Pregnancy is a risk factor for increased severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory infections, but the mechanisms underlying this risk are poorly u Show more
Pregnancy is a risk factor for increased severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory infections, but the mechanisms underlying this risk are poorly understood. To gain insight into the role of pregnancy in modulating immune responses at baseline and upon SARS-CoV-2 infection, we collected peripheral blood mononuclear cells and plasma from 226 women, including 152 pregnant individuals and 74 non-pregnant women. We find that SARS-CoV-2 infection is associated with altered T cell responses in pregnant women, including a clonal expansion of CD4-expressing CD8 Show less
Dong Sun Oh, Eunha Kim, Guangqing Lu+31 more · 2024 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understand Show more
Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8 Show less
Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene-gene (G × G) interactions of 78 single nucleotide polymorphisms Show more
Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene-gene (G × G) interactions of 78 single nucleotide polymorphisms (SNPs) in genes associated with depression and age-related diseases would identify significant interactions with increased predictive power for depression. A retrospective cohort study. A survey of participants in the Wisconsin Longitudinal Study. A total of 4811 persons (2464 women and 2347 men) who provided saliva for genotyping; the group comes from a randomly selected sample of Wisconsin high school graduates from the class of 1957 as well as a randomly selected sibling, almost all of whom are non-Hispanic white. Depression as determine by the Composite International Diagnostic Interview-Short-Form. Using a classification tree approach (recursive partitioning (RP)), the authors identified a number of candidate G × G interactions associated with depression. The primary SNP splits revealed by RP (ANKK1 rs1800497 (also known as DRD2 Taq1A) in men and DRD2 rs224592 in women) were found to be significant as single factors by logistic regression (LR) after controlling for multiple testing (p=0.001 for both). Without considering interaction effects, only one of the five subsequent RP splits reached nominal significance in LR (FTO rs1421085 in women, p=0.008). However, after controlling for G × G interactions by running LR on RP-specific subsets, every split became significant and grew larger in magnitude (OR (before) → (after): men: GNRH1 novel SNP: (1.43 → 1.57); women: APOC3 rs2854116: (1.28 → 1.55), ACVR2B rs3749386: (1.11 → 2.17), FTO rs1421085: (1.32 → 1.65), IL6 rs1800795: (1.12 → 1.85)). The results suggest that examining G × G interactions improves the identification of genetic associations predictive of depression. 4 of the SNPs identified in these interactions were located in two pathways well known to impact depression: neurotransmitter (ANKK1 and DRD2) and neuroendocrine (GNRH1 and ACVR2B) signalling. This study demonstrates the utility of RP analysis as an efficient and powerful exploratory analysis technique for uncovering genetic and molecular pathway interactions associated with disease aetiology. Show less