Our genes are comprised of DNA, but those DNA genes only influence cellular function, health, and behavior if they are transcribed into RNA, or “expressed.” Only a subset of our ~20,000 genes are actively transcribed in any given cell, and which genes are “on” and “off” determines not only the identity of the cell but its functional capacities and behavior. As such, RNA “transcriptome profiling” has become the dominant method for analyzing the molecular underpinnings of healthy physiology, development, aging, and disease1. Research has also found that social and psychological processes can influence RNA profiles2-4. RNA profiling thus provides a useful method for mapping the molecular interface between social and behavioral processes and the biology of health and aging.

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Stress hormones and neurotransmitters exert their effects in part by altering the transcription of genes in cells and tissues throughout the body. These effects are mediated by cellular receptor systems that activate transcription factors within the cell which ultimately change the rate at which specific genes’ DNA id transcribed into RNA and subsequently translated into the proteins that mediate cell function. One major target of these effects are the immune cells (leukocytes) present in circulating blood.

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RNA profiling can be used to assess general stress effects in various ways. For example, one could examine a specific gene of interest, assess an a priori-specified set of genes known to be involved in a common biological process (e.g., inflammation), or assess the shared biological characteristics of an arbitrary set of genes that empirically tracks a specific risk factor or outcome (e.g., common regulation by the pro-inflammatory transcription factor NF-kappaB, or shared expression in a subset of leukocytes called monocytes). Chronic stress can impact RNA profiles at each of these levels, and such effects can be detected by analyzing genome-wide surveys of RNA expression using specialized bioinformatics software. For example, leukocytes from people exposed to chronic stress often show up-regulated expression of genes involved in inflammation, and down-regulated expression of genes involved in antiviral responses (Type I interferons) and antibody production2,3. This “Conserved Transcriptional Response to Adversity” (CTRA) pattern is seen across a diverse range of adverse environments and a diverse array of species including monkeys, mice, and fish. Transcriptome profiles such as the CTRA are directly relevant to health because immune cell-mediated inflammation and antimicrobial responses contribute to many of the disease processes that dominate contemporary epidemiology, including heart disease, cancer, neurodegeneration, and viral infections. A set of 53 genes involved in inflammation, antiviral responses, and antibody production has been used to assess the CTRA in several studies5,6. CTRA biology can also be assessed by TELiS bioinformatics analyses to detect increased activity of the pro-inflammatory transcription factor NF-kappaB and decreased activity of Interferon Response Factors5,7, or by Transcript Origin analyses to detect up-regulation of a specific type of leukocyte known as a CD16- Classical Monocyte8.

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Blood cell RNA profiles reflect a combination of recent effects on RNA transcription in existing cells (occurring over hours) as well as longer-term effects in changing cell population composition (occurring over days to weeks)8,9. Acute stress can also rapidly alter blood cell composition (over minutes)10,11, although these effects are transient and their health significance remains uncertain. RNA is a useful level at which to assess the molecular impact of stress because, unlike DNA, it is quantitatively responsive to environmental conditions and can show large effect sizes (e.g., 20-100-fold change over hours), and unlike protein, it can be measured with high sensitivity and specificity. These measurement advantages allow for efficient assessment of all ~20,000 human genes simultaneously (“transcriptome profiling”). RNA profiling can also be applied to other tissues such as cancers (to understand the ultimate impact of stress on diseased tissue)12,13 or placentas (to understand effects on fetal development)14. Here we focus on blood RNA profiling as a measure that is both health-relevant and easy to implement in field and population studies of aging.

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Collection and Measurement

RNA profiling is most often performed on venipuncture blood samples (i.e., blood drawn into tubes with a needle by a phlebotomist), but dried blood spot (DBS) samples can also be used in field settings where phlebotomy is infeasible15,16. Regardless of the sampling method or tissue analyzed, similar biochemical protocols are used to isolate RNA from other cellular components and quantify the abundance of RNA molecules derived from each gene17. These methods usually involve the “reverse transcription” of sample RNA into “complementary DNA” which can then be assayed by polymerase chain reaction (RT-PCR; if only a few genes are of interest) or by high-throughput DNA sequencing systems that survey all of the RNA species present (RNA sequencing, or RNAseq). The biggest challenge in transcriptome profiling studies involves analyzing the copious data that result, which often involves many more outcomes (~20,000 genes) than study subjects; large numbers of genes that are expressed weakly, inconsistently, or not at all; and >10-fold heteroscedasticity across genes. Different analytic strategies are appropriate for different study objectives, and the dominant approach among geneticists (searching for individual genes that show statistically significant association with an environmental risk factor) may not be optimal for most social or behavioral studies, which typically use genomic data to identify correlates of cellular and molecular signaling pathways already implicated in health and disease (i.e., focusing on sets of biologically-related genes rather than individual genes in isolation)17,18. Such gene set or “pathway” analyses can be used to assess the activity of specific hormones/neurotransmitters, receptors, and transcription factors that mediate environmental influences on gene expression7; the specific cell types that respond to a given stimulus8,19,20; a priori-defined transcriptome patterns such as the CTRA5,6; and the role of genetic polymorphisms or epigenetic marks in modifying individual molecular responses to environmental stimuli21-23. Gene set discovery analyses can also be used to identify novel groups of genes that track an environmental factor or health outcome24,25. A recent integrative review provides more background on transcriptome profiling data collection and analysis17.

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Many major research institutions have the ability to perform transcriptome profiling. To help social and behavioral scientists integrate transcriptome profiling into their studies, the National Institute of Aging-funded USC-UCLA Biodemography Center operates a Social Genomics Core Laboratory to provide strategic consulting in study design; sample processing and assay services; and assistance in data analysis, bioinformatics (including the free software for TELiS), and substantive interpretation (contact steve.cole@ucla.edu).

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Strengths:

1) Provides a system-wide comprehensive portrait of genomic response to environmental stimuli

2) Direct significance for health and aging

3) Well-validated, sensitive, specific, and comprehensive assay platforms (RT-PCR, RNAseq)

4) Some biological pathways have already been identified to mediate causal effects of psychological/social processes on gene expression

5) Large open-access databases of accumulated gene expression data (NCBI Gene Expression Omnibus; EMBL ArrayExpress) help facilitate interpretation of new data by empirical relationship to previous findings

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Limitations:

1) Complexity, both substantive and technical

2) Expense (per-sample costs currently $150-$500 depending on approach)

3) A moving target; genomics is a large field, much remains unknown, and technological and substantive state-of-the-art advance continually

4) Change in RNA abundance does not guarantee change in protein abundance or biological function (though they are generally well correlated)

5) Requires tissue capture, with varying invasiveness depending on tissue

Given the key role of RNA profiling in basic biology, transcriptome analyses will continue to be an essential tool for understanding how social, psychological, and environmental conditions interact with the human genome to shape individual health, development, and aging.

Author(s) and Reviewer(s):

Prepared by Steve Cole, PhD. Questions and any suggested additions should be addressed to steve.cole@ucla.edu or elissa.epel@ucsf.edu.

Version date: March 1, 2018.

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References:

Gibson, G. Microarray analysis: genome-scale hypothesis scanning. PLoS biology 1, E15, doi:10.1371/journal.pbio.0000015 (2003).

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Cole, S. W. Human social genomics. PLoS Genet 10, e1004601 (2014).

 

Cole, S. W. Social regulation of human gene expression: mechanisms and implications for public health. Am J Public Health 103 Suppl 1, S84-92 (2013).

 

Gibson, G. The environmental contribution to gene expression profiles. Nat Rev Genet. 9, 575-581 (2008).

 

Fredrickson, B. L. et al. A functional genomic perspective on human well-being. Proc Natl Acad Sci U S A 110, 13684-13689 (2013).

 

Fredrickson, B. L. et al. Psychological well-being and the human conserved transcriptional response to adversity. PLoS One 10, e0121839 (2015).

 

Cole, S. W., Yan, W., Galic, Z., Arevalo, J. & Zack, J. A. Expression-based monitoring of transcription factor activity: The TELiS database. Bioinformatics 21, 803-810 (2005).

 

Powell, N. D. et al. Social stress up-regulates inflammatory gene expression in the leukocyte transcriptome via beta-adrenergic induction of myelopoiesis. Proc Natl Acad Sci U S A 110, 16574-16579 (2013).

 

Heidt, T. et al. Chronic variable stress activates hematopoietic stem cells. Nat Med 20, 754-758 (2014).

 

Benschop, R. J., Rodrigues-Feuerhahn, M. & Schedlowski, M. Catecholamine-induced leukocytosis: Early observations, current research, and future directions. Brain, behavior, and immunity 10, 77-91 (1996).

 

Richlin, V. A., Arevalo, J. M., Zack, J. A. & Cole, S. W. Stress-induced enhancement of NF-kappaB DNA-binding in the peripheral blood leukocyte pool: effects of lymphocyte redistribution. Brain, behavior, and immunity 18, 231-237 (2004).

 

Lutgendorf, S. K. et al. Depression, social support, and beta-adrenergic transcription control in human ovarian cancer. Brain, behavior, and immunity 23, 176-183 (2009).

 

Lutgendorf, S. K. et al. Biobehavioral modulation of the exosome transcriptome in ovarian carcinoma. Cancer 124, 580-586, doi:10.1002/cncr.31078 (2018).

 

Miller, G. E. et al. Maternal socioeconomic disadvantage is associated with transcriptional indications of greater immune activation and slower tissue maturation in placental biopsies and newborn cord blood. Brain, behavior, and immunity 64, 276-284, doi:10.1016/j.bbi.2017.04.014 (2017).

 

McDade, T. W. et al. Genome-wide profiling of RNA from dried blood spots: convergence with bioinformatic results derived from whole venous blood and peripheral blood mononuclear cells. Biodemography and social biology 62, 182-197 (2016).

 

Kohrt, B. A. et al. Psychological resilience and the gene regulatory impact of posttraumatic stress in Nepali child soldiers. Proc Natl Acad Sci U S A 113, 8156-8161 (2016).

 

Cole, S. W. in Handbook of Psychophysiology (eds J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson) Ch. 16, 354-376 (Cambridge University Press, 2016).

 

Cole, S. W. Elevating the perspective on human stress genomics. Psychoneuroendocrinology 35, 955-962 (2010).

 

Shen-Orr, S. S. & Gaujoux, R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 25, 571-578 (2013).

 

Cole, S. W., Hawkley, L. C., Arevalo, J. M. & Cacioppo, J. T. Transcript origin analysis identifies antigen-presenting cells as primary targets of socially regulated gene expression in leukocytes. Proc Natl Acad Sci U S A 108, 3080-3085 (2011).

 

Idaghdour, Y. et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat Genet 42, 62-67 (2010).

 

Idaghdour, Y., Storey, J. D., Jadallah, S. J. & Gibson, G. A genome-wide gene expression signature of environmental geography in leukocytes of Moroccan Amazighs. PLoS Genet. 4, e1000052 (2008).

 

Cole, S. et al. Computational identification of gene-social environment interaction at the human IL6 locus. Proc Natl Acad Sci U S A 107, 5681-5686 (2010).

 

Preininger, M. et al. Blood-informative transcripts define nine common axes of peripheral blood gene expression. PLoS Genet 9, e1003362 (2013).

 

Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).