Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation
Bibliographic record
Abstract
Abstract Prior encounters with pathogens and other molecules can imprint long-lasting effects on our immune system, potentially influencing future physiological outcomes. However, given the wide range of pathogens and commensal microbes to which humans are exposed, their collective impact on the health and aging processes in the general population is still not fully understood. In this study, we aimed to explore relations between exposures, including to pathogens, microbiome and common allergens, and biological aging and inflammation. We capitalized on an extensive repository of the antibody-binding repertoire against 2,815 microbial, viral, and environmental peptides in a deeply-phenotyped population cohort of 1,443 participants. Utilizing antibody-binding as a proxy for past exposures, we investigated their impact on biological aging markers, immune cell composition and systemic inflammation. This identified that immune response against cytomegalovirus (CMV), rhinovirus and specific gut bacterial species influences the telomere length of different immune cell types. Using blood single-cell RNA-seq measurements, we identified a large effect of CMV infection on the transcriptional landscape of specific immune cells, in particular subpopulations of CD8 and CD4 T-cells. Our work provides a broad examination of the role of past and chronic exposures in biological aging and inflammation, highlighting a role for chronic infections (CMV and Epstein-Barr Virus) and common pathogens (rhinoviruses and adenovirus C). Highlights The study provides a broad association of antibody reactivity with biomarkers of aging and inflammation It shows that anti-CMV, rhinovirus and gut antimicrobial antibody reactivity relate to telomere length CMV infection associates to the telomere length of CD45RA+CD57+ cells in a sex-dependent manner CMV influences the transcriptomic landscape of CD8+ T effector memory and cytotoxic CD4+ cell populations Anti-Epstein-Barr-Virus and anti-adenoviral responses are associated with higher circulating IL-18BP concentrations
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".