Gut-derived bacterial toxins impair memory CD4+ T cell mitochondrial function in HIV-1 infection
Bibliographic record
Abstract
People living with HIV (PLWH) who are immune nonresponders (INRs) are at greater risk of comorbidity and mortality than are immune responders (IRs) who restore their CD4+ T cell count after antiretroviral therapy (ART). INRs have low CD4+ T cell counts (<350 c/μL), heightened systemic inflammation, and increased CD4+ T cell cycling (Ki67+). Here, we report the findings that memory CD4+ T cells and plasma samples of INRs from several cohorts are enriched in gut-derived bacterial solutes p-cresol sulfate (PCS) and indoxyl sulfate (IS) that both negatively correlated with CD4+ T cell counts. In vitro PCS or IS blocked CD4+ T cell proliferation, induced apoptosis, and diminished the expression of mitochondrial proteins. Electron microscopy imaging revealed perturbations of mitochondrial networks similar to those found in INRs following incubation of healthy memory CD4+ T cells with PCS. Using bacterial 16S rDNA, INR stool samples were found enriched in proteolytic bacterial genera that metabolize tyrosine and phenylalanine to produce PCS. We propose that toxic solutes from the gut bacterial flora may impair CD4+ T cell recovery during ART and may contribute to CD4+ T cell lymphopenia characteristic of INRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".