Minyi Shi

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.

Variation in Transcription Factor Binding Among Humans

Like Father, Like Mother, Like Child Transcriptional regulation is mediated by chromatin structure, which may affect the binding of transcription factors, but the extent of how individual-to-individual genetic variation affects such regulation is not well understood. Kasowski et al. (p. 232, published online 18 March) investigated the binding of two transcription factors across the genomes of human individuals and one chimpanzee. Transcription factor binding was associated with genomic features such as nucleotide variation, insertions and deletions, and copy number variation. Thus, genomic sequence variation affects transcription factor binding and may explain expression difference among individuals. McDaniell et al. (p. 235, published online 18 March) provide a genome-wide catalog of variation in chromatin and transcription factor binding in two parent-child trios of European and African ancestry. Up to 10% of active chromatin binding sites were specific to a set of individuals and were often inherited. Furthermore, variation in active chromatin sites showed heritable allele-specific correlation with variation in gene expression. Transcription factor binding sites vary among individuals and are correlated with differences in expression. Differences in gene expression may play a major role in speciation and phenotypic diversity. We examined genome-wide differences in transcription factor (TF) binding in several humans and a single chimpanzee by using chromatin immunoprecipitation followed by sequencing. The binding sites of RNA polymerase II (PolII) and a key regulator of immune responses, nuclear factor κB (p65), were mapped in 10 lymphoblastoid cell lines, and 25 and 7.5% of the respective binding regions were found to differ between individuals. Binding differences were frequently associated with single-nucleotide polymorphisms and genomic structural variants, and these differences were often correlated with differences in gene expression, suggesting functional consequences of binding variation. Furthermore, comparing PolII binding between humans and chimpanzee suggests extensive divergence in TF binding. Our results indicate that many differences in individuals and species occur at the level of TF binding, and they provide insight into the genetic events responsible for these differences.

Tcf7 is an important regulator of the switch of self-renewal and differentiation in a multipotential hematopoietic cell line.

A critical problem in biology is understanding how cells choose between self-renewal and differentiation. To generate a comprehensive view of the mechanisms controlling early hematopoietic precursor self-renewal and differentiation, we used systems-based approaches and murine EML multipotential hematopoietic precursor cells as a primary model. EML cells give rise to a mixture of self-renewing Lin-SCA+CD34+ cells and partially differentiated non-renewing Lin-SCA-CD34− cells in a cell autonomous fashion. We identified and validated the HMG box protein TCF7 as a regulator in this self-renewal/differentiation switch that operates in the absence of autocrine Wnt signaling. We found that Tcf7 is the most down-regulated transcription factor when CD34+ cells switch into CD34− cells, using RNA–Seq. We subsequently identified the target genes bound by TCF7, using ChIP–Seq. We show that TCF7 and RUNX1 (AML1) bind to each others promoter regions and that TCF7 is necessary for the production of the short isoforms, but not the long isoforms of RUNX1, suggesting that TCF7 and the short isoforms of RUNX1 function coordinately in regulation. Tcf7 knock-down experiments and Gene Set Enrichment Analyses suggest that TCF7 plays a dual role in promoting the expression of genes characteristic of self-renewing CD34+ cells while repressing genes activated in partially differentiated CD34− state. Finally a network of up-regulated transcription factors of CD34+ cells was constructed. Factors that control hematopoietic stem cell (HSC) establishment and development, cell growth, and multipotency were identified. These studies in EML cells demonstrate fundamental cell-intrinsic properties of the switch between self-renewal and differentiation, and yield valuable insights for manipulating HSCs and other differentiating systems.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA‐sequencing of the corpus callosum from patients with autism exhibited extensive gene mis‐expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

A Large Gene Network in Immature Erythroid Cells Is Controlled by the Myeloid and B Cell Transcriptional Regulator PU.1

PU.1 is a hematopoietic transcription factor that is required for the development of myeloid and B cells. PU.1 is also expressed in erythroid progenitors, where it blocks erythroid differentiation by binding to and inhibiting the main erythroid promoting factor, GATA-1. However, other mechanisms by which PU.1 affects the fate of erythroid progenitors have not been thoroughly explored. Here, we used ChIP-Seq analysis for PU.1 and gene expression profiling in erythroid cells to show that PU.1 regulates an extensive network of genes that constitute major pathways for controlling growth and survival of immature erythroid cells. By analyzing fetal liver erythroid progenitors from mice with low PU.1 expression, we also show that the earliest erythroid committed cells are dramatically reduced in vivo. Furthermore, we find that PU.1 also regulates many of the same genes and pathways in other blood cells, leading us to propose that PU.1 is a multifaceted factor with overlapping, as well as distinct, functions in several hematopoietic lineages.

NF90/ILF3 is a transcription factor that promotes proliferation over differentiation by hierarchical regulation in K562 erythroleukemia cells

NF90 and splice variant NF110 are DNA- and RNA-binding proteins encoded by the Interleukin enhancer-binding factor 3 (ILF3) gene that have been established to regulate RNA splicing, stabilization and export. The roles of NF90 and NF110 in regulating transcription as chromatin-interacting proteins have not been comprehensively characterized. Here, chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) identified 9,081 genomic sites specifically occupied by NF90/NF110 in K562 cells. One third of NF90/NF110 peaks occurred at promoters of annotated genes. NF90/NF110 occupancy colocalized with chromatin marks associated with active promoters and strong enhancers. Comparison with 150 ENCODE ChIP-seq experiments revealed that NF90/NF110 clustered with transcription factors exhibiting preference for promoters over enhancers (POLR2A, MYC, YY1). Differential gene expression analysis following shRNA knockdown of NF90/NF110 in K562 cells revealed that NF90/NF110 activates transcription factors that drive growth and proliferation (EGR1, MYC), while attenuating differentiation along the erythroid lineage (KLF1). NF90/NF110 associates with chromatin to hierarchically regulate transcription factors that promote proliferation and suppress differentiation.