Perturbed mitochondrial dynamics, an emerging aspect of epithelial-microbe interactions
Bibliographic record
Abstract
Mitochondria exist in a complex network that is constantly remodeling via the processes of fission and fusion in response to intracellular conditions and extracellular stimuli. Excessive fragmentation of the mitochondrial network because of an imbalance between fission and fusion reduces the cells' capacity to generate ATP and can be a forerunner to cell death. Given the critical roles mitochondria play in cellular homeostasis and innate immunity, it is not surprising that many microbial pathogens can disrupt mitochondrial activity. Here we note the putative contribution of mitochondrial dysfunction to gut disease and review data showing that infection with microbial pathogens can alter the balance between mitochondrial fragmentation and fusion, preventing normal remodeling (i.e., dynamics) and can lead to cell death. Current data indicate that infection of epithelia or macrophages with microbial pathogens will ultimately result in excessive fragmentation of the mitochondrial network. Concerted research efforts are required to elucidate fully the processes that regulate mitochondrial dynamics, the mechanisms by which microbes affect epithelial mitochondrial fission and/or fusion, and the implications of this for susceptibility to infectious disease. We speculate that the commensal microbiome of the gut may be important for normal epithelial mitochondrial form and function. Drugs designed to counteract the effect of microbial pathogen interference with mitochondrial dynamics may be a new approach to infectious disease at mucosal surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".