Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

BackgroundA healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.ResultsWe apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers.ConclusionsOur data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.

[1]  S. Hannenhalli,et al.  Determinants of expression variability , 2014, Nucleic acids research.

[2]  P. Laird,et al.  Low-level processing of Illumina Infinium DNA Methylation BeadArrays , 2013, Nucleic acids research.

[3]  K. Kaneko,et al.  Fluctuation and response in biology , 2010, Cellular and Molecular Life Sciences.

[4]  H. Simon,et al.  Peculiarities of cell death mechanisms in neutrophils , 2011, Cell Death and Differentiation.

[5]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[6]  Pornpimol Charoentong,et al.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks , 2009, Bioinform..

[7]  John Quackenbush,et al.  Variance of Gene Expression Identifies Altered Network Constraints in Neurological Disease , 2011, PLoS genetics.

[8]  M. Hemler.  Tetraspanin functions and associated microdomains , 2005, Nature Reviews Molecular Cell Biology.

[9]  Технология,et al.  National Climatic Data Center , 2011 .

[10]  N. Rose,et al.  Sex differences in autoimmune disease from a pathological perspective. , 2008, The American journal of pathology.

[11]  A. Valencia,et al.  Automatic identification of informative regions with epigenomic changes associated to hematopoiesis , 2017, bioRxiv.

[12]  J. Brugge,et al.  Sensing the environment: a historical perspective on integrin signal transduction , 2002, Nature Cell Biology.

[13]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[14]  W. Dröge,et al.  Role of cysteine and glutathione in HIV infection and other diseases associated with muscle wasting and immunological dysfunction , 1997, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[15]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[16]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[17]  A. Feinberg,et al.  Regulated Noise in the Epigenetic Landscape of Development and Disease , 2012, Cell.

[18]  F. Berditchevski.  Complexes of tetraspanins with integrins: more than meets the eye. , 2001, Journal of cell science.

[19]  Matthew T. Maurano,et al.  Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells , 2016, Cell.

[20]  E. Bonifacio,et al.  Widespread seasonal gene expression reveals annual differences in human immunity and physiology , 2015, Nature Communications.

[21]  R. Salgia,et al.  Fyn: a novel molecular target in cancer. , 2010, Cancer.

[22]  A. Valencia,et al.  Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia , 2015, Genome Medicine.

[23]  H. Kitchener,et al.  Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation , 2012, Genome Medicine.

[24]  Vasyl Pihur,et al.  Gene expression anti-profiles as a basis for accurate universal cancer signatures , 2012, BMC Bioinformatics.

[25]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[26]  Yu Liu,et al.  Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility , 2010, PLoS Comput. Biol..

[27]  J. P. McCoy,et al.  Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. , 2016, Immunity.

[28]  A. Oshlack,et al.  SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips , 2012, Genome Biology.

[29]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[30]  J. Knight,et al.  Genomic modulators of gene expression in human neutrophils , 2015, Nature Communications.

[31]  Cory Y. McLean,et al.  GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.

[32]  I. Amit,et al.  Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors , 2015, Cell.

[33]  E. Dermitzakis,et al.  Tissue-Specific Effects of Genetic and Epigenetic Variation on Gene Regulation and Splicing , 2015, PLoS genetics.

[34]  J. Raser,et al.  Noise in Gene Expression: Origins, Consequences, and Control , 2005, Science.

[35]  Michael J. Ziller,et al.  Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. , 2014, Cancer cell.

[36]  S. Swain,et al.  Age-associated increase in lifespan of naïve CD4 T cells contributes to T-cell homeostasis but facilitates development of functional defects , 2009, Proceedings of the National Academy of Sciences.

[37]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[38]  R. Andrews,et al.  Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression , 2014, Science.

[39]  Santa Jeremy Ono,et al.  A novel cysteine-rich sequence-specific DNA-binding protein interacts with the conserved X-box motif of the human major histocompatibility complex class II genes via a repeated Cys-His domain and functions as a transcriptional repressor , 1994, The Journal of experimental medicine.

[40]  A. Regev,et al.  Writ large: Genomic dissection of the effect of cellular environment on immune response , 2016, Science.

[41]  David S. Harburger,et al.  Integrin signalling at a glance , 2009, Journal of Cell Science.

[42]  Manolis Kellis,et al.  ChromHMM: automating chromatin-state discovery and characterization , 2012, Nature Methods.

[43]  A. Tarakhovsky,et al.  Epigenetic control of immunity. , 2014, Cold Spring Harbor perspectives in biology.

[44]  Young-Joon Kim,et al.  Intrinsic variability of gene expression encoded in nucleosome positioning sequences , 2009, Nature Genetics.

[45]  M. Blackman,et al.  T-cell immunosenescence: lessons learned from mouse models of aging , 2009, Trends in Immunology.

[46]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[47]  Christofer L. Bäcklin,et al.  Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia , 2013, Genome Biology.

[48]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[49]  B. Pugh,et al.  Identification and Distinct Regulation of Yeast TATA Box-Containing Genes , 2004, Cell.

[50]  A. Valencia,et al.  Searching for the chromatin determinants of human hematopoiesis , 2016 .

[51]  M. Gerstein,et al.  RNA expression patterns change dramatically in human neutrophils exposed to bacteria. , 2001, Blood.

[52]  Farren J. Isaacs,et al.  Phenotypic consequences of promoter-mediated transcriptional noise. , 2006, Molecular cell.

[53]  M. Ross,et al.  Meta-analysis of IDH-mutant cancers identifies EBF1 as an interaction partner for TET2 , 2013, Nature Communications.

[54]  B. Snijder,et al.  Origins of regulated cell-to-cell variability , 2011, Nature Reviews Molecular Cell Biology.

[55]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[56]  G. Gilkeson,et al.  ETS transcription factors and regulation of immunity , 2006, Archivum Immunologiae et Therapiae Experimentalis.

[57]  Luonan Chen,et al.  The Dynamics of DNA Methylation Covariation Patterns in Carcinogenesis , 2014, PLoS Comput. Biol..

[58]  A. Huttenlocher,et al.  Asymmetric localization of calpain 2 during neutrophil chemotaxis. , 2007, Molecular biology of the cell.

[59]  Claire V. Harper,et al.  Population robustness arising from cellular heterogeneity , 2010, Proceedings of the National Academy of Sciences.

[60]  Leighton J. Core,et al.  Coordinated Effects of Sequence Variation on DNA Binding, Chromatin Structure, and Transcription , 2013, Science.

[61]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , 2022 .

[62]  C. Ponting,et al.  Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity , 2015, Nature Methods.

[63]  Nathan C. Sheffield,et al.  Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics , 2015, Cell reports.

[64]  David J. Arenillas,et al.  JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles , 2013, Nucleic Acids Res..

[65]  C. Rice,et al.  Interferon-stimulated genes: a complex web of host defenses. , 2014, Annual review of immunology.

[66]  Manolis Kellis,et al.  Discovery and characterization of chromatin states for systematic annotation of the human genome , 2010, Nature Biotechnology.

[67]  William Stafford Noble,et al.  FIMO: scanning for occurrences of a given motif , 2011, Bioinform..

[68]  Hannah H. Chang,et al.  Non-genetic heterogeneity — a mutation-independent driving force for the somatic evolution of tumours , 2009, Nature Reviews Genetics.

[69]  A. Oshlack,et al.  DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging , 2014, bioRxiv.

[70]  Rafael A. Irizarry,et al.  Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays , 2014, Bioinform..

[71]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[72]  Matthew D. Young,et al.  Gene ontology analysis for RNA-seq: accounting for selection bias , 2010, Genome Biology.

[73]  N. Weng.  Aging of the immune system: how much can the adaptive immune system adapt? , 2006, Immunity.

[74]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[75]  Jianer Chen,et al.  A Fast Agglomerate Algorithm for Mining Functional Modules in Protein Interaction Networks , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[76]  Nathan C. Sheffield,et al.  Multi-Omics of Single Cells: Strategies and Applications , 2016, Trends in biotechnology.

[77]  Andrew E. Jaffe,et al.  Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .

[78]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[79]  A. Feinberg,et al.  Increased methylation variation in epigenetic domains across cancer types , 2011, Nature Genetics.

[80]  A. Feinberg,et al.  Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease , 2010, Proceedings of the National Academy of Sciences.

[81]  T. Palella,et al.  Quantitative changes in T cell DNA methylation occur during differentiation and ageing , 1990, European journal of immunology.

[82]  Peter A. Jones.  Functions of DNA methylation: islands, start sites, gene bodies and beyond , 2012, Nature Reviews Genetics.

[83]  O. Soehnlein,et al.  Neutrophil heterogeneity: implications for homeostasis and pathogenesis. , 2016, Blood.

[84]  Charles Y. Lin,et al.  Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer , 2016, Genome Biology.