Autophagy-enhancing ATG16L1 polymorphism improves clinical outcomes and promotes superior T-cell immunity in chronic HIV-1 infection
Bibliographic record
Abstract
Abstract Chronic HIV-1 infection is characterized by T-cell dysregulation that is only partly restored by antiretroviral therapy. Here, we demonstrate a protective role for autophagy in HIV-1 disease pathogenesis. Targeted analysis of genetic variation in core autophagy gene ATG16L1 revealed a previously unidentified polymorphism, which correlated functionally with enhanced autophagy and clinically with improved survival of untreated HIV-1-infected individuals. Intrinsically-enhanced autophagy in homozygous minor T-cells resulted in superior antiviral immunity, evidenced by increased proliferation, revamped immune responsiveness, and suppressed exhaustion/immunosenescence features. In-depth flow-cytometric and transcriptional T-helper-profiling revealed signatures unique to minor genotyped individuals with enriched regulation of pro-inflammatory networks and skewing towards immunoregulatory T-cell-phenotype. Treatment with autophagy-enhancing pharmaceuticals reprogrammed T-cells to exhibit the protective traits observed in homozygous minor donors. These data underscore the in vivo relevance of autophagy for longer-lasting T-cell-mediated HIV-1 control, with implications towards development of host-directed therapeutics targeting autophagy to restore immune function in chronic HIV-1 infection.
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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 | 0.000 |
| 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) | 0.002 | 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".