Downregulation of miRNA-26a by HIV-1 Enhances CD59 Expression and Packaging Impacting Virus Susceptibility to Antibody-Dependent Complement-Mediated Lysis
Bibliographic record
Abstract
MicroRNAs (miRNAs) play important roles in the control of HIV-1 infection. Here, we performed RNAseq profilings of miRNAs and mRNAs expressed in CD4+ T-lymphocytes upon HIV-1 infection. Our results reveal significant alterations of miRNAs and mRNAs expression profiles in infected relative to uninfected cells. One of the miRNAs markedly downregulated in infected cells is miRNA-26a. Among the putative targets of miRNA-26a are CD59 receptor transcripts, which are significantly upregulated in infected CD4+ T-cells. Addition of miRNA-26a mimics to CD4+ T-cells reduces CD59 at both mRNA and surface protein levels, validating CD59 as a miRNA-26a target. Consistent with the reported inhibitory role of CD59 in complement-mediated lysis (CML), knocking-out CD59 in CD4+ T-cells renders both HIV-1 infected cells and progeny virions more prone to antibody-dependent CML (ADCML). Addition of miRNA-26a mimics to infected cells leads to enhanced sensitivity of progeny virions to ADCML, a condition linked to a reduction of CD59 packaging into released virions. Lastly, HIV-1-mediated downregulation of miRNA-26a expression is shown to be dependent on integrated HIV-1 expression but does not involve viral accessory proteins. Overall, these results highlight a novel mechanism by which HIV-1 limits ADCML by upregulating CD59 expression via miRNA-26a downmodulation.
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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) | 0.001 | 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".