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. 2017 Nov 21;114(47):12554-12559.
doi: 10.1073/pnas.1707373114. Epub 2017 Nov 6.

Natural language indicators of differential gene regulation in the human immune system

Matthias R Mehl  1 , Charles L Raison  2   3 , Thaddeus W W Pace  4 , Jesusa M G Arevalo  5   6 , Steve W Cole  7   6
Affiliations

Natural language indicators of differential gene regulation in the human immune system

Matthias R Mehl et al. Proc Natl Acad Sci U S A. .

Abstract

Adverse social conditions have been linked to a conserved transcriptional response to adversity (CTRA) in circulating leukocytes that may contribute to social gradients in disease. However, the CNS mechanisms involved remain obscure, in part because CTRA gene-expression profiles often track external social-environmental variables more closely than they do self-reported internal affective states such as stress, depression, or anxiety. This study examined the possibility that variations in patterns of natural language use might provide more sensitive indicators of the automatic threat-detection and -response systems that proximally regulate autonomic induction of the CTRA. In 22,627 audio samples of natural speech sampled from the daily interactions of 143 healthy adults, both total language output and patterns of function-word use covaried with CTRA gene expression. These language features predicted CTRA gene expression substantially better than did conventional self-report measures of stress, depression, and anxiety and did so independently of demographic and behavioral factors (age, sex, race, smoking, body mass index) and leukocyte subset distributions. This predictive relationship held when language and gene expression were sampled more than a week apart, suggesting that associations reflect stable individual differences or chronic life circumstances. Given the observed relationship between personal expression and gene expression, patterns of natural language use may provide a useful behavioral indicator of nonconsciously evaluated well-being (implicit safety vs. threat) that is distinct from conscious affective experience and more closely tracks the neurobiological processes involved in peripheral gene regulation.

Keywords: genomics; psycholinguistics; psychoneuroimmunology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Natural language-use features. Distribution of individual differences in (A) EAR-sampled parameters of language volume and (B) language structure, including eight general categories of function word (capitalized labels) and five subcategories of personal pronoun (lowercase labels). Data represent average values for each parameter computed over a mean of 158 (range 14–260) 30- to 50-s audio samples collected at 9- to 12.5-min intervals over 2 d (resulting in an average of 4,070 ± 272 words per individual). Whiskers indicate the range of individual values across 143 study participants; boxes span 25th–75th percentiles, and internal bars indicate 50th percentile.
Fig. 2.
Fig. 2.
Prediction of CTRA gene expression. (A) Prediction of peripheral blood mRNA levels for 50 CTRA indicator transcripts (proinflammatory genes, IFN response genes, and antibody-related transcripts) by demographic and behavioral characteristics (blue chords), language-volume metavariables (orange chords), and language-structure features representing the prevalence of eight categories of function word (dark green chords) and five subcategories of personal pronoun (light green chords). Black arcs indicate total CTRA prediction in each mixed-effect linear model. Chord widths indicate the relative contribution from standardized values of each variable (squared partial regression coefficients, sorted top-to-bottom by descending magnitude in Dataset S2 model 3). Colored chords indicate statistically significant effects (P < 0.05), and gray chords indicate nonsignificant effects. (B) Increment to CTRA prediction from adding self-report measures of depression, anxiety, perceived stress, and perceived social isolation/loneliness (red chords); Dataset S2 model 4. (C) Increment to prediction from adding mRNA markers of leukocyte subsets in the peripheral blood cell pool (purple chords); Dataset S2 model 5.
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