Transcription of Preintegrated HIV-1 cDNA Modulates Cell Surface Expression of Major Histocompatibility Complex Class I via Nef
Bibliographic record
Abstract
Although transcription from unintegrated human immunodeficiency virus type 1 (HIV-1) DNA can occur inside infected cells, yielding all classes of viral mRNA transcripts, the translation of viral proteins is very limited. One of the proteins made is Nef, but it is unclear whether Nef produced in this way is able to play a role in immune evasion as occurs with integrated virus. We therefore asked whether transcription from preintegrated HIV-1 cDNAs could result in Nef-mediated modulation of cell surface major histocompatibility complex class I (MHC-I) expression. We infected a Rev-CEM green fluorescent protein (GFP) reporter cell line with virus and blocked integration though use of either an inactive integrase or the integrase inhibitor raltegravir. Infected cells were assayed by flow cytometry for cell surface expression of the HLA-A, HLA-B, and HLA-C allotypes (HLA-ABC), HLA-A31, and HLA-E. Viral RNA and DNA products were assayed via quantitative PCR (qPCR). The prevention of integration had no effect, relative to productively infected cells, on levels of expression of multiply spliced viral mRNA transcripts and Nef protein. Downregulation of HLA-ABC and HLA-A31 also occurred at levels similar to those seen in cells in which integration had occurred. Parallel experiments assaying cell surface HLA-ABC expression in infected activated primary CD4(+) T cells produced a similar pattern of results. Hence, the capacity of HIV-1 to modulate MHC-I is not linked to its ability to integrate. Thus, Nef-mediated evasion of host immune responsiveness might be attributable, in part at least, to transcription from unintegrated viral DNA.
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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 | 0.001 |
| 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".