037025271
GOTERM_BP_FAT GO:0048870~cell motility 35 2.48 0.001142795 1.787169119 0.967176979 0.074712308 2.037025271
GOTERM_BP_FAT GO:0050801~ion homeostasis 35 2.48 0.001944662 1.73227314 0.997021414 0.088188798 3.442881529
GOTERM_BP_FAT GO:0003006~reproductive developmental process 34 2.41 0.000637094 1.867630413 0.851064809 0.057771652 1.140498257
GOTERM_BP_FAT GO:0048666~neuron development 34 2.41 0.003430729 1.688542565 0.999965296 0.129567826 5.998064029
GOTERM_BP_FAT GO:0009967~positive regulation of signal transduction 33 2.34 2.09119E-07 2.782283885 0.000624651 0.000312374 0.000376386
GOTERM_BP_FAT GO:0055082~cellular chemical homeostasis 33 2.34 0.001648408 1.785644881 0.992769937 0.08572823 2.925715077
GOTERM_BP_FAT GO:0002684~positive regulation of immune system process 32 2.27 3.07549E-05 2.252675861 0.087800851 0.007628831 0.055340276
GOTERM_BP_FAT GO:0051094~positive regulation of developmental process 32 2.27 6.46665E-05 2.168463679 0.175707413 0.010677571 0.116327303
SP_PIR_KEYWORDS synapse 32 2.27 7.80465E-05 2.150995252 0.037447099 0.00293156 0.111980324
GOTERM_BP_FAT GO:0006954~inflammatory response 32 2.27 0.000165712 2.062449899 0.390544535 0.02042149 0.29784082
GOTERM_BP_FAT GO:0048729~tissue morphogenesis 32 2.27 0.000452033 1.949795073 0.741013691 0.044033749 0.810482177
GOTERM_BP_FAT GO:0008283~cell proliferation 32 2.27 0.000851028 1.878749908 0.921444872 0.066445122 1.520710342
GOTERM_MF_FAT GO:0042802~identical protein binding 32 2.27 0.009882965 1.598977174 0.999821517 0.273609338 14.28266541
INTERPRO IPR000742:EGF-like, type 3 31 2.20 3.19508E-05 2.290646111 0.05017122 0.01701149 0.053532267
GOTERM_CC_FAT GO:0044456~synapse part 31 2.20 0.000210185 2.061347344 0.074634388 0.007026684 0.289334868
GOTERM_BP_FAT GO:0016477~cell migration 31 2.20 0.001092781 1.873123444 0.961879962 0.076591254 1.94870129
GOTERM_BP_FAT GO:0006873~cellular ion homeostasis 31 2.20 0.003969337 1.722412362 0.9999931 0.146536985 6.908290455
GOTERM_BP_FAT GO:0035295~tube development 31 2.20 0.004726543 1.702839494 0.999999289 0.16218242 8.173869518
INTERPRO IPR003599:Immunoglobulin subtype 31 2.20 0.036155719 1.456353278 1 0.716984573 46.05215226
KEGG_PATHWAY mmu05200:Pathways in cancer 31 2.20 0.047059008 1.412066365 0.999593843 0.405827023 44.19305132
GOTERM_MF_FAT GO:0030246~carbohydrate binding 31 2.20 0.048276922 1.417074606 1 0.562599705 53.59629761
GOTERM_BP_FAT GO:0048584~positive regulation of response to stimulus 30 2.13 2.81594E-05 2.33896788 0.080698945 0.007620057 0.050671207
GOTERM_MF_FAT GO:0031420~alkali metal ion binding 30 2.13 0.000184609 2.110300579 0.148231001 0.013281012 0.286070928
SP_PIR_KEYWORDS extracellular matrix 30 2.13 0.000414869 2.016558049 0.183650571 0.012601971 0.593911928
GOTERM_CC_FAT GO:0043005~neuron projection 30 2.13 0.004438581 1.726157881 0.806307247 0.087158897 5.947650022
GOTERM_BP_FAT GO:0007423~sensory organ development 30 2.13 0.005920563 1.692793874 0.99999998 0.18260063 10.1365697
SP_PIR_KEYWORDS DNA binding 30 2.13 0.00757075 1.664832808 0.975673317 0.124290415 10.33542159
GOTERM_BP_FAT GO:0044271~nitrogen compound biosynthetic process 30 2.13 0.043717448 1.440556376 1 0.456597366 55.27220378
GOTERM_MF_FAT GO:0004866~endopeptidase inhibitor activity 29 2.06 4.76745E-06 2.610131607 0.004134357 0.000591672 0.007397321
GOTERM_MF_FAT GO:0030414~peptidase inhibitor activity 29 2.06 2.67117E-05 2.387677208 0.022945407 0.002318583 0.041440009
GOTERM_BP_FAT GO:0045321~leukocyte activation 29 2.06 0.001116347 1.920303309 0.964474591 0.076389151 1.990326921
GOTERM_BP_FAT GO:0019226~transmission of nerve impulse 29 2.06 0.001800577 1.860824888 0.995415041 0.087229454 3.191680715
KEGG_PATHWAY mmu04060:Cytokine-cytokine receptor interaction 29 2.06 0.003896757 1.748654897 0.46874215 0.118827222 4.614614448
GOTERM_BP_FAT GO:0043085~positive regulation of catalytic activity 29 2.06 0.013010381 1.611288984 1 0.261647769 20.99875322
GOTERM_BP_FAT GO:0016337~cell-cell adhesion 28 1.99 0.006461023 1.720528915 0.999999996 0.193621412 11.0118785
GOTERM_BP_FAT GO:0045597~positive regulation of cell differentiation 27 1.92 0.000161927 2.237389846 0.383611272 0.020818392 0.291046955
GOTERM_BP_FAT GO:0000904~cell morphogenesis involved in differentiation 27 1.92 0.002963983 1.846901996 0.999859392 0.11743513 5.202490044
GOTERM_BP_FAT GO:0031175~neuron projection development 27 1.92 0.004326214 1.796069831 0.999997635 0.154852196 7.506805738
KEGG_PATHWAY mmu04010:MAPK signaling pathway 27 1.92 0.034850743 1.49904209 0.996806214 0.357277466 34.89931789
GOTERM_BP_FAT GO:0006813~potassium ion transport 26 1.85 9.49068E-05 2.356510139 0.246928496 0.014079696 0.17068221
GOTERM_CC_FAT GO:0034702~ion channel complex 26 1.85 0.000119551 2.319752258 0.043157961 0.00488989 0.164666081
GOTERM_BP_FAT GO:0045596~negative regulation of cell differentiation 26 1.85 0.000716914 2.071657265 0.882686589 0.061081552 1.282518519
GOTERM_CC_FAT GO:0009897~external side of plasma membrane 26 1.85 0.005760851 1.77922746 0.881388366 0.09653606 7.654993537
GOTERM_BP_FAT GO:0007626~locomotory behavior 26 1.85 0.024076767 1.577580009 1 0.340401132 35.50951665
GOTERM_BP_FAT GO:0048568~embryonic organ development 26 1.85 0.026341023 1.564488059 1 0.35483786 38.15012464
GOTERM_BP_FAT GO:0043069~negative regulation of programmed cell death 26 1.85 0.03004002 1.54525255 1 0.381006158 42.24548118
GOTERM_BP_FAT GO:0060548~negative regulation of cell death 26 1.85 0.031356996 1.538945397 1 0.389354753 43.6407143
GOTERM_BP_FAT GO:0010627~regulation of protein kinase cascade 25 1.77 0.000150083 2.33896788 0.361403642 0.020179196 0.269786143
GOTERM_BP_FAT GO:0051240~positive regulation of multicellular organismal process 25 1.77 0.000326793 2.22417191 0.623416878 0.035524627 0.58655356
GOTERM_BP_FAT GO:0007268~synaptic transmission 25 1.77 0.00118084 2.036741693 0.970707841 0.072363798 2.104161184
INTERPRO IPR003598:Immunoglobulin subtype 2 25 1.77 0.00197458 1.965838262 0.958588326 0.180459965 3.258173081
INTERPRO IPR003961:Fibronectin, type III 25 1.77 0.002619758 1.924668874 0.985388828 0.199420479 4.30093573
GOTERM_CC_FAT GO:0031225~anchored to membrane 25 1.77 0.011608123 1.702530922 0.986545649 0.147491692 14.86611796
SMART SM00408:IGc2 25 1.77 0.013019435 1.679122318 0.980892782 0.35579819 16.07938579
SMART SM00060:FN3 25 1.77 0.016569976 1.643957453 0.993565337 0.396256344 20.02900662
GOTERM_BP_FAT GO:0046903~secretion 25 1.77 0.017841667 1.640452585 1 0.298052134 27.67685575
GOTERM_BP_FAT GO:0001568~blood vessel development 25 1.77 0.049309465 1.48581976 1 0.480081537 59.75298158
GOTERM_BP_FAT GO:0050778~positive regulation of immune response 24 1.70 5.14919E-05 2.559106033 0.142609505 0.009570274 0.092638184
KEGG_PATHWAY mmu04514:Cell adhesion molecules (CAMs) 24 1.70 0.000245329 2.292907093 0.03896864 0.01967793 0.296456907
GOTERM_BP_FAT GO:0035239~tube morphogenesis 24 1.70 0.001529239 2.035312401 0.989671661 0.085761678 2.716946121
GOTERM_BP_FAT GO:0002009~morphogenesis of an epithelium 24 1.70 0.001786225 2.011782777 0.995213773 0.087989403 3.166624979
INTERPRO IPR013151:Immunoglobulin 24 1.70 0.002218319 1.982625195 0.972059583 0.189781913 3.653362827
GOTERM_BP_FAT GO:0048858~cell projection morphogenesis 24 1.70 0.011979554 1.722962478 1 0.253808257 19.50042283
INTERPRO IPR018247:EF-HAND 1 24 1.70 0.016888083 1.672546373 1 0.564598428 24.83176289
GOTERM_BP_FAT GO:0032990~cell part morphogenesis 24 1.70 0.020294857 1.641690663 1 0.316498851 30.86012352
INTERPRO IPR006209:EGF 23 1.63 4.83477E-05 2.642209489 0.074933794 0.019284128 0.080994111
GOTERM_BP_FAT GO:0060341~regulation of cellular localization 23 1.63 0.001560006 2.071657265 0.990580158 0.084255313 2.770886076
UP_SEQ_FEATURE DNA-binding region:Basic motif 23 1.63 0.002258691 2.010711557 0.998596687 0.396676362 3.975971655
GOTERM_BP_FAT GO:0048667~cell morphogenesis involved in neuron differentiation 23 1.63 0.00706148 1.832619888 0.999999999 0.205592054 11.97490938
GOTERM_BP_FAT GO:0046649~lymphocyte activation 23 1.63 0.012153529 1.746266071 1 0.253453816 19.75516681
GOTERM_MF_FAT GO:0042803~protein homodimerization activity 23 1.63 0.013702172 1.726874636 0.999993791 0.31248555 19.27197238
GOTERM_MF_FAT GO:0004867~serine-type endopeptidase inhibitor activity 22 1.56 3.39693E-05 2.772139775 0.029088372 0.002680023 0.052696598
INTERPRO IPR000008:C2 calcium-dependent membrane targeting 22 1.56 0.000177066 2.488448803 0.248194837 0.046433723 0.296327519
SMART SM00239:C2 22 1.56 0.001333659 2.125510528 0.331710551 0.095848616 1.76932827
GOTERM_BP_FAT GO:0048812~neuron projection morphogenesis 22 1.56 0.00964344 1.812700107 1 0.229604115 16.00489495
GOTERM_CC_FAT GO:0005911~cell-cell junction 22 1.56 0.010763345 1.79267649 0.98155945 0.147624507 13.85762474
GOTERM_MF_FAT GO:0022832~voltage-gated channel activity 22 1.56 0.011652038 1.780983654 0.999962271 0.287875496 16.62855438
GOTERM_MF_FAT GO:0005244~voltage-gated ion channel activity 22 1.56 0.011652038 1.780983654 0.999962271 0.287875496 16.62855438
INTERPRO IPR008957:Fibronectin, type III-like fold 22 1.56 0.01607225 1.72993767 1 0.557674917 23.7795155
GOTERM_BP_FAT GO:0051338~regulation of transferase activity 22 1.56 0.033524222 1.603192054 1 0.40070403 45.86765384
GOTERM_MF_FAT GO:0005267~potassium channel activity 21 1.49 0.000513689 2.358956151 0.360142688 0.033763769 0.794116762
GOTERM_BP_FAT GO:0001817~regulation of cytokine production 21 1.49 0.001329536 2.190889338 0.981226284 0.079481975 2.366141258
GOTERM_BP_FAT GO:0015674~di-, tri-valent inorganic cation transport 21 1.49 0.007392386 1.891513155 1 0.210105867 12.50141212
GOTERM_BP_FAT GO:0055080~cation homeostasis 21 1.49 0.02850294 1.65507401 1 0.373150761 40.57581757
GOTERM_BP_FAT GO:0007169~transmembrane receptor protein tyrosine kinase signaling pathway 21 1.49 0.041918957 1.586112593 1 0.445480716 53.73370666
KEGG_PATHWAY mmu04510:Focal adhesion 21 1.49 0.046036468 1.56045066 0.999516777 0.420366638 43.46410705
GOTERM_BP_FAT GO:0051046~regulation of secretion 20 1.42 0.000891842 2.320256137 0.930471055 0.067753687 1.593088949
INTERPRO IPR003591:Leucine-rich repeat, typical subtype 20 1.42 0.001133041 2.279762822 0.839002167 0.122303659 1.881985101
GOTERM_BP_FAT GO:0051050~positive regulation of transport 20 1.42 0.001726726 2.197212251 0.994280738 0.088088904 3.062686594
SMART SM00369:LRR_TYP 20 1.42 0.006310858 1.947261231 0.852203425 0.239007066 8.119840939
SP_PIR_KEYWORDS ATP 20 1.42 0.008963746 1.896365848 0.987759866 0.136505573 12.12528311
GOTERM_BP_FAT GO:0060284~regulation of cell development 20 1.42 0.013174983 1.824100736 1 0.261037035 21.23555335
GOTERM_BP_FAT GO:0048562~embryonic organ morphogenesis 20 1.42 0.014906401 1.8014411 1 0.279319481 23.6861338
GOTERM_BP_FAT GO:0007409~axonogenesis 20 1.42 0.016811565 1.779337528 1 0.291514595 26.29935076
GOTERM_BP_FAT GO:0010740~positive regulation of protein kinase cascade 19 1.35 2.51532E-05 3.131027457 0.072403836 0.007487707 0.045262862
INTERPRO IPR018029:C2 membrane targeting protein 19 1.35 0.000266658 2.635706922 0.349259331 0.052288842 0.445950339
UP_SEQ_FEATURE domain:EGF-like 1 19 1.35 0.000759359 2.413183764 0.889945223 0.217449992 1.353729024
UP_SEQ_FEATURE domain:Leucine-zipper 19 1.35 0.00085094 2.390630645 0.91567135 0.219096239 1.515818013
GOTERM_BP_FAT GO:0019932~second-messenger-mediated signaling 19 1.35 0.001154964 2.335003527 0.968350316 0.072317564 2.058503549
GOTERM_MF_FAT GO:0030955~potassium ion binding 19 1.35 0.001163094 2.333253239 0.636260245 0.061251139 1.789596122
SP_PIR_KEYWORDS potassium 19 1.35 0.001355504 2.305370177 0.484846938 0.031091691 1.928360853
GOTERM_CC_FAT GO:0043025~cell soma 19 1.35 0.001431424 2.28924927 0.410554936 0.039844011 1.955156002
GOTERM_BP_FAT GO:0006816~calcium ion transport 19 1.35 0.001548352 2.277110878 0.990245841 0.085190695 2.750458259
INTERPRO IPR013098:Immunoglobulin I-set 19 1.35 0.002339781 2.199881368 0.977035439 0.189136643 3.849729561
GOTERM_CC_FAT GO:0045211~postsynaptic membrane 19 1.35 0.003309701 2.125731465 0.705744055 0.069431079 4.466897444
GOTERM_BP_FAT GO:0006575~cellular amino acid derivative metabolic process 19 1.35 0.008140336 1.954116427 1 0.214795082 13.68053257
GOTERM_BP_FAT GO:0002694~regulation of leukocyte activation 19 1.35 0.019111392 1.789158547 1 0.31063495 29.34134545
GOTERM_BP_FAT GO:0050865~regulation of cell activation 19 1.35 0.021510069 1.766220617 1 0.323894455 32.38752756
KEGG_PATHWAY mmu04512:ECM-receptor interaction 18 1.28 3.04377E-05 3.190732159 0.004918841 0.004918841 0.036824883
GOTERM_MF_FAT GO:0022834~ligand-gated channel activity 18 1.28 0.000891692 2.460690109 0.539400158 0.053868071 1.37470334
GOTERM_MF_FAT GO:0015276~ligand-gated ion channel activity 18 1.28 0.000891692 2.460690109 0.539400158 0.053868071 1.37470334
SP_PIR_KEYWORDS potassium transport 18 1.28 0.001152686 2.408561857 0.431064825 0.029247388 1.642047241
SP_PIR_KEYWORDS protease inhibitor 18 1.28 0.001421192 2.364368061 0.501153748 0.031117271 2.020924971
SP_PIR_KEYWORDS postsynaptic cell membrane 18 1.28 0.001574126 2.342873806 0.537150898 0.032938958 2.236116418
GOTERM_BP_FAT GO:0001763~morphogenesis of a branching structure 18 1.28 0.005321356 2.088230523 0.999999881 0.174759417 9.156614335
GOTERM_BP_FAT GO:0002252~immune effector process 18 1.28 0.005768502 2.071657265 0.999999969 0.180197509 9.888836146
GOTERM_BP_FAT GO:0007548~sex differentiation 18 1.28 0.007872356 2.007913964 1 0.212224556 13.25980575
SP_PIR_KEYWORDS voltage-gated channel 18 1.28 0.012864333 1.909008287 0.998220587 0.165480521 16.96187685
GOTERM_BP_FAT GO:0016053~organic acid biosynthetic process 18 1.28 0.016937492 1.851268194 1 0.290055596 26.46906638
GOTERM_BP_FAT GO:0046394~carboxylic acid biosynthetic process 18 1.28 0.016937492 1.851268194 1 0.290055596 26.46906638
GOTERM_BP_FAT GO:0001655~urogenital system development 18 1.28 0.0230844 1.787868598 1 0.336680678 34.31895706
GOTERM_BP_FAT GO:0030003~cellular cation homeostasis 18 1.28 0.032536179 1.717294838 1 0.39449932 44.86289748
SP_PIR_KEYWORDS Serine protease inhibitor 17 1.21 0.000149138 2.96827508 0.070337538 0.005196004 0.213879372
SP_PIR_KEYWORDS collagen 17 1.21 0.000200037 2.897601864 0.093194979 0.006500634 0.286777055
GOTERM_BP_FAT GO:0032101~regulation of response to external stimulus 17 1.21 0.001750448 2.393468102 0.994672764 0.087749587 3.104140134
GOTERM_BP_FAT GO:0045137~development of primary sexual characteristics 17 1.21 0.001939741 2.370453986 0.996977213 0.089329201 3.434313594
INTERPRO IPR005821:Ion transport 17 1.21 0.003358176 2.252035976 0.995568984 0.227446203 5.48142169
GOTERM_MF_FAT GO:0019842~vitamin binding 17 1.21 0.008842891 2.035887776 0.99955554 0.256860263 12.87479181
GOTERM_BP_FAT GO:0030855~epithelial cell differentiation 17 1.21 0.010236968 2.004286297 1 0.234597864 16.90632818
SP_PIR_KEYWORDS lyase 17 1.21 0.010753634 1.995070136 0.99494312 0.148037092 14.3763288
GOTERM_BP_FAT GO:0010817~regulation of hormone levels 17 1.21 0.01467994 1.925993863 1 0.277413171 23.36975399
GOTERM_BP_FAT GO:0048608~reproductive structure development 17 1.21 0.016827616 1.896363189 1 0.290096956 26.32100427
GOTERM_BP_FAT GO:0050767~regulation of neurogenesis 17 1.21 0.019209647 1.867630413 1 0.310313778 29.46862905
GOTERM_MF_FAT GO:0015293~symporter activity 17 1.21 0.019315666 1.866230462 0.999999956 0.392568541 26.11426148
GOTERM_BP_FAT GO:0051347~positive regulation of transferase activity 17 1.21 0.023255125 1.826127515 1 0.337113936 34.52524631
UP_SEQ_FEATURE domain:Ig-like C2-type 1 17 1.21 0.031234799 1.760549451 1 0.933549999 43.41125074
GOTERM_BP_FAT GO:0006006~glucose metabolic process 17 1.21 0.031380777 1.760908675 1 0.388031681 43.66561309
UP_SEQ_FEATURE domain:Ig-like C2-type 2 17 1.21 0.033228169 1.747110142 1 0.929573726 45.4644072
GOTERM_BP_FAT GO:0051960~regulation of nervous system development 17 1.21 0.048458743 1.665724422 1 0.478459158 59.09980693
INTERPRO IPR000215:Protease inhibitor I4, serpin 16 1.14 4.85468E-05 3.40973222 0.075230597 0.01552047 0.081327661
SMART SM00093:SERPIN 16 1.14 0.000271372 2.912425493 0.078696253 0.040154311 0.362393563
INTERPRO IPR008160:Collagen triple helix repeat 16 1.14 0.000323872 2.904586706 0.406573604 0.056333496 0.541388068
GOTERM_BP_FAT GO:0002526~acute inflammatory response 16 1.14 0.000370366 2.864513749 0.669404272 0.038759536 0.664516472
INTERPRO IPR000483:Cysteine-rich flanking region, C-terminal 16 1.14 0.000425901 2.834596665 0.496551169 0.066325423 0.71137003
KEGG_PATHWAY mmu04640:Hematopoietic cell lineage 16 1.14 0.000439127 2.802442002 0.068681741 0.017631271 0.5300725
GOTERM_BP_FAT GO:0002253~activation of immune response 16 1.14 0.000717805 2.697972252 0.882998925 0.059460904 1.284103794
INTERPRO IPR002035:von Willebrand factor, type A 16 1.14 0.000808006 2.673540036 0.728073748 0.102837881 1.345536113
GOTERM_BP_FAT GO:0048754~branching morphogenesis of a tube 16 1.14 0.001641347 2.494899072 0.99261552 0.086887174 2.913357023
GOTERM_BP_FAT GO:0043408~regulation of MAPKKK cascade 16 1.14 0.001641347 2.494899072 0.99261552 0.086887174 2.913357023
GOTERM_CC_FAT GO:0044420~extracellular matrix part 16 1.14 0.001932678 2.451644527 0.510244074 0.04971102 2.631386053
SMART SM00082:LRRCT 16 1.14 0.002053407 2.421173 0.462467663 0.116755546 2.712119058
SMART SM00327:VWA 16 1.14 0.003697071 2.283606352 0.673258065 0.170083983 4.833837672
GOTERM_CC_FAT GO:0016327~apicolateral plasma membrane 16 1.14 0.005838092 2.189818413 0.884740469 0.093539367 7.753834658
GOTERM_BP_FAT GO:0040012~regulation of locomotion 16 1.14 0.008333567 2.109323761 1 0.21554598 13.98270597
SP_PIR_KEYWORDS Sodium transport 16 1.14 0.010195676 2.063792742 0.993337604 0.14926455 13.68043774
GOTERM_BP_FAT GO:0031644~regulation of neurological system process 16 1.14 0.010600192 2.053324015 1 0.238265808 17.45346824
GOTERM_BP_FAT GO:0042110~T cell activation 16 1.14 0.013325546 2.000220807 1 0.260208198 21.45156929
KEGG_PATHWAY mmu04670:Leukocyte transendothelial migration 16 1.14 0.013973322 1.978194355 0.897679239 0.277951697 15.65685246
GOTERM_BP_FAT GO:0045860~positive regulation of protein kinase activity 16 1.14 0.023271338 1.871174304 1 0.335719049 34.54480517
GOTERM_BP_FAT GO:0006814~sodium ion transport 16 1.14 0.024826781 1.856204909 1 0.345837861 36.39576547
SP_PIR_KEYWORDS gpi-anchor 16 1.14 0.025925237 1.847427374 0.99999736 0.239128554 31.4135793
GOTERM_MF_FAT GO:0022843~voltage-gated cation channel activity 16 1.14 0.030111977 1.81134133 1 0.453296906 37.7755213
SP_PIR_KEYWORDS growth factor 16 1.14 0.031295548 1.803787357 0.999999823 0.267258359 36.64644879
GOTERM_BP_FAT GO:0033674~positive regulation of kinase activity 16 1.14 0.033789301 1.784812413 1 0.401625159 46.13425916
GOTERM_BP_FAT GO:0030005~cellular di-, tri-valent inorganic cation homeostasis 16 1.14 0.042496656 1.73153443 1 0.448556547 54.23325836
GOTERM_BP_FAT GO:0009309~amine biosynthetic process 15 1.06 0.000396437 2.979780997 0.694189391 0.040031496 0.711136136
KEGG_PATHWAY mmu04610:Complement and coagulation cascades 15 1.06 0.000423876 2.942564103 0.066376877 0.022634061 0.511705826
GOTERM_BP_FAT GO:0002697~regulation of immune effector process 15 1.06 0.002639551 2.471863782 0.999628294 0.109647957 4.645751125
GOTERM_CC_FAT GO:0034703~cation channel complex 15 1.06 0.00683804 2.225835163 0.920492444 0.10423999 9.024587596
GOTERM_BP_FAT GO:0046942~carboxylic acid transport 15 1.06 0.007746351 2.197212251 1 0.213016942 13.06130969
GOTERM_BP_FAT GO:0006875~cellular metal ion homeostasis 15 1.06 0.008454042 2.175240128 1 0.216452562 14.1705995
GOTERM_BP_FAT GO:0015849~organic acid transport 15 1.06 0.008454042 2.175240128 1 0.216452562 14.1705995
GOTERM_BP_FAT GO:0045664~regulation of neuron differentiation 15 1.06 0.010022204 2.132588361 1 0.2338282 16.5812115
GOTERM_CC_FAT GO:0043296~apical junction complex 15 1.06 0.011604668 2.093607331 0.986528281 0.152665808 14.86201506
GOTERM_BP_FAT GO:0055065~metal ion homeostasis 15 1.06 0.013836332 2.052113328 1 0.26536577 22.18024454
GOTERM_BP_FAT GO:0001822~kidney development 15 1.06 0.014945205 2.032934699 1 0.27822271 23.74022142
GOTERM_BP_FAT GO:0051969~regulation of transmission of nerve impulse 15 1.06 0.014945205 2.032934699 1 0.27822271 23.74022142
GOTERM_CC_FAT GO:0030424~axon 15 1.06 0.018632117 1.976208789 0.999031835 0.219531589 22.8371692
GOTERM_BP_FAT GO:0060562~epithelial tube morphogenesis 15 1.06 0.02007544 1.959675791 1 0.31526488 30.58088717
GOTERM_MF_FAT GO:0005179~hormone activity 15 1.06 0.023207891 1.923548315 0.999999999 0.392067775 30.53571427
GOTERM_BP_FAT GO:0008544~epidermis development 15 1.06 0.048654195 1.740192102 1 0.478374536 59.25075249
UP_SEQ_FEATURE site:Reactive bond 14 0.99 8.02658E-05 3.624660633 0.207990298 0.032762939 0.143916712
UP_SEQ_FEATURE domain:C2 2 14 0.99 0.00021865 3.306707946 0.470196445 0.076335274 0.391581231
UP_SEQ_FEATURE domain:C2 1 14 0.99 0.00021865 3.306707946 0.470196445 0.076335274 0.391581231
GOTERM_BP_FAT GO:0006836~neurotransmitter transport 14 0.99 0.003055676 2.537780149 0.999893177 0.119270461 5.359282103
GOTERM_CC_FAT GO:0043235~receptor complex 14 0.99 0.005360703 2.377799813 0.862402232 0.094412187 7.141372126
GOTERM_MF_FAT GO:0042165~neurotransmitter binding 14 0.99 0.006403169 2.331841713 0.996235894 0.224105257 9.486968382
GOTERM_MF_FAT GO:0030594~neurotransmitter receptor activity 14 0.99 0.006403169 2.331841713 0.996235894 0.224105257 9.486968382
SP_PIR_KEYWORDS Proto-oncogene 14 0.99 0.006465111 2.330765931 0.958067866 0.110833236 8.89070471
GOTERM_BP_FAT GO:0008406~gonad development 14 0.99 0.00701543 2.307072863 0.999999999 0.206391169 11.90140278
GOTERM_BP_FAT GO:0002443~leukocyte mediated immunity 14 0.99 0.007712759 2.281150696 1 0.21415066 13.00832109
GOTERM_BP_FAT GO:0032880~regulation of protein localization 14 0.99 0.008463653 2.255804577 1 0.214846795 14.18557148
GOTERM_BP_FAT GO:0006874~cellular calcium ion homeostasis 14 0.99 0.009270823 2.231015516 1 0.227164545 15.43426378
INTERPRO IPR000372:Leucine-rich repeat, cysteine-rich flanking region, N-terminal 14 0.99 0.009996841 2.213576159 0.999999907 0.450902713 15.49658796
GOTERM_BP_FAT GO:0030334~regulation of cell migration 14 0.99 0.010137018 2.206765347 1 0.234367065 16.75516942
GOTERM_BP_FAT GO:0030534~adult behavior 14 0.99 0.011065016 2.183036688 1 0.240249231 18.14868991
GOTERM_BP_FAT GO:0055074~calcium ion homeostasis 14 0.99 0.013117655 2.137078021 1 0.261769542 21.15315553
GOTERM_BP_FAT GO:0048511~rhythmic process 14 0.99 0.016730677 2.071657265 1 0.291993926 26.19014089
GOTERM_BP_FAT GO:0007411~axon guidance 14 0.99 0.016730677 2.071657265 1 0.291993926 26.19014089
GOTERM_BP_FAT GO:0050804~regulation of synaptic transmission 14 0.99 0.019528445 2.03022412 1 0.312943897 29.88012268
GOTERM_MF_FAT GO:0031402~sodium ion binding 14 0.99 0.024481956 1.969613874 1 0.394027295 31.9283804
GOTERM_BP_FAT GO:0043583~ear development 14 0.99 0.026158203 1.952138576 1 0.355967752 37.94076665
SP_PIR_KEYWORDS Sodium 14 0.99 0.026871712 1.946076409 0.999998359 0.242325159 32.36408197
SMART SM00013:LRRNT 14 0.99 0.031854815 1.89072784 0.999943242 0.502587973 35.14669567
GOTERM_BP_FAT GO:0051270~regulation of cell motion 14 0.99 0.032119978 1.897405719 1 0.392074161 44.43440673
GOTERM_BP_FAT GO:0008015~blood circulation 14 0.99 0.041516367 1.829030738 1 0.443769194 53.38253524
GOTERM_BP_FAT GO:0003013~circulatory system process 14 0.99 0.041516367 1.829030738 1 0.443769194 53.38253524
GOTERM_BP_FAT GO:0042060~wound healing 14 0.99 0.044139018 1.812700107 1 0.458342397 55.62577407
INTERPRO IPR004827:Basic-leucine zipper (bZIP) transcription factor 13 0.92 0.000218188 3.539965048 0.296396051 0.048979776 0.365029786
PIR_SUPERFAMILY PIRSF001630:serpin 13 0.92 0.000228768 3.501092354 0.128669454 0.128669454 0.337494251
GOTERM_BP_FAT GO:0001656~metanephros development 13 0.92 0.000494834 3.250358812 0.772118416 0.046587278 0.886900236
SMART SM00338:BRLZ 13 0.92 0.000887094 3.023663967 0.235108498 0.085465812 1.180134444
GOTERM_BP_FAT GO:0016064~immunoglobulin mediated immune response 13 0.92 0.001077757 2.992393827 0.960127771 0.077392963 1.922154898
GOTERM_BP_FAT GO:0019724~B cell mediated immunity 13 0.92 0.001433095 2.900320171 0.986228569 0.083737355 2.548204968
UP_SEQ_FEATURE domain:EGF-like 3 13 0.92 0.003077297 2.651807996 0.999870694 0.472456349 5.379744252
GOTERM_CC_FAT GO:0005604~basement membrane 13 0.92 0.004934976 2.510416828 0.838865235 0.091608381 6.592008637
GOTERM_BP_FAT GO:0002449~lymphocyte mediated immunity 13 0.92 0.005511314 2.480536988 0.999999933 0.176568364 9.468363757
INTERPRO IPR013320:Concanavalin A-like lectin/glucanase, subgroup 13 0.92 0.005541208 2.482572891 0.999870485 0.311326461 8.891796723
GOTERM_BP_FAT GO:0043523~regulation of neuron apoptosis 13 0.92 0.00832478 2.356510139 1 0.217207658 13.96898691
GOTERM_BP_FAT GO:0006576~biogenic amine metabolic process 13 0.92 0.00832478 2.356510139 1 0.217207658 13.96898691
GOTERM_BP_FAT GO:0022612~gland morphogenesis 13 0.92 0.012152133 2.24429537 1 0.255186166 19.7531255
GOTERM_BP_FAT GO:0002460~adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 13 0.92 0.012152133 2.24429537 1 0.255186166 19.7531255
GOTERM_BP_FAT GO:0002250~adaptive immune response 13 0.92 0.012152133 2.24429537 1 0.255186166 19.7531255
UP_SEQ_FEATURE domain:EGF-like 2 13 0.92 0.013225742 2.215434528 1 0.827619442 21.24922975
GOTERM_BP_FAT GO:0006979~response to oxidative stress 13 0.92 0.015814382 2.166905875 1 0.28496915 24.94229365
GOTERM_BP_FAT GO:0042493~response to drug 13 0.92 0.01720369 2.142281944 1 0.290638725 26.82661631
GOTERM_BP_FAT GO:0050954~sensory perception of mechanical stimulus 13 0.92 0.023691416 2.049139251 1 0.337500574 35.04964562
INTERPRO IPR000152:EGF-type aspartate/asparagine hydroxylation conserved site 13 0.92 0.025199831 2.033596942 1 0.633170696 34.80136734
UP_SEQ_FEATURE domain:Ig-like C2-type 3 13 0.92 0.026710466 2.011716411 1 0.939728354 38.47713376
GOTERM_BP_FAT GO:0030155~regulation of cell adhesion 13 0.92 0.027538243 2.005540544 1 0.366153234 39.50473532
GOTERM_BP_FAT GO:0043434~response to peptide hormone stimulus 13 0.92 0.029623148 1.984429591 1 0.379936501 41.79708397
GOTERM_BP_FAT GO:0002696~positive regulation of leukocyte activation 13 0.92 0.039105808 1.904250617 1 0.431583935 51.22662228
GOTERM_MF_FAT GO:0005249~voltage-gated potassium channel activity 13 0.92 0.039276777 1.902823216 1 0.530906974 46.29921926
INTERPRO IPR008266:Tyrosine protein kinase, active site 13 0.92 0.040990316 1.89265458 1 0.726559821 50.41219997
GOTERM_BP_FAT GO:0050867~positive regulation of cell activation 13 0.92 0.044569556 1.866542684 1 0.46013604 55.98413969
SP_PIR_KEYWORDS hormone 13 0.92 0.045542068 1.861283079 1 0.339276926 48.78451554
GOTERM_MF_FAT GO:0005275~amine transmembrane transporter activity 12 0.85 0.003540475 2.76013917 0.954138636 0.165816518 5.354744294
GOTERM_MF_FAT GO:0005230~extracellular ligand-gated ion channel activity 12 0.85 0.005117218 2.634678299 0.988417728 0.199816322 7.652067532
GOTERM_BP_FAT GO:0002703~regulation of leukocyte mediated immunity 12 0.85 0.007990667 2.485988718 1 0.213151986 13.44579103
GOTERM_MF_FAT GO:0043168~anion binding 12 0.85 0.00802902 2.484125253 0.99909301 0.253149907 11.75805559
GOTERM_MF_FAT GO:0008081~phosphoric diester hydrolase activity 12 0.85 0.009899611 2.415121774 0.999824106 0.26565211 14.30502379
GOTERM_MF_FAT GO:0015294~solute:cation symporter activity 12 0.85 0.010952276 2.382037914 0.999930211 0.281077032 15.70790519
UP_SEQ_FEATURE domain:EGF-like 12 0.85 0.011000878 2.375827978 1 0.815719524 18.00186374
KEGG_PATHWAY mmu04662:B cell receptor signaling pathway 12 0.85 0.018264698 2.206923077 0.949522936 0.282372054 19.99293348
GOTERM_BP_FAT GO:0050670~regulation of lymphocyte proliferation 12 0.85 0.02079132 2.175240128 1 0.318051478 31.48802472
GOTERM_BP_FAT GO:0032944~regulation of mononuclear cell proliferation 12 0.85 0.02079132 2.175240128 1 0.318051478 31.48802472
UP_SEQ_FEATURE short sequence motif:Cell attachment site 12 0.85 0.024032247 2.125740823 1 0.940789135 35.36778401
GOTERM_BP_FAT GO:0070663~regulation of leukocyte proliferation 12 0.85 0.024555525 2.122185491 1 0.344324729 36.0765734
GOTERM_BP_FAT GO:0007605~sensory perception of sound 12 0.85 0.02879196 2.071657265 1 0.374571394 40.89320725
UP_SEQ_FEATURE repeat:LRR 12 12 0.85 0.03104252 2.045016488 1 0.93771258 43.20939034
INTERPRO IPR016130:Protein-tyrosine phosphatase, active site 12 0.85 0.033261006 2.028202786 1 0.702099888 43.27164795
GOTERM_BP_FAT GO:0001890~placenta development 12 0.85 0.036092276 2.000220807 1 0.414796246 48.39888134
GOTERM_CC_FAT GO:0045121~membrane raft 12 0.85 0.037052344 1.990158499 0.99999911 0.352978954 40.57414367
INTERPRO IPR011701:Major facilitator superfamily MFS-1 12 0.85 0.038415992 1.982625195 1 0.716959477 48.13359834
GOTERM_BP_FAT GO:0048839~inner ear development 12 0.85 0.038790645 1.977491026 1 0.431991559 50.93789059
INTERPRO IPR001881:EGF-like calcium-binding 12 0.85 0.044103012 1.939051015 1 0.726807276 53.04162322
GOTERM_BP_FAT GO:0051098~regulation of binding 12 0.85 0.044602131 1.933546781 1 0.458883269 56.01114272
GOTERM_BP_FAT GO:0016485~protein processing 12 0.85 0.044602131 1.933546781 1 0.458883269 56.01114272
GOTERM_BP_FAT GO:0043410~positive regulation of MAPKKK cascade 11 0.78 0.001142635 3.393991689 0.967161211 0.076371394 2.036741843
SP_PIR_KEYWORDS proteoglycan 11 0.78 0.002081083 3.149863673 0.638939565 0.039929344 2.946308598
GOTERM_BP_FAT GO:0046546~development of primary male sexual characteristics 11 0.78 0.002958352 3.009766215 0.999856999 0.118796371 5.192854122
GOTERM_MF_FAT GO:0004714~transmembrane receptor protein tyrosine kinase activity 11 0.78 0.004503464 2.846393519 0.980205457 0.186524459 6.764096806
GOTERM_BP_FAT GO:0046661~male sex differentiation 11 0.78 0.005108704 2.798554551 0.999999774 0.170252056 8.806418932
GOTERM_BP_FAT GO:0046545~development of primary female sexual characteristics 11 0.78 0.005108704 2.798554551 0.999999774 0.170252056 8.806418932
UP_SEQ_FEATURE domain:Ig-like C2-type 4 11 0.78 0.007545596 2.644522809 1 0.725909942 12.7068306
GOTERM_BP_FAT GO:0001819~positive regulation of cytokine production 11 0.78 0.009331425 2.572864668 1 0.226633222 15.52731855
GOTERM_BP_FAT GO:0046660~female sex differentiation 11 0.78 0.011618516 2.492462647 1 0.248905946 18.9693286
GOTERM_BP_FAT GO:0008217~regulation of blood pressure 11 0.78 0.012909629 2.454117068 1 0.261638151 20.85347659
INTERPRO IPR000198:RhoGAP 11 0.78 0.014538073 2.414166749 1 0.532827397 21.76314153
GOTERM_MF_FAT GO:0031404~chloride ion binding 11 0.78 0.017506115 2.344088781 0.999999784 0.371913373 23.97022934
SP_PIR_KEYWORDS chloride 11 0.78 0.018992017 2.31607623 0.999915302 0.208962957 24.06250489
UP_SEQ_FEATURE repeat:LRR 13 11 0.78 0.020813284 2.278358112 1 0.921594513 31.43425546
GOTERM_BP_FAT GO:0001764~neuron migration 11 0.78 0.02103449 2.278822991 1 0.31953491 31.79360629
INTERPRO IPR013111:EGF, extracellular 11 0.78 0.021233736 2.278157355 1 0.607211959 30.21036666
GOTERM_CC_FAT GO:0034705~potassium channel complex 11 0.78 0.022827009 2.247340817 0.999800719 0.247253249 27.26153277
GOTERM_CC_FAT GO:0008076~voltage-gated potassium channel complex 11 0.78 0.022827009 2.247340817 0.999800719 0.247253249 27.26153277
SP_PIR_KEYWORDS potassium channel 11 0.78 0.022877358 2.249902623 0.999987835 0.226789588 28.26761124
GOTERM_BP_FAT GO:0006865~amino acid transport 11 0.78 0.023026344 2.246726893 1 0.337594304 34.24866747
KEGG_PATHWAY mmu04070:Phosphatidylinositol signaling system 11 0.78 0.028907194 2.157880342 0.991365457 0.350789711 29.87892493
GOTERM_BP_FAT GO:0051101~regulation of DNA binding 11 0.78 0.029816183 2.15564337 1 0.380318668 42.00512458
KEGG_PATHWAY mmu04520:Adherens junction 11 0.78 0.031360756 2.129487179 0.994268692 0.349589095 31.99293451
KEGG_PATHWAY mmu04370:VEGF signaling pathway 11 0.78 0.031360756 2.129487179 0.994268692 0.349589095 31.99293451
GOTERM_BP_FAT GO:0042471~ear morphogenesis 11 0.78 0.035062227 2.098915913 1 0.408653515 47.39733441
SMART SM00324:RhoGAP 11 0.78 0.037843467 2.062062453 0.999991286 0.517204499 40.31220552
GOTERM_MF_FAT GO:0032403~protein complex binding 11 0.78 0.041079166 2.043564578 1 0.53205808 47.84137971
GOTERM_CC_FAT GO:0070160~occluding junction 11 0.78 0.041263434 2.040348899 0.999999823 0.367029473 44.05791006
GOTERM_CC_FAT GO:0005923~tight junction 11 0.78 0.041263434 2.040348899 0.999999823 0.367029473 44.05791006
GOTERM_MF_FAT GO:0017124~SH3 domain binding 11 0.78 0.044255466 2.017696672 1 0.5375763 50.45869916
SP_PIR_KEYWORDS allosteric enzyme 10 0.71 0.000642214 3.977100597 0.269585743 0.018309335 0.917978337
GOTERM_BP_FAT GO:0001657~ureteric bud development 10 0.71 0.001896653 3.452762108 0.996561024 0.08880025 3.359251042
GOTERM_BP_FAT GO:0043388~positive regulation of DNA binding 10 0.71 0.001896653 3.452762108 0.996561024 0.08880025 3.359251042
GOTERM_BP_FAT GO:0045785~positive regulation of cell adhesion 10 0.71 0.002254802 3.372465315 0.998823198 0.098566075 3.981500718
INTERPRO IPR001791:Laminin G 10 0.71 0.002868391 3.267660044 0.990221977 0.206562812 4.699956427
GOTERM_BP_FAT GO:0051099~positive regulation of binding 10 0.71 0.003131395 3.222577968 0.999914866 0.120475457 5.488574214
GOTERM_MF_FAT GO:0015171~amino acid transmembrane transporter activity 10 0.71 0.006535102 2.898146129 0.996646132 0.219424272 9.673278963
SMART SM00282:LamG 10 0.71 0.007954514 2.791074431 0.910352367 0.260280013 10.13211045
UP_SEQ_FEATURE domain:Ig-like C2-type 5 10 0.71 0.007978955 2.804796919 1 0.725520733 13.38820788
GOTERM_BP_FAT GO:0030278~regulation of ossification 10 0.71 0.009575042 2.736151104 1 0.229985678 15.90042241
GOTERM_BP_FAT GO:0007219~Notch signaling pathway 10 0.71 0.010807746 2.68548164 1 0.238794139 17.76458587
GOTERM_BP_FAT GO:0002819~regulation of adaptive immune response 10 0.71 0.013625076 2.589571581 1 0.263545353 21.87964866
GOTERM_BP_FAT GO:0002822~regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 10 0.71 0.013625076 2.589571581 1 0.263545353 21.87964866
GOTERM_BP_FAT GO:0031589~cell-substrate adhesion 10 0.71 0.015221895 2.544140501 1 0.277512338 24.12484312
GOTERM_BP_FAT GO:0050727~regulation of inflammatory response 10 0.71 0.015221895 2.544140501 1 0.277512338 24.12484312
GOTERM_BP_FAT GO:0042129~regulation of T cell proliferation 10 0.71 0.018821757 2.45789845 1 0.308347403 28.96487683
UP_SEQ_FEATURE repeat:LRR 14 10 0.71 0.019045133 2.447822765 1 0.911847284 29.17849547
GOTERM_MF_FAT GO:0043176~amine binding 10 0.71 0.023091883 2.375529614 0.999999998 0.398033918 30.40759221
SP_PIR_KEYWORDS Tight junction 10 0.71 0.024855465 2.347141336 0.999995484 0.239294827 30.32430015
GOTERM_BP_FAT GO:0002706~regulation of lymphocyte mediated immunity 10 0.71 0.033274273 2.231015516 1 0.399914576 45.61512371
GOTERM_BP_FAT GO:0050870~positive regulation of T cell activation 10 0.71 0.036269324 2.197212251 1 0.414833411 48.56920607
GOTERM_BP_FAT GO:0042472~inner ear morphogenesis 10 0.71 0.039443838 2.164418038 1 0.43288664 51.53451933
INTERPRO IPR011616:bZIP transcription factor, bZIP-1 9 0.64 0.000674824 4.41134106 0.662944956 0.094134517 1.124932407
INTERPRO IPR005829:Sugar transporter, conserved site 9 0.64 0.000855959 4.269039735 0.748305924 0.100681956 1.424852389
UP_SEQ_FEATURE domain:Collagen-like 9 0.64 0.001893256 3.78647584 0.99593411 0.367933213 3.342983155
GOTERM_BP_FAT GO:0051222~positive regulation of protein transport 9 0.64 0.002632652 3.625400213 0.999620531 0.110915732 4.633878561
INTERPRO IPR012680:Laminin G, subdomain 2 9 0.64 0.00411615 3.393339277 0.998699235 0.260688917 6.678920676
INTERPRO IPR008983:Tumour necrosis factor-like 9 0.64 0.005668306 3.227810531 0.999894586 0.306708516 9.086742712
GOTERM_BP_FAT GO:0002699~positive regulation of immune effector process 9 0.64 0.012361068 2.837269732 1 0.255439142 20.05806251
GOTERM_BP_FAT GO:0010001~glial cell differentiation 9 0.64 0.015843123 2.71905016 1 0.283727165 24.98173494
GOTERM_BP_FAT GO:0032946~positive regulation of mononuclear cell proliferation 9 0.64 0.019983586 2.610288154 1 0.3173306 30.46367546
GOTERM_BP_FAT GO:0001942~hair follicle development 9 0.64 0.019983586 2.610288154 1 0.3173306 30.46367546
GOTERM_BP_FAT GO:0022405~hair cycle process 9 0.64 0.019983586 2.610288154 1 0.3173306 30.46367546
GOTERM_BP_FAT GO:0022404~molting cycle process 9 0.64 0.019983586 2.610288154 1 0.3173306 30.46367546
GOTERM_BP_FAT GO:0050671~positive regulation of lymphocyte proliferation 9 0.64 0.019983586 2.610288154 1 0.3173306 30.46367546
GOTERM_BP_FAT GO:0007631~feeding behavior 9 0.64 0.022318983 2.559106033 1 0.330655194 33.38652613
GOTERM_BP_FAT GO:0070665~positive regulation of leukocyte proliferation 9 0.64 0.024840352 2.509892455 1 0.344429467 36.41169496
GOTERM_BP_FAT GO:0042063~gliogenesis 9 0.64 0.024840352 2.509892455 1 0.344429467 36.41169496
GOTERM_BP_FAT GO:0042303~molting cycle 9 0.64 0.024840352 2.509892455 1 0.344429467 36.41169496
GOTERM_BP_FAT GO:0042633~hair cycle 9 0.64 0.024840352 2.509892455 1 0.344429467 36.41169496
GOTERM_CC_FAT GO:0014069~postsynaptic density 9 0.64 0.025907504 2.487698123 0.999937845 0.268346294 30.35941451
GOTERM_BP_FAT GO:0008585~female gonad development 9 0.64 0.027554184 2.462535994 1 0.364749756 39.52258161
GOTERM_BP_FAT GO:0051047~positive regulation of secretion 9 0.64 0.027554184 2.462535994 1 0.364749756 39.52258161
GOTERM_BP_FAT GO:0001508~regulation of action potential 9 0.64 0.030466609 2.416933476 1 0.383705528 42.70095167
GOTERM_BP_FAT GO:0035282~segmentation 9 0.64 0.030466609 2.416933476 1 0.383705528 42.70095167
GOTERM_BP_FAT GO:0006959~humoral immune response 9 0.64 0.030466609 2.416933476 1 0.383705528 42.70095167
GOTERM_BP_FAT GO:0035148~tube lumen formation 9 0.64 0.048192886 2.212108605 1 0.478036808 58.89363991
GOTERM_BP_FAT GO:0051090~regulation of transcription factor activity 9 0.64 0.048192886 2.212108605 1 0.478036808 58.89363991
GOTERM_BP_FAT GO:0051952~regulation of amine transport 8 0.57 0.001151866 4.640512273 0.968055658 0.073673027 2.053036226
GOTERM_BP_FAT GO:0043123~positive regulation of I-kappaB kinase/NF-kappaB cascade 8 0.57 0.006321494 3.515539601 0.999999994 0.191767392 10.78667662
GOTERM_BP_FAT GO:0002824~positive regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 8 0.57 0.007497563 3.412141377 1 0.210777162 12.66813495
GOTERM_BP_FAT GO:0051091~positive regulation of transcription factor activity 8 0.57 0.007497563 3.412141377 1 0.210777162 12.66813495
GOTERM_BP_FAT GO:0002821~positive regulation of adaptive immune response 8 0.57 0.007497563 3.412141377 1 0.210777162 12.66813495
INTERPRO IPR016160:Aldehyde dehydrogenase, conserved site 8 0.57 0.008232417 3.36102176 0.999998354 0.400826732 12.93686211
GOTERM_BP_FAT GO:0042102~positive regulation of T cell proliferation 8 0.57 0.008827765 3.314651624 1 0.221158656 14.75099407
KEGG_PATHWAY mmu00500:Starch and sucrose metabolism 8 0.57 0.009360237 3.26951567 0.782052197 0.224243513 10.75608489
GOTERM_BP_FAT GO:0002541~activation of plasma proteins involved in acute inflammatory response 8 0.57 0.010323138 3.222577968 1 0.234550331 17.03643872
GOTERM_BP_FAT GO:0006956~complement activation 8 0.57 0.010323138 3.222577968 1 0.234550331 17.03643872
UP_SEQ_FEATURE domain:PDZ 1 8 0.57 0.011127482 3.167770638 1 0.803152599 18.18999433
SP_PIR_KEYWORDS tyrosine-specific protein kinase 8 0.57 0.012857343 3.095689113 0.998214415 0.169824297 16.95343493
SP_PIR_KEYWORDS neurotransmitter transport 8 0.57 0.012857343 3.095689113 0.998214415 0.169824297 16.95343493
GOTERM_BP_FAT GO:0008584~male gonad development 8 0.57 0.015908046 2.974687355 1 0.283047498 25.07075669
GOTERM_BP_FAT GO:0043122~regulation of I-kappaB kinase/NF-kappaB cascade 8 0.57 0.018170488 2.900320171 1 0.301012561 28.11142575
INTERPRO IPR018000:Neurotransmitter-gated ion-channel, conserved site 8 0.57 0.019341437 2.869164917 1 0.582724722 27.9143585
SP_PIR_KEYWORDS amidation 8 0.57 0.019428401 2.86351243 0.999931863 0.208638007 24.54598396
GOTERM_BP_FAT GO:0050769~positive regulation of neurogenesis 8 0.57 0.020649669 2.829580654 1 0.31784474 31.30942303
KEGG_PATHWAY mmu04960:Aldosterone-regulated sodium reabsorption 8 0.57 0.021346512 2.802442002 0.9696688 0.295000255 22.97962951
INTERPRO IPR006201:Neurotransmitter-gated ion-channel 8 0.57 0.021890896 2.800851466 1 0.608733944 30.99150855
INTERPRO IPR006202:Neurotransmitter-gated ion-channel ligand-binding 8 0.57 0.021890896 2.800851466 1 0.608733944 30.99150855
INTERPRO IPR006029:Neurotransmitter-gated ion-channel transmembrane region 8 0.57 0.021890896 2.800851466 1 0.608733944 30.99150855
GOTERM_BP_FAT GO:0046651~lymphocyte proliferation 8 0.57 0.023354695 2.762209686 1 0.335127327 34.64527534
GOTERM_MF_FAT GO:0016836~hydro-lyase activity 8 0.57 0.02343314 2.76013917 0.999999999 0.387749549 30.78385461
GOTERM_BP_FAT GO:0070661~leukocyte proliferation 8 0.57 0.026294126 2.697972252 1 0.355886159 38.0964842
GOTERM_BP_FAT GO:0032943~mononuclear cell proliferation 8 0.57 0.026294126 2.697972252 1 0.355886159 38.0964842
GOTERM_BP_FAT GO:0008652~cellular amino acid biosynthetic process 8 0.57 0.029475925 2.636654701 1 0.380018186 41.6379433
COG_ONTOLOGY Amino acid transport and metabolism 8 0.57 0.036060424 2.497704316 0.600749676 0.600749676 25.76776022
GOTERM_BP_FAT GO:0045088~regulation of innate immune response 8 0.57 0.036595215 2.52201754 1 0.416173859 48.88133437
GOTERM_BP_FAT GO:0001649~osteoblast differentiation 8 0.57 0.040545253 2.468357592 1 0.438932747 52.52506557
GOTERM_BP_FAT GO:0010720~positive regulation of cell development 8 0.57 0.040545253 2.468357592 1 0.438932747 52.52506557
INTERPRO IPR001723:Steroid hormone receptor 8 0.57 0.042137478 2.450745033 1 0.723171014 51.39696266
GOTERM_BP_FAT GO:0042398~cellular amino acid derivative biosynthetic process 8 0.57 0.04476269 2.416933476 1 0.457126504 56.14400902
GOTERM_BP_FAT GO:0042698~ovulation cycle 8 0.57 0.04476269 2.416933476 1 0.457126504 56.14400902
INTERPRO IPR008946:Nuclear hormone receptor, ligand-binding 8 0.57 0.046396222 2.400729828 1 0.738859955 54.89429834
INTERPRO IPR000536:Nuclear hormone receptor, ligand-binding, core 8 0.57 0.046396222 2.400729828 1 0.738859955 54.89429834
GOTERM_MF_FAT GO:0003707~steroid hormone receptor activity 8 0.57 0.049405016 2.365833574 1 0.557522857 54.44246765
GOTERM_MF_FAT GO:0051183~vitamin transporter activity 7 0.50 0.000953989 5.635284139 0.563694464 0.053793236 1.470082957
SP_PIR_KEYWORDS complement pathway 7 0.50 0.007831468 3.854728271 0.978606376 0.124162374 10.67297779
GOTERM_BP_FAT GO:0032680~regulation of tumor necrosis factor production 7 0.50 0.008902365 3.759674295 1 0.220976284 14.86640247
GOTERM_BP_FAT GO:0001658~branching involved in ureteric bud morphogenesis 7 0.50 0.010684823 3.625400213 1 0.238158896 17.58046151
GOTERM_BP_FAT GO:0060675~ureteric bud morphogenesis 7 0.50 0.010684823 3.625400213 1 0.238158896 17.58046151
GOTERM_BP_FAT GO:0042098~T cell proliferation 7 0.50 0.010684823 3.625400213 1 0.238158896 17.58046151
GOTERM_BP_FAT GO:0050873~brown fat cell differentiation 7 0.50 0.012705948 3.500386413 1 0.259815152 20.55901518
GOTERM_BP_FAT GO:0042401~biogenic amine biosynthetic process 7 0.50 0.012705948 3.500386413 1 0.259815152 20.55901518
GOTERM_BP_FAT GO:0006584~catecholamine metabolic process 7 0.50 0.014980632 3.383706866 1 0.27708684 23.78957067
GOTERM_BP_FAT GO:0034311~diol metabolic process 7 0.50 0.014980632 3.383706866 1 0.27708684 23.78957067
GOTERM_BP_FAT GO:0006958~complement activation, classical pathway 7 0.50 0.014980632 3.383706866 1 0.27708684 23.78957067
GOTERM_BP_FAT GO:0009712~catechol metabolic process 7 0.50 0.014980632 3.383706866 1 0.27708684 23.78957067
GOTERM_BP_FAT GO:0018958~phenol metabolic process 7 0.50 0.014980632 3.383706866 1 0.27708684 23.78957067
GOTERM_BP_FAT GO:0007189~activation of adenylate cyclase activity by G-protein signaling pathway 7 0.50 0.020346378 3.172225187 1 0.315555695 30.92553702
GOTERM_BP_FAT GO:0034097~response to cytokine stimulus 7 0.50 0.020346378 3.172225187 1 0.315555695 30.92553702
GOTERM_BP_FAT GO:0010579~positive regulation of adenylate cyclase activity by G-protein signaling pathway 7 0.50 0.020346378 3.172225187 1 0.315555695 30.92553702
GOTERM_BP_FAT GO:0010578~regulation of adenylate cyclase activity involved in G-protein signaling 7 0.50 0.020346378 3.172225187 1 0.315555695 30.92553702
SP_PIR_KEYWORDS amino-acid transport 7 0.50 0.024879733 3.037058638 0.999995539 0.234960728 30.34918731
UP_SEQ_FEATURE domain:EGF-like 4; calcium-binding 7 0.50 0.028240676 2.945036765 1 0.937587863 40.18969853
GOTERM_BP_FAT GO:0002455~humoral immune response mediated by circulating immunoglobulin 7 0.50 0.03062 2.900320171 1 0.383669395 42.86389738
UP_SEQ_FEATURE domain:PDZ 2 7 0.50 0.032421724 2.855793226 1 0.935144467 44.64239559
INTERPRO IPR004031:PMP-22/EMP/MP20/Claudin 7 0.50 0.032778709 2.859202539 1 0.704846014 42.7954839
GOTERM_BP_FAT GO:0051899~membrane depolarization 7 0.50 0.034679373 2.819755722 1 0.408257397 47.02041478
SP_PIR_KEYWORDS sugar transport 7 0.50 0.036685032 2.783970418 0.999999988 0.301177755 41.52259573
INTERPRO IPR003091:Voltage-dependent potassium channel 7 0.50 0.036954837 2.781926794 1 0.710037361 46.79686106
INTERPRO IPR001811:Small chemokine, interleukin-8-like 7 0.50 0.036954837 2.781926794 1 0.710037361 46.79686106
GOTERM_BP_FAT GO:0030335~positive regulation of cell migration 7 0.50 0.039069979 2.743546107 1 0.432810092 51.1938792
GOTERM_BP_FAT GO:0033555~multicellular organismal response to stress 7 0.50 0.039069979 2.743546107 1 0.432810092 51.1938792
SP_PIR_KEYWORDS blood coagulation 7 0.50 0.041296843 2.708727974 0.999999999 0.327394158 45.4155004
INTERPRO IPR011042:Six-bladed beta-propeller, TolB-like 7 0.50 0.041456326 2.708718194 1 0.723896883 50.81448007
GOTERM_MF_FAT GO:0008009~chemokine activity 7 0.50 0.043923766 2.669345119 1 0.541900258 50.19123337
GOTERM_BP_FAT GO:0032103~positive regulation of response to external stimulus 7 0.50 0.048869149 2.602851435 1 0.47842987 59.41615151
GOTERM_MF_FAT GO:0042379~chemokine receptor binding 7 0.50 0.049007505 2.600900372 1 0.561280819 54.14596003
INTERPRO IPR005828:General substrate transporter 6 0.43 0.00171286 6.301915799 0.936820985 0.168163084 2.83213322
UP_SEQ_FEATURE binding site:AMP 6 0.43 0.001734737 6.213703943 0.993550717 0.367785177 3.067179892
GOTERM_BP_FAT GO:0032652~regulation of interleukin-1 production 6 0.43 0.001817529 6.214971794 0.995641899 0.086612729 3.221267479
GOTERM_BP_FAT GO:0051180~vitamin transport 6 0.43 0.004724259 5.118212066 0.999999284 0.163984416 8.170076644
UP_SEQ_FEATURE domain:VWFA 2 6 0.43 0.006498906 4.751655957 0.999999994 0.717119044 11.04016684
BIOCARTA m_Ccr5Pathway:Pertussis toxin-insensitive CCR5 Signaling in Macrophage 6 0.43 0.009915194 4.182142857 0.816216176 0.816216176 11.44686303
INTERPRO IPR003129:Laminin G, thrombospondin-type, N-terminal 6 0.43 0.011699721 4.2012772 0.999999994 0.491922171 17.89978277
SP_PIR_KEYWORDS amino-acid biosynthesis 6 0.43 0.013040062 4.090732043 0.998368949 0.163302362 17.1738306
GOTERM_BP_FAT GO:0051971~positive regulation of transmission of nerve impulse 6 0.43 0.015098146 3.954982051 1 0.27725219 23.95304816
KEGG_PATHWAY mmu00910:Nitrogen metabolism 6 0.43 0.016981747 3.83812709 0.937629919 0.29307712 18.71855088
INTERPRO IPR000233:Cadherin cytoplasmic region 6 0.43 0.01728773 3.835948747 1 0.562332812 25.34222304
GOTERM_BP_FAT GO:0031646~positive regulation of neurological system process 6 0.43 0.021749788 3.625400213 1 0.325272426 32.68504327
GOTERM_MF_FAT GO:0005544~calcium-dependent phospholipid binding 6 0.43 0.021808944 3.622682661 0.999999995 0.404219803 28.97586019
GOTERM_MF_FAT GO:0051119~sugar transmembrane transporter activity 6 0.43 0.021808944 3.622682661 0.999999995 0.404219803 28.97586019
SMART SM00210:TSPN 6 0.43 0.021849246 3.588524268 0.998733662 0.426483637 25.58465168
INTERPRO IPR017946:PLC-like phosphodiesterase, TIM beta/alpha-barrel domain 6 0.43 0.024403376 3.529072848 1 0.639603639 33.90285138
GOTERM_BP_FAT GO:0002064~epithelial cell development 6 0.43 0.025689512 3.480384205 1 0.352368017 37.40097955
GOTERM_BP_FAT GO:0060078~regulation of postsynaptic membrane potential 6 0.43 0.025689512 3.480384205 1 0.352368017 37.40097955
INTERPRO IPR001315:Caspase Recruitment 6 0.43 0.028573056 3.393339277 1 0.662464365 38.48104976
UP_SEQ_FEATURE lipid moiety-binding region:GPI-anchor amidated glycine 6 0.43 0.02900162 3.365756303 1 0.93657649 41.02446943
UP_SEQ_FEATURE metal ion-binding site:Divalent metal cation 1 6 0.43 0.034106039 3.23112605 1 0.92954754 46.34611502
UP_SEQ_FEATURE metal ion-binding site:Divalent metal cation 2 6 0.43 0.034106039 3.23112605 1 0.92954754 46.34611502
GOTERM_BP_FAT GO:0007435~salivary gland morphogenesis 6 0.43 0.034865751 3.222577968 1 0.40841052 47.20422135
GOTERM_BP_FAT GO:0010811~positive regulation of cell-substrate adhesion 6 0.43 0.034865751 3.222577968 1 0.40841052 47.20422135
GOTERM_BP_FAT GO:0046456~icosanoid biosynthetic process 6 0.43 0.034865751 3.222577968 1 0.40841052 47.20422135
GOTERM_CC_FAT GO:0043197~dendritic spine 6 0.43 0.038702458 3.132656896 0.999999527 0.356839461 41.96255588
UP_SEQ_FEATURE domain:EGF-like 8 6 0.43 0.039732942 3.106851972 1 0.951198628 51.68585757
UP_SEQ_FEATURE repeat:LRR 18 6 0.43 0.039732942 3.106851972 1 0.951198628 51.68585757
GOTERM_BP_FAT GO:0006636~unsaturated fatty acid biosynthetic process 6 0.43 0.040122475 3.107485897 1 0.436984226 52.1471268
GOTERM_MF_FAT GO:0005272~sodium channel activity 6 0.43 0.040225858 3.105156566 1 0.531923298 47.11651119
SP_PIR_KEYWORDS sodium channel 6 0.43 0.042126855 3.068049032 0.999999999 0.327736042 46.08998894
GOTERM_CC_FAT GO:0005901~caveola 6 0.43 0.044468279 3.020776292 0.999999949 0.38094818 46.58122975
GOTERM_BP_FAT GO:0048489~synaptic vesicle transport 6 0.43 0.045834251 3.000331211 1 0.462223656 57.02109019
SP_PIR_KEYWORDS vitamin a 5 0.35 0.001315711 8.948476343 0.474710787 0.031677721 1.872246759
INTERPRO IPR003663:Sugar/inositol transporter 5 0.35 0.0047548 6.68385009 0.999537217 0.283829513 7.676814096
GOTERM_BP_FAT GO:0032651~regulation of interleukin-1 beta production 5 0.35 0.009677134 5.57753879 1 0.228508344 16.05631539
INTERPRO IPR003585:Neurexin/syndecan/glycophorin C 5 0.35 0.012243936 5.2515965 0.999999998 0.495592111 18.65415507
GOTERM_BP_FAT GO:0051057~positive regulation of small GTPase mediated signal transduction 5 0.35 0.016547501 4.833866951 1 0.290961498 25.94226248
GOTERM_BP_FAT GO:0007405~neuroblast proliferation 5 0.35 0.016547501 4.833866951 1 0.290961498 25.94226248
GOTERM_BP_FAT GO:0032760~positive regulation of tumor necrosis factor production 5 0.35 0.016547501 4.833866951 1 0.290961498 25.94226248
BIOCARTA m_cblPathway:CBL mediated ligand-induced downregulation of EGF receptors 5 0.35 0.016629801 4.646825397 0.942204064 0.759592146 18.50158938
SP_PIR_KEYWORDS Growth factor binding 5 0.35 0.0173074 4.772520716 0.999804003 0.196604406 22.16891462
SMART SM00294:4.1m 5 0.35 0.020826479 4.485655335 0.998263729 0.438879492 24.53706422
SP_PIR_KEYWORDS leucine zipper 5 0.35 0.021828284 4.474238172 0.999979442 0.221964099 27.15407805
INTERPRO IPR006212:Furin-like repeat 5 0.35 0.024746657 4.324844176 1 0.635493561 34.29154049
UP_SEQ_FEATURE domain:VWFA 1 5 0.35 0.026723567 4.207195378 1 0.933687939 38.49199053
GOTERM_BP_FAT GO:0002673~regulation of acute inflammatory response 5 0.35 0.031482272 4.028222459 1 0.387472914 43.77176284
INTERPRO IPR001148:Carbonic anhydrase, alpha-class, catalytic domain 5 0.35 0.036193142 3.869597421 1 0.709821796 46.08724443
INTERPRO IPR015590:Aldehyde dehydrogenase 5 0.35 0.036193142 3.869597421 1 0.709821796 46.08724443
INTERPRO IPR000867:Insulin-like growth factor-binding protein, IGFBP 5 0.35 0.036193142 3.869597421 1 0.709821796 46.08724443
INTERPRO IPR016162:Aldehyde dehydrogenase, N-terminal 5 0.35 0.036193142 3.869597421 1 0.709821796 46.08724443
GOTERM_BP_FAT GO:0042417~dopamine metabolic process 5 0.35 0.037767209 3.816210751 1 0.424807834 49.98911269
GOTERM_BP_FAT GO:0048265~response to pain 5 0.35 0.037767209 3.816210751 1 0.424807834 49.98911269
GOTERM_BP_FAT GO:0060444~branching involved in mammary gland duct morphogenesis 5 0.35 0.037767209 3.816210751 1 0.424807834 49.98911269
PIR_SUPERFAMILY PIRSF002504:cadherin 5 0.35 0.039541928 3.756232687 1 0.999994679 44.90635307
UP_SEQ_FEATURE domain:IGFBP N-terminal 5 0.35 0.039943495 3.739729225 1 0.948202987 51.87557736
SMART SM00261:FU 5 0.35 0.040965797 3.694069099 0.999996735 0.524349389 42.85178352
GOTERM_BP_FAT GO:0042558~pteridine and derivative metabolic process 5 0.35 0.044717944 3.625400213 1 0.458271695 56.1070186
GOTERM_BP_FAT GO:0060079~regulation of excitatory postsynaptic membrane potential 5 0.35 0.044717944 3.625400213 1 0.458271695 56.1070186
GOTERM_BP_FAT GO:0048145~regulation of fibroblast proliferation 5 0.35 0.044717944 3.625400213 1 0.458271695 56.1070186
UP_SEQ_FEATURE domain:TSP N-terminal 5 0.35 0.047718104 3.542901371 1 0.966015384 58.40828416
UP_SEQ_FEATURE domain:CARD 5 0.35 0.047718104 3.542901371 1 0.966015384 58.40828416
GOTERM_BP_FAT GO:0060662~salivary gland cavitation 4 0.28 0.002932338 11.60128068 0.999845404 0.119415627 5.148321504
GOTERM_BP_FAT GO:0060605~tube lumen cavitation 4 0.28 0.002932338 11.60128068 0.999845404 0.119415627 5.148321504
GOTERM_BP_FAT GO:0035095~behavioral response to nicotine 4 0.28 0.005564758 9.667733903 0.999999943 0.176245675 9.555890462
UP_SEQ_FEATURE repeat:BNR 5 4 0.28 0.006876255 8.97535014 0.999999998 0.714291212 11.64445981
UP_SEQ_FEATURE repeat:BNR 4 4 0.28 0.011373074 7.693157263 1 0.794496081 18.55377841
INTERPRO IPR003608:MIR 4 0.28 0.019280538 6.535320088 1 0.591850805 27.83929952
INTERPRO IPR007421:ATPase associated with various cellular activities, AAA-4 4 0.28 0.019280538 6.535320088 1 0.591850805 27.83929952
INTERPRO IPR016093:MIR motif 4 0.28 0.019280538 6.535320088 1 0.591850805 27.83929952
GOTERM_BP_FAT GO:0035094~response to nicotine 4 0.28 0.019990445 6.445155935 1 0.31577961 30.47243522
GOTERM_MF_FAT GO:0016918~retinal binding 4 0.28 0.020028506 6.44032473 0.999999977 0.394878848 26.94322765
GOTERM_MF_FAT GO:0005355~glucose transmembrane transporter activity 4 0.28 0.020028506 6.44032473 0.999999977 0.394878848 26.94322765
SP_PIR_KEYWORDS hydroxyproline 4 0.28 0.020711488 6.363360955 0.999964082 0.216256216 25.95100448
UP_SEQ_FEATURE repeat:BNR 3 4 0.28 0.024399107 5.98356676 1 0.936704415 35.80230166
SMART SM00472:MIR 4 0.28 0.029243151 5.582148861 0.999871956 0.498158992 32.76702508
UP_SEQ_FEATURE repeat:BNR 2 4 0.28 0.032966693 5.385210084 1 0.933134131 45.19915464
UP_SEQ_FEATURE repeat:BNR 1 4 0.28 0.032966693 5.385210084 1 0.933134131 45.19915464
INTERPRO IPR019742:Alpha-2-macroglobulin, conserved site 4 0.28 0.034205784 5.347080072 1 0.704449021 44.1936317
GOTERM_BP_FAT GO:0046325~negative regulation of glucose import 4 0.28 0.035417825 5.273309401 1 0.410320801 47.74515005
GOTERM_BP_FAT GO:0040036~regulation of fibroblast growth factor receptor signaling pathway 4 0.28 0.035417825 5.273309401 1 0.410320801 47.74515005
GOTERM_BP_FAT GO:0051181~cofactor transport 4 0.28 0.035417825 5.273309401 1 0.410320801 47.74515005
GOTERM_MF_FAT GO:0051184~cofactor transporter activity 4 0.28 0.03548281 5.269356598 1 0.502253602 42.91256921
INTERPRO IPR004170:WWE domain 4 0.28 0.043356958 4.901490066 1 0.727009475 52.42362327
GOTERM_BP_FAT GO:0010829~negative regulation of glucose transport 4 0.28 0.044863593 4.833866951 1 0.456413699 56.22731424
GOTERM_BP_FAT GO:0042423~catecholamine biosynthetic process 4 0.28 0.044863593 4.833866951 1 0.456413699 56.22731424
GOTERM_BP_FAT GO:0060135~maternal process involved in female pregnancy 4 0.28 0.044863593 4.833866951 1 0.456413699 56.22731424
GOTERM_BP_FAT GO:0050433~regulation of catecholamine secretion 4 0.28 0.044863593 4.833866951 1 0.456413699 56.22731424
INTERPRO IPR015395:C-myb, C-terminal 3 0.21 0.013214975 14.7044702 1 0.51050196 19.9840117
GOTERM_MF_FAT GO:0050543~icosatetraenoic acid binding 3 0.21 0.013588205 14.49073064 0.999993135 0.318541335 19.12710732
GOTERM_MF_FAT GO:0050542~icosanoid binding 3 0.21 0.013588205 14.49073064 0.999993135 0.318541335 19.12710732
GOTERM_MF_FAT GO:0050544~arachidonic acid binding 3 0.21 0.013588205 14.49073064 0.999993135 0.318541335 19.12710732
SP_PIR_KEYWORDS kinase-related transforming protein 3 0.21 0.013922032 14.31756215 0.998946574 0.169136205 18.2300319
INTERPRO IPR018123:WWE domain, subgroup 3 0.21 0.025241547 11.02835265 1 0.62492249 34.84811126
UP_SEQ_FEATURE domain:HTH myb-type 3 3 0.21 0.029849625 10.09726891 1 0.936139395 41.94178326
UP_SEQ_FEATURE domain:WWE 2 3 0.21 0.029849625 10.09726891 1 0.936139395 41.94178326
UP_SEQ_FEATURE domain:WWE 1 3 0.21 0.029849625 10.09726891 1 0.936139395 41.94178326
UP_SEQ_FEATURE site:Essential for catalytic activity 3 0.21 0.029849625 10.09726891 1 0.936139395 41.94178326
SMART SM00678:WWE 3 0.21 0.033995505 9.419876204 0.999970913 0.501597552 37.03885002
INTERPRO IPR006581:VPS10 3 0.21 0.040188927 8.822682119 1 0.726297054 49.71316199
INTERPRO IPR000699:Intracellular calcium-release channel 3 0.21 0.040188927 8.822682119 1 0.726297054 49.71316199
INTERPRO IPR013662:RyR and IP3R Homology associated 3 0.21 0.040188927 8.822682119 1 0.726297054 49.71316199
GOTERM_BP_FAT GO:0040037~negative regulation of fibroblast growth factor receptor signaling pathway 3 0.21 0.041215164 8.700960512 1 0.442857115 53.11815777
GOTERM_BP_FAT GO:0070391~response to lipoteichoic acid 3 0.21 0.041215164 8.700960512 1 0.442857115 53.11815777
SP_PIR_KEYWORDS neurotransmitter 3 0.21 0.042232606 8.590537289 0.999999999 0.323450895 46.17536593
PIR_SUPERFAMILY PIRSF005322:glucose transport protein 3 0.21 0.042371639 8.564210526 1 0.999831397 47.25668446
UP_SEQ_FEATURE region of interest:Nonhelical region 1 (NC1) 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598
UP_SEQ_FEATURE region of interest:Nonhelical region 4 (NC4) 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598
UP_SEQ_FEATURE domain:HTH myb-type 2 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598
UP_SEQ_FEATURE region of interest:Nonhelical region 2 (NC2) 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598
UP_SEQ_FEATURE region of interest:Nonhelical region 3 (NC3) 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598
UP_SEQ_FEATURE domain:HTH myb-type 1 3 0.21 0.047323636 8.077815126 1 0.967774186 58.09807598

Cite This Work



Recommendation
EssayHub’s Community of Professional Tutors & Editors
Tutoring Service, EssayHub
Professional Essay Writers for Hire
Essay Writing Service, EssayPro
Professional Custom
Professional Custom Essay Writing Services
In need of qualified essay help online or professional assistance with your research paper?
Browsing the web for a reliable custom writing service to give you a hand with college assignment?
Out of time and require quick and moreover effective support with your term paper or dissertation?