Systems biology in heterogenous tissues: Integrating multiple ∗omics datasets to understand hematopoietic differentiation

Jens Lichtenberg, Guanjue Xiang, Elisabeth Heuston, Belinda Giardine, Cheryl A. Keller, Ross C. Hardison, David M. Bodine, Yu Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Motivation: Systems biology integrates expression, methylation, transcription factor binding and histone modification profiles with other physiological characteristics of a specific organ. Repositories that provide the required data, like ENCODE, generally work on a high level and do not take the heterogeneity of cell types within an organ into consideration. The hematopoietic system allows the characterization and study of each cell type involved in the generation of blood cells from bone marrow stem cells and thus provides a good foundation for systems biology studies. Here we compare RNA expression, DNA methylation, chromatin accessibility, DNA binding proteins and histone modification profiles in seven different hematopoietic populations using a Bayesian non-parametric hierarchical latent-class mixed-effect model known as IDEAS to characterize epigenetic changes associated with hematopoietic differentiation. Unlike other existing approaches IDEAS considers various cell types of a biological systems in concert instead of disjointly. Results: Using the VISION database and the IDEAS toolkit we provide insights into the transcriptional, epigenetic and regulatory programs governing the hematopoietic differentiation process. The characterization of the different hematopoietic components and their interactions provide the foundations for a systems biology model of hematopoiesis. Previous hematopoietic epigenome segmentation studies have focused on histone modifications, chromatin accessibility and DNA binding protein profiles. DNA methylation has been shown to vary markedly in hematopoietic populations. Inclusion of DNA methylation in these segmentation studies increased the original 36-state model of regulatory interactions to 41 states. These new DNA methylation-related states were associated with repressive marks, active RNA transcription, and a novel state regulated by DNA methylation alone. Imputing epigenetic models on inputs systematically perturbed for hematopoietic populations resulted in models of varying degrees of overlap, which were quantified and set in context with underlying biological processes. Conclusion: Our data show that methylation has a strong impact on functional genomic modeling and can be used to discern cell type specific epigenetic regulatory behavior by leveraging imputation for missing cell type data.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages240-243
Number of pages4
ISBN (Electronic)9781728146171
DOIs
StatePublished - Oct 2019
Event19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 - Athens, Greece
Duration: Oct 28 2019Oct 30 2019

Publication series

NameProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019

Conference

Conference19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Country/TerritoryGreece
CityAthens
Period10/28/1910/30/19

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biomedical Engineering
  • Health Informatics

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