Interplay between the Lung Microbiome, Pulmonary Immunity and Viral Reservoirs in People Living with HIV under Antiretroviral Therapy
Bibliographic record
Abstract
Pulmonary dysbiosis may predispose people living with HIV (PLWH) to chronic lung disease. Herein, we assessed whether intrapulmonary HIV reservoir size and immune disruption are associated with reduced bacterial lung diversity in PLWH. Bacterial DNA was extracted and PCR-amplified from cell-free bronchoalveolar lavage (BAL) fluid from 28 PLWH and 9 HIV-negative controls. Amplicon sequence variant (ASV) relative abundances and taxonomic identities were analyzed using joint species distribution modeling. HIV-DNA was quantified from blood and pulmonary CD4+ T-cells using ultra-sensitive qPCR. Immunophenotyping of BAL T-cells was performed using flow cytometry. Lung microbiome diversity was lower in smokers than non-smokers and microbiome composition was more variable in PLWH than HIV-negative individuals. Frequencies of effector memory BAL CD4+ and CD8+ T-cells positively correlated with abundance of several bacterial families while frequencies of BAL activated CD4+ T-cells negatively correlated with abundance of most lung bacterial families. Higher HIV-DNA levels in blood, but not in BAL, as well as frequencies of senescent CD4+ T-cells were associated with reduced bacterial diversity. These findings suggest that HIV infection may weaken the relationship between the lung microbiome and smoking status. Viral reservoir and immune activation levels may impact the lung microbiome, predisposing PLWH to pulmonary comorbidities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".