Enhancement of mitochondrial function fosters B cell immune memory
Bibliographic record
Abstract
Abstract Differentiation of T and B cells to effector and memory cell fates are associated with extensive metabolic changes which are accompanied by altered mitochondrial dynamics. However, whether alterations in mitochondrial structure and function plays an active role in regulating effector versus memory cell fate decisions during immune responses remains unclear. Our studies here characterize changes in mitochondrial dynamics in activated B cells and show that increased mitochondrial mass and activity is a distinct feature of memory B cell lineage commitment in vivo . Using a directed screen of mitochondrial modulators, we identify mitochondrial fission inhibitor, Mdivi-1 as an agent that could enhance mitochondrial mass and function leading to augmented memory B cell differentiation. The enhanced memory B cell responses mediated by Mdivi-1, translated to more robust recall responses upon secondary antigen exposures. Moreover, Mdivi-1 when used in combination with subunit (SARS-CoV2) and inactivated (H1N1 influenza) vaccines led to remarkably improved vaccine efficacies and protection from lethal viral (H1N1) challenge. Single-cell transcriptomics revealed enhanced commitment to memory lineage differentiation in B cells following Mdivi-1 treatment. We propose that mitochondrial modulators such as Mdivi-1 are a novel class of “immune enhancers” that specifically reinforces immunological memory and could be broadly applied to improve the fidelity of immune responses and vaccine efficacies.
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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".