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Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

ABSTRACT: Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.

SUBMITTER: Carrillo-de-Santa-Pau E

PROVIDER: S-EPMC5716146 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

Carrillo-de-Santa-Pau Enrique E Juan David D Pancaldi Vera V Were Felipe F Martin-Subero Ignacio I Rico Daniel D Valencia Alfonso A

Nucleic acids research 20170901 16

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspec ...[more]

PMID: 28934481

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Project description:Human blood develops from self-renewing hematopoietic stem cells to terminal lineages and necessitates regulator and effector gene expression changes; each cell type specifically expresses a subset of genes to carry out a specific function. Gene expression changes coincide with histone modification, histone variant deposition, and recruitment of transcription-related enzymes to specific genetic loci. Transcriptional regulation has been mostly studied using in vitro systems while epigenetic changes occurring during in vivo development remain poorly understood.By integrating previously published and novel global expression profiles from human CD34+/CD133+ hematopoietic stem and progenitor cells (HSPCs), in vivo differentiated human CD4+ T-cells and CD19+ B-cells, and in vitro differentiated CD36+ erythrocyte precursors, we identified hundreds of transcripts specifically expressed in each cell type. To relate concurrent epigenomic changes to expression, we examined genome-wide distributions of H3K4me1, H3K4me3, H3K27me1, H3K27me3, histone variant H2A.Z, ATP-dependent chromatin remodeler BRG1, and RNA Polymerase II in these cell types, as well as embryonic stem cells. These datasets revealed that numerous differentiation genes are primed for subsequent downstream expression by BRG1 and PolII binding in HSPCs, as well as the bivalent H3K4me3 and H3K27me3 modifications in the HSPCs prior to their expression in downstream, differentiated cell types; much HSPC bivalency is retained from embryonic stem cells. After differentiation, bivalency resolves to active chromatin configuration in the specific lineage, while it remains in parallel differentiated lineages. PolII and BRG1 are lost in closer lineages; bivalency resolves to silent monovalency in more distant lineages. Correlation of expression with epigenomic changes predicts tens of thousands of potential common and tissue-specific enhancers, which may contribute to expression patterns and differentiation pathways.Several crucial lineage factors are bivalently prepared for their eventual expression or repression. Bivalency is not only resolved during differentiation but is also established in a step-wise manner in differentiated cell types. We note a progressive, specific silencing of alternate lineage genes in certain cell types coinciding with H3K27me3 enrichment, though expression silencing is maintained in its absence. Globally, the expression of type-specific genes across many cell types correlates strongly with their epigenetic profiles. These epigenomic data appear useful for further understanding mechanisms of differentiation and function of human blood lineages.

Project description:Prostate cancer (PCa) is a common malignancy and a leading cause of cancer death among men in the United States with African-American (AA) men having the highest incidence and mortality rates. Given recent results from admixture mapping and genome-wide association studies for PCa in AA men, it is clear that many risk alleles are enriched in men with West African genetic ancestry.A total of 77 ancestry informative markers (AIMs) within surrounding candidate gene regions were genotyped and haplotyped using Pyrosequencing in 358 unrelated men enrolled in a PCa genetic association study at the Howard University Hospital between 2000 and 2004. Sequence analysis of promoter region single-nucleotide polymorphisms (SNPs) to evaluate disruption of transcription factor-binding sites was conducted using in silico methods.Eight AIMs were significantly associated with PCa risk after adjusting for age and West African ancestry. SNP rs1993973 (intervening sequences) had the strongest association with PCa using the log-additive genetic model (P=0.002). SNPs rs1561131 (genotypic, P=0.007), rs1963562 (dominant, P=0.01) and rs615382 (recessive, P=0.009) remained highly significant after adjusting for both age and ancestry. We also tested the independent effect of each significantly associated SNP and rs1561131 (P=0.04) and rs1963562 (P=0.04) remained significantly associated with PCa development. After multiple comparisons testing using the false discovery rate, rs1993973 remained significant. Analysis of the rs156113-, rs1963562-rs615382l and rs1993973-rs585224 haplotypes revealed that the least frequently found haplotypes in this population were significantly associated with a decreased risk of PCa (P=0.032 and 0.0017, respectively).The approach for SNP selection utilized herein showed that AIMs may not only leverage increased linkage disequilibrium in populations to identify risk and protective alleles, but may also be informative in dissecting the biology of PCa and other health disparities.

Dataset Information

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

Publications

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

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