Sexual-dimorphism in human immune system aging

Castelo-Branco, C. & Soveral, I. The immune system and aging: a review. Gynecol. Endocrinol. 30, 16–22 (2014).

Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).

Jones, M. J., Goodman, S. J. & Kobor, M. S. DNA methylation and healthy human aging. Aging Cell 14, 924–932 (2015).

Moskowitz, D. M. et al. Epigenomics of human CD8 T cell differentiation and aging. Sci. Immunol. 2, eaag0192 (2017).

Ucar, D. et al. The chromatin accessibility signature of human immune aging stems from CD8(+) T cells. J. Exp. Med. 214, 3123–3144 (2017).

Giefing‐Kröll, C., Berger, P., Lepperdinger, G. & Grubeck‐Loebenstein, B. How sex and age affect immune responses, susceptibility to infections, and response to vaccination. Aging Cell 14, 309–321 (2015).

Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626 (2016).

Abdullah, M. et al. Gender effect on in vitro lymphocyte subset levels of healthy individuals. Cell. Immunol. 272, 214–219 (2012).

Fan, H. et al. Gender differences of B cell signature in healthy subjects underlie disparities in incidence and course of SLE related to estrogen. J. Immunol. Res. 2014, 814598 (2014).

Schmiedel, B. J. et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701–1715.e1716 (2018).

Bakker, O. B. et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses. Nat. Immunol. 19, 776 (2018).

Piasecka, B. et al. Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl Acad. Sci. USA 115, E488–E497 (2018).

Chaussabel, D. et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29, 150–164 (2008).

Goronzy, J. J. & Weyand, C. M. Understanding immunosenescence to improve responses to vaccines. Nat. Immunol. 14, 428–436 (2013).

Kleiveland, C. R. In: (eds Verhoeckx, K., Cotter, P., López-Expósito, I., Kleiveland, C., Lea, T., Mackie, A., Requena, T., Swiatecka, D. & Wichers, H.) The Impact of Food Bioactives on Health (eds). (Springer, Cham, 2015).

Patin, E. et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat. Immunol. 19, 302 (2018).

Olson, N. C. et al. Decreased naive and increased memory CD4(+) T cells are associated with subclinical atherosclerosis: the multi-ethnic study of atherosclerosis. PLoS ONE 8, e71498 (2013).

Clave, E. et al. Human thymopoiesis is influenced by a common genetic variant within the TCRA-TCRD locus. Sci. Transl. Med. 10, eaao2966 (2018).

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317 (2015).

Feser, J. & Tyler, J. Chromatin structure as a mediator of aging. FEBS Lett. 585, 2041–2048 (2011).

Chaussabel, D. & Baldwin, N. Democratizing systems immunology with modular transcriptional repertoire analyses. Nat. Rev. Immunol. 14, 271 (2014).

Hidalgo, L., Einecke, G., Allanach, K. & Halloran, P. The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4+ CTL, CD8+ CTL and NK cells. Am. J. Transplant. 8, 627–636 (2008).

Wang, S. et al. S100A8/A9 in Inflammation. Front. Immunol. 9, 1298 (2018).

Brockwell, P. J., Davis, R. A. & Calder, M. V. Introduction to Time Series and Forecasting. (Springer, 2002).

Wöhner, M. et al. Molecular functions of the transcription factors E2A and E2-2 in controlling germinal center B cell and plasma cell development. J. Exp. Med. 213, 1201–1221 (2016).

Kijima, M. et al. Dendritic cell-mediated NK cell activation is controlled by Jagged2–Notch interaction. Proc. Natl Acad. Sci. USA 105, 7010–7015 (2008).

Johnson, J. L. et al. Lineage-determining transcription factor TCF-1 initiates the epigenetic identity of T cells. Immunity 48, 243–257. e210 (2018).

Whiting, C. C. et al. Large-scale and comprehensive immune profiling and functional analysis of normal human aging. PLoS ONE 10, e0133627 (2015).

Fulop, T. et al. Immunosenescence and inflamm-aging as two sides of the same coin: friends or foes? Front. Immunol. 8, 1960 (2018).

Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

Coleman, P., Finch, C. & Joseph, J. The need for multiple time points in aging studies. Neurobiol. Aging 11, 1–2 (1990).

World Health Organization. World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals. (World Health Organization, 2016).

Hirokawa, K. et al. Slower immune system aging in women versus men in the Japanese population. Immun. Ageing 10, 19 (2013).

De Cecco, M. et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature 566, 73 (2019).

Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics. Nat. Methods 10, 1213 (2013).

Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. cell 71, 882–895 (2018).

Kuchel, G. A. Inclusion of older adults in research: ensuring relevance, feasibility, and rigor. J. Am. Geriatrics Soc. 67, 203–204 (2019).

Robertson, D. & Williams, G. H. Clinical and Translational Science: Principles of Human Research. (Academic Press, 2009).

Hardy, S. E., Kang, Y., Studenski, S. A. & Degenholtz, H. B. Ability to walk 1/4 mile predicts subsequent disability, mortality, and health care costs. J. Gen. Intern. Med. 26, 130–135 (2011).

Podsiadlo, D. & Richardson, S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J. Am. Geriatrics Soc. 39, 142–148 (1991).

Rockwood, K., Awalt, E., Carver, D. & MacKnight, C. Feasibility and measurement properties of the functional reach and the timed up and go tests in the Canadian study of health and aging. J. Gerontol. Ser. A, Biol. Sci. Med. Sci. 55, M70–M73 (2000).

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

Ewing, B., Hillier, L., Wendl, M. C. & Green, P. Base-calling of automated sequencer traces usingPhred. I. Accuracy assessment. Genome Res. 8, 175–185 (1998).

Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12, 1 (2011).

Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

Qu, K. et al. Individuality and variation of personal regulomes in primary human T cells. Cell Syst. 1, 51–61 (2015).

Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

Beekman, R. et al. The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia. Nat. Med. 24, 868 (2018).

Kelder, T. et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 40, D1301–D1307 (2012).

Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2017).

Jolma, A. et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527, 384 (2015).

Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

Chang, W., Cheng, J., Allaire, J., Xie, Y. & McPherson, J. Shiny: web application framework for R. R package version 1, http://CRAN.R-project.org/package=shiny (2017).

Wickham, H. ggplot2: Elegant Graphics For Data Analysis. (Springer, 2016).