Transcriptional similarity in couples reveals the impact of shared environment and lifestyle on gene regulation through modified cytosines

Cohabitation as a determinant of adaptive and innate immune cell profiles: Findings from the Health and Retirement Study

Introduction

Non-genetic factors are important but poorly understood determinants of immune profiles. Age and Cytomegalovirus (CMV) infection remain two well documented non-genetic determinants of the immune profile. Recently, one study identified cohabitation in the same household as an important determinant of immune profiles.

Methods

We used immunophenotyping data from the Health and Retirement Study (HRS) to evaluate the association between cohabitation and the adaptive (subsets of T-cells, B-cells) and innate immune profiles (subsets of monocytes, natural killer cells and neutrophils). We compared adaptive and innate immune cell profiles using immunophenotyping data from 1184 same-household pairs (cohabitating partners) to 1184 non-household pairs to evaluate the association between cohabitation and adaptive immune cell profiles. We used data from 1737 same-household pairs and 1737 non-household pairs to evaluate the association between cohabitation and innate cell profiles. Household and non-household pairs were matched on age (±2years), educational background and race/ethnicity to minimize confounding due to these factors. The adaptive immune cells and innate immune cell profiles were compressed to two coordinates using multidimensional scaling (MDS). The Euclidean distances between same-household pairs were compared to the distances between non-household pairs for the adaptive and innate cell profiles separately using two sample independent t-tests. We also performed additional adjustment for age and BMI differences, CMV serostatus and smoking concordance/discordance status among household members.

Results

For adaptive immune cell profiles, the mean Euclidean distance between same-household pairs was 4% lower than the non-household pairs (p = 0.03). When stratified by concordance for CMV serostatus among household pairs, the Euclidean distance was significantly lower by 8% in the same-household pairs as compared to non-household pairs among those who were discordant for CMV serostatus (p = 0.01) and among same-household pairs who were CMV seronegative (p = 0.02) after covariate adjustment. The mean Euclidian distance between same-household pairs was also 8% lower than non-household pairs for the innate immune cell profiles (p-value <0.0001) and this difference remained consistent across all strata of CMV infection.

Discussion

This study confirms that cohabitation is associated with similarity in immune cell profiles. The differential effects of cohabitation on the adaptive and innate immune profiles suggest that further studies into the common environmental factors that influence individual immune cell subsets need to be evaluated in greater detail.

Marriage and Gut (Microbiome) Feelings: Tracing Novel Dyadic Pathways to Accelerated Aging

Within a couple, partners influence each other's mental and physical health. This review focuses on how couples' relationships, the partners' individual and joint vulnerabilities, and their health behaviors influence health through changes in the gut microbiota, metabolism, and immune function. Couples' shared stressors and emotions and their intertwined lifestyles and routines serve to promote common disease risks in part through parallel changes in their gut microbiotas. Marital discord, stress, and depression have strong bidirectional links, fueling one another. Chronic marital stress and depression can elevate the risk for obesity, metabolic syndrome, and cardiovascular disease by altering resting energy expenditure, insulin production, and triglyceride responses after unhealthy meals. During stressful times, health behaviors typically suffer-and sleep disturbances, poor diets, and sedentary behavior all influence these metabolic pathways while also promoting gut dysbiosis. Dysbiosis increases intestinal permeability (gut leakiness), providing a mechanistic pathway from marital distress and depression to heightened inflammation and accelerated aging. Age-related changes in the gut microbiota's composition and gut leakiness foster immunosenescence, as well as the progression of inflamm-aging; these age-related risks may be altered by stress and depression, diet, sleep, exercise habits, and developmental shifts in emotion regulation strategies. Consideration of the strong mutual influences that partners have on each other's mood and health behaviors, as well as the biological pathways that underlie these influences, provides a new way to view marriage's health implications.

Marriage, divorce, and the immune system

This article reviews evidence from several lines of work to describe how marriage and divorce can provoke health-relevant immune alterations, including ways that marital closeness can be perilous for health and divorce can be beneficial. The multiple stresses of a troubled relationship are depressogenic, and the development of a mood disorder sets the stage for psychological and biological vulnerability. Depression provides a central pathway to immune dysregulation, inflammation, and poor health; gender-related differences in depression and inflammation can heighten risk for women compared to men. Sleep and obesity can simultaneously feed off depression as they promote it. In addition, spousal similarities in health behaviors, gene expression, immune profiles and the gut microbiota offer new ways to consider the health advantages and risks of marriage and divorce, providing new perspectives on couples' interdependence, as well as new directions for research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Background

Oral tongue squamous cell carcinoma (OTSCC) is the most common subtype of oral cancer. A predictive gene signature is necessary for prognosis of OTSCC.

Methods

Five microarray data sets of OTSCC from the Gene Expression Omnibus (GEO) and one data set from The Cancer Genome Atlas (TCGA) were obtained. Differentially expressed genes (DEGs) of GEO data sets were identified by integrated analysis. The DEGs associated with prognosis were screened in the TCGA data set by univariate survival analysis to obtain a gene signature. A risk score was calculated as the summation of weighted expression levels with coefficients by Cox analysis. The signature was used to distinguish carcinoma, estimated by receiver operator characteristic curves and the area under the curve (AUC). All were validated in the GEO and TCGA data sets.

Results

Integrated analysis of GEO data sets revealed 300 DEGs. A 16-gene signature and a risk score were developed after survival analysis. The risk score was effective to stratify patients into high-risk and low-risk groups in the TCGA data set (P < 0.001). The 16-gene signature was valid to distinguish the carcinoma from normal samples (AUC 0.872, P < 0.001).

Discussion

We identified a useful 16-gene signature for prognosis of OTSCC patients, which could be applied to clinical practice. Further studies were needed to prove the findings.