Cataloguing of Potential HIV Susceptibility Factors during the Menstrual Cycle of Pig-Tailed Macaques by Using a Systems Biology Approach
Bibliographic record
Abstract
UNLABELLED: Our earlier studies with pig-tailed macaques demonstrated various simian-human immunodeficiency virus (SHIV) susceptibilities during the menstrual cycle, likely caused by cyclic variations in immune responses in the female genital tract. There is concern that high-dose, long-lasting, injectable progestin-based contraception could mimic the high-progesterone luteal phase and predispose women to human immunodeficiency type 1 (HIV-1) acquisition and transmission. In this study, we adopted a systems biology approach employing proteomics (tandem mass spectrometry), transcriptomics (RNA microarray hybridization), and other specific protein assays (enzyme-linked immunosorbent assays and multiplex chemokine and cytokine measurements) to characterize the effects of hormonal changes on the expression of innate factors and secreted proteins in the macaque vagina. Several antiviral factors and pathways (including acute-phase response signaling and complement system) were overexpressed in the follicular phase. Conversely, during the luteal phase there were factors overexpressed (including moesins, syndecans, and integrins, among others) that could play direct or indirect roles in enhancing HIV-1 infection. Thus, our study showed that specific pathways and proteins or genes might work in tandem to regulate innate immunity, thus fostering further investigation and future design of approaches to help counter HIV-1 acquisition in the female genital tract. IMPORTANCE: HIV infection in women is poorly understood. High levels of the hormone progesterone may make women more vulnerable to infection. This could be the case during the menstrual cycle, when using hormone-based birth control, or during pregnancy. The biological basis for increased HIV vulnerability is not known. We used an animal model with high risk for infection during periods of high progesterone. Genital secretions and tissues during the menstrual cycle were studied. Our goal was to identify biological factors upregulated at high progesterone levels, and we indeed show an upregulation of genes and proteins which enhance the ability of HIV to infect when progesterone is high. In contrast, during low-progesterone periods, we found more HIV inhibitory factors. This study contributes to our understanding of mechanisms that may regulate HIV infection in females under hormonal influences. Such knowledge is needed for the development of novel prevention strategies.
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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) | 0.000 | 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".