MicroRNA regulation constrains the organization of target genes on mammalian chromosomes.

The regulation of microRNAs (miRNAs) is a complicated process requiring a large number of molecular events to be coordinated in both space and time. It is not known whether this complicated regulation process constrains the organization of target genes on mammalian chromosomes. We performed a genome-wide analysis to provide a better picture of chromosomal organization of miRNA target genes. Our results showed clustering of the target genes (TGs) of miRNAs on mammalian chromosomes, and further revealed that the particular gene organization is constrained by miRNA regulation. The clustering pattern of TGs provides an insight into the complexity of miRNA regulation.

From phenotype to gene: detecting disease-specific gene functional modules via a text-based human disease phenotype network construction.

Currently, some efforts have been devoted to the text analysis of disease phenotype data, and their results indicated that similar disease phenotypes arise from functionally related genes. These related genes work together, as a functional module, to perform a desired cellular function. We constructed a text-based human disease phenotype network and detected 82 disease-specific gene functional modules, each corresponding to a different phenotype cluster, by means of graph-based clustering and mapping from disease phenotype to gene. Since genes in such gene functional modules are functionally related and cause clinically similar diseases, they may share common genetic origin of their associated disease phenotypes. We believe the investigation may facilitate the ultimate understanding of the common pathophysiologic basis of associated diseases.

Role of coxsackievirus and adenovirus receptor in the pathogenesis of dilated cardiomyopathy and its influencing factor.

BACKGROUND: Although clinical treatment for heart failure and sudden death has been improved over the last few decades, the morbidity and mortality of dilated cardiomyopathy (DCM) have increased. So a better understanding of the underlying molecular events leading to DCM is urgent. Persistent viral infection (especially coxsackievirus group B3) of the myocardium in viral myocarditis and DCM has never been neglected by experts. Recent data indicate that the up-regulation of coxsackievirus and adenovirus receptor (CAR) in viral cardiomyopathy contributes to viral infection as a key factor in the pathogenesis of this disease. This study aimed to investigate the role and regulatory mechanism of CAR in DCM by the bioinformatic method. METHODS: We identified the clusters of genes co-expressed with CAR by clustering algorithm based on the public available microarray dataset of DCM (Kittleson, et al. 2005), and mapped these genes into the protein-protein interaction networks to investigate the interaction relationship to each other at the protein level after confirming that the samples are characterized by the cluster of genes in correctly partitioning. RESULTS: The gene cluster GENESET 11 containing 33 genes including CAR with similar expression pattern was identified by cluster algorithm, of which 19 genes were found to have interaction information of the protein encoded by them in the current human protein interaction database. Especially, 12 genes present as critical nodes (called HUB node) at the protein level are involved in energy metabolism, signal transduction, viral infection, immuno-response, cell apoptosis, cell proliferation, tissue repair, etc. CONCLUSIONS: The genes in GENESET 11 together with CAR may play a pathogenic role in the development of DCM, mainly involved in the mechanism of energy metabolism, signal transduction, viral infection, immuno-response, cell apoptosis and tissue repair.

[Relationship between muscarinic acetylcholine receptor subtypes].

Muscarinic acetylcholine receptor belongs to the G-coupled receptor family, the cooperation between its five subtypes is crucially important in maintaining the normal physiological function. At present, many types of biological resources are increasing, which provides unprecedented opportunities to study the relationships between muscarinic acetylcholine receptor subtypes. In this study, we take advantage of different types of data, using bioinformatics tools and strategies, to analyze the relationship between muscarinic acetylcholine receptor subtypes from four aspects, including evolution, sequence similarity, expression correlation and protein-protein interaction network. From evolution and sequence similarity aspects, we found the five subtypes of muscarinic acetylcholine receptor can be classified into two subclasses. The first subclass includes M1, M3, and M5, and the second subclass includes M2 and M4. The evolutionary distance between two subtypes of the same subclass is relatively near, and the sequence similarity between them is relatively high. From expression correlation aspect, we found that the subtypes of the first subclass have a positive correlation with the subtypes of the second subclass, that is, there are potential co-expression relationships between them. From protein-protein interaction aspect, we found that the subtypes of the first subclass have indirect interactions with the subtypes of the second subclass, which indicates cooperation relationships.

Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes.

Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges.

Familial aggregation analysis of gene expressions.

Traditional studies of familial aggregation are aimed at defining the genetic (and non-genetic) causes of a disease from physiological or clinical traits. However, there has been little attempt to use genome-wide gene expressions, the direct phenotypic measures of genes, as the traits to investigate several extended issues regarding the distributions of familially aggregated genes on chromosomes or in functions. In this study we conducted a genome-wide familial aggregation analysis by using the in vitro cell gene expressions of 3300 human autosome genes (Problem 1 data provided to Genetic Analysis Workshop 15) in order to answer three basic genetics questions. First, we investigated how gene expressions aggregate among different types (degrees) of relative pairs. Second, we conducted a bioinformatics analysis of highly familially aggregated genes to see how they are distributed on chromosomes. Third, we performed a gene ontology enrichment test of familially aggregated genes to find evidence to support their functional consensus. The results indicated that 1) gene expressions did aggregate in families, especially between sibs. Of 3300 human genes analyzed, there were a total of 1105 genes with one or more significant (empirical p < 0.05) familial correlation; 2) there were several genomic hot spots where highly familially aggregated genes (e.g., the chromosome 6 HLA genes cluster) were clustered; 3) as we expected, gene ontology enrichment tests revealed that the 1105 genes were aggregating not only in families but also in functional categories.

Novel strategies to identify relevant molecular signatures for complex human diseases based on data of identical-by-decent profiles and genomic context.

OBJECTIVE: To develop novel strategies to identify relevant molecular signatures for complex human diseases based on data of identical-by-decent profiles and genomic context. METHODS: In the proposed strategies, we define four relevancy criteria for mapping SNP-phenotype relationships-point-wise IBD mean difference, averaged IBD difference for window, Z curve and averaged slope for window. RESULTS: Application of these criteria and permutation test to 100 simulated replicates for two hypothetical American populations to extract the relevant SNPs for alcoholism based on sib-pair IBD profiles of pedigrees demonstrates that the proposed strategies have successfully identified most of the simulated true loci. CONCLUSION: The data mining practice implies that IBD statistic and genomic context could be used as the informatics for locating the underlying genes for complex human diseases. Compared with the classical Haseman-Elston sib-pair regression method, the proposed strategies are more efficient for large-scale genomic mining.