Upregulation of Glucose Uptake and Hexokinase Activity of Primary Human CD4+ T Cells in Response to Infection with HIV-1
Bibliographic record
Abstract
Infection of primary CD4+ T cells with HIV-1 coincides with an increase in glycolysis. We investigated the expression of glucose transporters (GLUT) and glycolytic enzymes in human CD4+ T cells in response to infection with HIV-1. We demonstrate the co-expression of GLUT1, GLUT3, GLUT4, and GLUT6 in human CD4+ T cells after activation, and their concerted overexpression in HIV-1 infected cells. The investigation of glycolytic enzymes demonstrated activation-dependent expression of hexokinases HK1 and HK2 in human CD4+ T cells, and a highly significant increase in cellular hexokinase enzyme activity in response to infection with HIV-1. HIV-1 infected CD4+ T cells showed a marked increase in expression of HK1, as well as the functionally related voltage-dependent anion channel (VDAC) protein, but not HK2. The elevation of GLUT, HK1, and VDAC expression in HIV-1 infected cells mirrored replication kinetics and was dependent on virus replication, as evidenced by the use of reverse transcription inhibitors. Finally, we demonstrated that the upregulation of HK1 in HIV-1 infected CD4+ T cells is independent of the viral accessory proteins Vpu, Vif, Nef, and Vpr. Though these data are consistent with HIV-1 dependency on CD4+ T cell glucose metabolism, a cellular response mechanism to infection cannot be ruled out.
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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 | 0.001 |
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".