Mouse models of type 1 and type 2 diabetes derived from the same closed colony: genetic susceptibility shared between two types of diabetes

Except for rare subtypes of diabetes, both type 1 and type 2 diabetes are multifactorial diseases in which genetic factors consisting of multiple susceptibility genes and environmental factors contribute to the disease development. Due to complex interaction among multiple susceptibility genes and between genetic and environmental factors, genetic analysis of multifactorial diseases is difficult in humans. Inbred animal models, in which the genetic background is homogeneous and environmental factors can be controlled, are therefore valuable in genetic dissection of multifactorial diseases. We are fortunate to have excellent animal models for both type 1 and type 2 diabetes--the nonobese diabetic (NOD) mouse and the Nagoya-Shibata-Yasuda (NSY) mouse, respectively. Congenic mapping of susceptibility genes for type 1 diabetes in the NOD mouse has revealed that susceptibility initially mapped as a single locus often consists of multiple components on the same chromosome, indicating the importance of congenic mapping in defining genes responsible for polygenic diseases. The NSY mouse is an inbred animal model of type 2 diabetes established from Jcl:ICR, from which the NOD mouse was also derived. We have recently mapped three major loci contributing to type 2 diabetes in the NSY mouse. Interestingly, support intervals where type 2 diabetes susceptibility genes were mapped in the NSY mouse overlapped the regions where type 1 diabetes susceptibility genes have been mapped in the NOD mouse. Although additional evidence is needed, it may be possible that some of the genes predisposing to diabetes are derived from a common ancestor contained in the original closed colony, contributing to type 1 diabetes in the NOD mouse and type 2 diabetes in the NSY mouse. Such genes, if they exist, will provide valuable information on etiological pathways common to both forms of diabetes, for the establishment of effective methods for prediction, prevention, and intervention in both type 1 and type 2 diabetes.     

牛疾病相关基因的DNA变异及遗传控制

牛基因组中一些重要基因的DNA突变通过改变基因的表达和蛋白质功能来影响机体对疾病的抗性或易感性。控制牛疾病的DNA变异主要分为单基因座及多基因座两类。导致疾病的单基因座类型亦称因果突变,其遗传基础较简单,突变一般位于基因编码区或非编码区,多为单碱基或少数几个碱基的突变,这些突变导致氨基酸的错义突变、翻译提前终止或部分外显子缺失等。相比而言,多基因相关疾病的遗传基础较为复杂,遗传-病原体-环境间的互作是导致这类复杂疾病的主要原因。文章综述了由单基因座和多基因座遗传变异所控制的牛主要疾病的研究和应用现状,以及在牛育种及生产中为降低这些疾病的发生所采用的遗传控制策略。     

A human genome map of comparative anchor tagged sequences.

Effective comparative mapping inference utilizing developing gene maps of animal species requires the inclusion of anchored reference loci that are homologous to genes mapped in the more "gene-dense" mouse and human maps. Nominated anchor loci, termed comparative anchor tagged sequences (CATS), have been ordered in the mouse linkage map, but due to the dearth of common polymorphisms among human coding genes have not been well represented in human linkage maps. We present here an ordered framework map of 314 comparative anchor markers in humans based on mapping analysis in the Genebridge 4 panel of radiation hybrid cell lines, plus empirically optimized CATS PCR primers which detect these markers. The ordering of these homologous gene markers in human and mouse maps provides a framework for comparative gene mapping of representative mammalian species.     

Selecting SNPs for association studies based on population frequencies: a novel interactive tool and its application to polygenic diseases

Common complex polygenic diseases as autoimmune diseases have not been completely understood on a molecular level. While many genes are known to be involved in the pathways responsible for the phenotype, explicit causes for the susceptibility of the disease remain to be elucidated. The susceptibility to disease is thought to be the result of genetic epistatic interactions between common polymorphic genes. This polymorphism is mostly caused by single nucleotide polymorphisms (SNPs). Human subpopulations are known to differ in the susceptibility to the diseases and generally in the distribution of single nucleotide polymorphisms. The here presented approach retrieves SNPs with the most divergent frequencies for selected human subpopulations to help defining properties for the experimental verification of SNPs within defined regions. A web-accessible program implementing this approach was evaluated for multiple sclerosis (MS), a common human polygenic disease. A link to a summary of data from "The SNP Consortium" (TSC) with sex-dependencies of SNPs is available. Associations of SNPs to genes, genetic markers and chromosomal loci are retrieved from the Ensembl project. This tool is recommended to be used in conjunction with microarray analyses or marker association studies that link genes or chromosomal loci to particular diseases.     

Clustered miRNAs and their role in biological functions and diseases

MicroRNAs (miRNAs) are endogenous, small non‐coding RNAs known to regulate expression of protein‐coding genes. A large proportion of miRNAs are highly conserved, localized as clusters in the genome, transcribed together from physically adjacent miRNAs and show similar expression profiles. Since a single miRNA can target multiple genes and miRNA clusters contain multiple miRNAs, it is important to understand their regulation, effects and various biological functions. Like protein‐coding genes, miRNA clusters are also regulated by genetic and epigenetic events. These clusters can potentially regulate every aspect of cellular function including growth, proliferation, differentiation, development, metabolism, infection, immunity, cell death, organellar biogenesis, messenger signalling, DNA repair and self‐renewal, among others. Dysregulation of miRNA clusters leading to altered biological functions is key to the pathogenesis of many diseases including carcinogenesis. Here, we review recent advances in miRNA cluster research and discuss their regulation and biological functions in pathological conditions.     

Presence of multiple independent effects in risk loci of common complex human diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.     

The gene coding the human S11 surface antigens maps between the loci for HPRT and G6PD on the X-chromosome

The human S11 surface antigens are expressed on fibroblasts and are coded by a gene on the X-chromosome. We have regionally mapped this gene by examining S11 expression on a panel of hybrid lines which had fragmented the X-chromosome either during chromosome-mediated gene transfer, or by interspecies translocation during hybrid cell expansion. Using indirect immunofluorescence and the fluorescence-activated cell sorter (FACS), it was possible to isolate antigen-positive and -negative hybrid subpopulations for subsequent genetic analysis. The gene coding S11 could be localized to Xq27–28, between the loci for HPRT and G6PD where genes for the S10 and S12 antigens have been previously mapped. This work demonstrates the value of cell surface antigens and the FACS in somatic cell genetic analysis, and provides evidence for regional clustering of surface antigen loci on the human X-chromosome.     

Evaluation of candidate genes from orphan FEB and GEFS+ loci by analysis of human brain gene expression atlases

Febrile seizures, or febrile convulsions (FEB), represent the most common form of childhood seizures and are believed to be influenced by variations in several susceptibility genes. Most of the associated loci, however, remain 'orphan', i.e. the susceptibility genes they contain still remain to be identified. Further orphan loci have been mapped for a related disorder, genetic (generalized) epilepsy with febrile seizures plus (GEFS+).We show that both spatially mapped and 'traditional' gene expression data from the human brain can be successfully employed to predict the most promising candidate genes for FEB and GEFS+, apply our prediction method to the remaining orphan loci and discuss the validity of the predictions. For several of the orphan FEB/GEFS+ loci we propose excellent, and not always obvious, candidates for mutation screening in order to aid in gaining a better understanding of the genetic origin of the susceptibility to seizures.     

Orthologous microRNA genes are located in cancer-associated genomic regions in human and mouse

Background

MicroRNAs (miRNAs) are short non-coding RNAs that regulate differentiation and development in many organisms and play an important role in cancer.

Methodology/Principal Findings

Using a public database of mapped retroviral insertion sites from various mouse models of cancer we demonstrate that MLV-derived retroviral inserts are enriched in close proximity to mouse miRNA loci. Clustered inserts from cancer-associated regions (Common Integration Sites, CIS) have a higher association with miRNAs than non-clustered inserts. Ten CIS-associated miRNA loci containing 22 miRNAs are located within 10 kb of known CIS insertions. Only one CIS-associated miRNA locus overlaps a RefSeq protein-coding gene and six loci are located more than 10 kb from any RefSeq gene. CIS-associated miRNAs on average are more conserved in vertebrates than miRNAs associated with non-CIS inserts and their human homologs are also located in regions perturbed in cancer. In addition we show that miRNA genes are enriched around promoter and/or terminator regions of RefSeq genes in both mouse and human.

Conclusions/Significance

We provide a list of ten miRNA loci potentially involved in the development of blood cancer or brain tumors. There is independent experimental support from other studies for the involvement of miRNAs from at least three CIS-associated miRNA loci in cancer development.     

MicroRNA与细胞信号转导通路研究进展

成熟的microRNA(miRNA)是一种长约22 nt的非编码RNA,通过与靶基因的3′非翻译区(3′UTR)结合来调控靶基因的表达。直至目前,在不同物种中发现的miRNA达6 397个。miRNA的发现为基因表达调控研究打开了新的窗口。目前研究者不仅证实miRNA在生物体生长、发育和疾病发生等过程中发挥着重要的作用,而且开始进一步探寻其发挥作用的分子机理。综述了miRNA与细胞信号转导途径之间的关系,从而有助于从基因水平上理解疾病的发生机制,为疾病的诊断、治疗提供依据。     

A tripartite clustering analysis on microrna, gene and disease model

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.