Human Immunodeficiency Virus Type 1 Employs the Cellular Dynein Light Chain 1 Protein for Reverse Transcription through Interaction with Its Integrase Protein
Bibliographic record
Abstract
UNLABELLED: In this study, we examined the requirement for host dynein adapter proteins such as dynein light chain 1 (DYNLL1), dynein light chain Tctex-type 1 (DYNLT1), and p150(Glued) in early steps of human immunodeficiency virus type 1 (HIV-1) replication. We found that the knockdown (KD) of DYNLL1, but not DYNLT1 or p150(Glued), resulted in significantly lower levels of HIV-1 reverse transcription in cells. Following an attempt to determine how DYNLL1 could impact HIV-1 reverse transcription, we detected the DYNLL1 interaction with HIV-1 integrase (IN) but not with capsid (CA), matrix (MA), or reverse transcriptase (RT) protein. Furthermore, by mutational analysis of putative DYNLL1 interaction motifs in IN, we identified the motifs (52)GQVD and (250)VIQD in IN as essential for DYNLL1 interaction. The DYNLL1 interaction-defective IN mutant HIV-1 (HIV-1IN(Q53A/Q252A)) exhibited impaired reverse transcription. Through further investigations, we have also detected relatively smaller amounts of particulate CA in DYNLL1-KD cells or in infections with HIV-1IN(Q53A/Q252A) mutant virus. Overall, our study demonstrates the novel interaction between HIV-1 IN and cellular DYNLL1 proteins and suggests the requirement of this virus-cell interaction for proper uncoating and efficient reverse transcription of HIV-1. IMPORTANCE: Host cellular DYNLL1, DYNLT1, and p150(Glued) proteins have been implicated in the replication of several viruses. However, their roles in HIV-1 replication have not been investigated. For the first time, we demonstrated that during viral infection, HIV-1 IN interacts with DYNLL1, and their interaction was found to have a role in proper uncoating and efficient reverse transcription of HIV-1. Thus, interaction of IN and DYNLL1 may be a potential target for future anti-HIV therapy. Moreover, while our study has evaluated the involvement of IN in HIV-1 uncoating and reverse transcription, it also predicts a possible mechanism by which IN contributes to these early viral replication steps.
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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".