Molecular characterization of the cervical and systemic B-cell repertoire
Bibliographic record
Abstract
The cervical mucosa of women who are highly exposed to HIV-1, yet remain persistently seronegative (HEPS), presents a unique opportunity to study the dynamics of an immune compartment potentially capable of preventing HIV-1 infection. Herein, we provide a detailed characterization of the immunoglobulin repertoire of cervical and systemic B cells from one such HEPS individual from Nairobi, Kenya. Analysis was done on 512 VH sequences that were RT-PCR amplified from B cells in a paired sample from the cervix and peripheral blood. The VH3 and DH repertoire of class switched cervical B cells differs significantly from that of systemic B cells indicating that the cervical environment affects local B cell populations and hence VH gene expression. Six networks of clonally related, heavily mutated B cells were identified that spanned the systemic and cervical B cell compartments. Analysis of somatic mutations suggests this is likely the result of systemic, class switched B cells homing to the cervical mucosa. Multiple networks of somatically mutated V-gene sequences, unique to the cervical mucosa, were also identified. This supports the notion that site specific responses occur and have unique regulation of tolerance and recruitment into local memory or blast B cell compartments. We conclude that while the nature of the cervical environment shapes the local B cell repertoire, the infusion of post germinal center B cells to the human cervix is a common occurrence, and represents a means by which systemic immunization could provide the local antibodies necessary to prevent HIV-1 at the site of initial contact.
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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 | 0.000 |
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".