MicroRNAs upregulated during HIV infection target peroxisome biogenesis factors: Implications for virus biology, disease mechanisms and neuropathology
Bibliographic record
Abstract
HIV-associated neurocognitive disorders (HAND) represent a spectrum neurological syndrome that affects up to 25% of patients with HIV/AIDS. Multiple pathogenic mechanisms contribute to the development of HAND symptoms including chronic neuroinflammation and neurodegeneration. Among the factors linked to development of HAND is altered expression of host cell microRNAs (miRNAs) in brain. Here, we examined brain miRNA profiles among HIV/AIDS patients with and without HAND. Our analyses revealed differential expression of 17 miRNAs in brain tissue from HAND patients. A subset of the upregulated miRNAs (miR-500a-5p, miR-34c-3p, miR-93-3p and miR-381-3p), are predicted to target peroxisome biogenesis factors (PEX2, PEX7, PEX11B and PEX13). Expression of these miRNAs in transfected cells significantly decreased levels of peroxisomal proteins and concomitantly decreased peroxisome numbers or affected their morphology. The levels of miR-500a-5p, miR-34c-3p, miR-93-3p and miR-381-3p were not only elevated in the brains of HAND patients, but were also upregulated during HIV infection of primary macrophages. Moreover, concomitant loss of peroxisomal proteins was observed in HIV-infected macrophages as well as in brain tissue from HIV-infected patients. HIV-induced loss of peroxisomes was abrogated by blocking the functions of the upregulated miRNAs. Overall, these findings point to previously unrecognized miRNA expression patterns in the brains of HIV patients. Targeting peroxisomes by up-regulating miRNAs that repress peroxisome biogenesis factors may represent a novel mechanism by which HIV-1 subverts innate immune responses and/or causes neurocognitive dysfunction.
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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 | 0.001 | 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".