Regulation of immune activation in models of resistance to HIV infection and delayed disease progression
Bibliographic record
Abstract
Understanding natural mechanisms of protection against HIV infection and disease progression are key priorities for informing vaccine and microbicide design. The research presented in this thesis aimed to characterize mechanisms of defence in HIV-exposed seronegative (HESN) individuals, who naturally resist infection by HIV, and HIV-controllers, who are HIV-infected, but suppress viral replication in the absence of treatment. Previous studies have linked resistance to HIV infection with low basal levels of gene transcription and reduced production of inflammatory mediators, suggesting an overall state of immune quiescence in HESN. Immune quiescence may also be protective in HIV-infected individuals, as immune activation drives disease progression. The central hypothesis of this thesis is that immune quiescence protects against HIV infection and disease progression by limiting the pool of activated target CD4+ T cells susceptible to HIV infection. This hypothesis was addressed by evaluating immune function in HESN from the Pumwani commercial sex worker cohort and HIV-controllers from the Manitoba elite controller cohort. In HESN, immune quiescence was marked by low levels of circulating activated T cells and low levels of the proinflammatory mediators IL-1α and IL-8 in the cervical mucosa. Regulatory T cells (Tregs), which suppress T cell activation, were elevated in HESN, and may represent a driver of immune quiescence. Low T cell activation and elevated Tregs were associated with reduced cellular susceptibility to infection in vitro. These data suggest that immune quiescence protects against infection by limiting the activated target CD4+ T cell pool, in support of the central hypothesis. HIV-controllers expressed low levels of the proinflammatory chemokines IP-10 and MCP-1 and low frequencies of activated T cells. These data demonstrate that immune quiescence is not only protective prior to exposure, but is also beneficial following infection. HIV-controllers also had elevated MIP-1α, reduced TGFβ and HIV-specific T cell proliferation responses, which contribute to protection by mechanisms other than immune quiescence. Taken together, these data support a role for immune quiescence in protection from HIV infection and disease progression. Mechanisms of reducing inflammation and target cell activation should be considered during future HIV vaccine and microbicide development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".