Architecture of the human regulatory network derived from ENCODE data.

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Natural variation in four human collagen genes across an ethnically diverse population.

Collagens are members of one of the most important families of structural proteins in higher organisms. There are 28 types of collagens encoded by 43 genes in humans that fall into several different functional protein classes. Mutations in the major fibrillar collagen genes lead to osteogenesis imperfecta (COL1A1 and COL1A2 encoding the chains of Type I collagen), chondrodysplasias (COL2A1 encoding the chains of Type II collagen), and vascular Ehlers-Danlos syndrome (COL3A1 encoding the chains of Type III collagen). Over the past 2 decades, mutations in these collagen genes have been catalogued, in hopes of understanding the molecular etiology of diseases caused by these mutations, characterizing the genotype-phenotype relationships, and developing robust models predicting the molecular and clinical outcomes. To achieve these goals better, it is necessary to understand the natural patterns of variation in collagen genes in human populations. We screened exons, flanking intronic regions, and conserved noncoding regions for variations in COL1A1, COL1A2, COL2A1, and COL3A1 in 48 individuals from each of four ethnically diverse populations. We identified 459 single-nucleotide polymorphisms (SNPs), more than half of which were novel and not found in public databases. Of the 52 SNPs found in coding regions, 15 caused amino acid substitutions while 37 did not. Although the four collagens have similar gene and protein structures, they have different molecular evolutionary characteristics. For example, COL1A1 appears to have been under substantially stronger negative selection than the rest. Phylogenetic analysis also suggests that the four genes have very different evolutionary histories among the different ethnic groups. Our observations suggest that the study of collagen mutations and their relationships with disease phenotypes should be performed in the context of the genetic background of the subjects.