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BMC Syst Biol. 2015 Nov 6;9:75. doi: 10.1186/s12918-015-0225-4.

A multi-omic analysis of human naïve CD4+ T cells.

Author information

1
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. cmitch48@jhmi.edu.
2
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. dgetnet1@jhmi.edu.
3
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. mskim@jhmi.edu.
4
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. srikanth@ibioinformatics.org.
5
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. praveen@ibioinformatics.org.
6
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. huang@jhmi.edu.
7
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. pinto@jhmi.edu.
8
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. raja@ibioinformatics.org.
9
Department of Molecular & Cellular BioAnalysis, Kyoto University, Kyoto, Japan. omio13@gmail.com.
10
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. pshaw@jhsph.edu.
11
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. xinyan@jhmi.edu.
12
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jzhong@jhmi.edu.
13
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. raghothama@jhmi.edu.
14
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. arivusudar@ibioinformatics.org.
15
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. babylakshmi@ibioinformatics.org.
16
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. nandini@ibioinformatics.org.
17
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. rajesh@ibioinformatics.org.
18
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. cbowma14@jhmi.edu.
19
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ludmila.danilova@gmail.com.
20
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jevon@jhmi.edu.
21
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. dhanashree@ibioinformatics.org.
22
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. cdrake@jhmi.edu.
23
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. keshav@ibioinformatics.org.
24
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. marchion@gmail.com.
25
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. pmurakam@jhsph.edu.
26
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. afscott@jhmi.edu.
27
National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA. leming.shi@fda.hhs.gov.
28
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA. mieg@ncbi.nlm.nih.gov.
29
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA, USA. rafa@jhu.edu.
30
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Lcope1@jhmi.edu.
31
Department of Molecular & Cellular BioAnalysis, Kyoto University, Kyoto, Japan. yishiham@pharm.kyoto-u.ac.jp.
32
Center for Genomics and Division of Microbiology & Molecular Genetics, Loma Linda University, Loma Linda, CA, USA. chwang@llu.edu.
33
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. harsha@bioinformatics.org.
34
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. pandey@jhmi.edu.
35
Institute of Bioinformatics, International Tech Park, Whitefield, Bangalore, India. pandey@jhmi.edu.
36
Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD, USA. pandey@jhmi.edu.
37
Department of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. pandey@jhmi.edu.

Abstract

BACKGROUND:

Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.

RESULTS:

Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome.

CONCLUSIONS:

We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.

PMID:
26542228
PMCID:
PMC4636073
DOI:
10.1186/s12918-015-0225-4
[Indexed for MEDLINE]
Free PMC Article

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