Klebsiella pneumoniae infection causes mitochondrial damage and dysfunction in bovine mammary epithelial cells
Bibliographic record
Abstract
Klebsiella pneumoniae, an important cause of bovine mastitis worldwide, is strongly pathogenic to bovine mammary epithelial cells (bMECs). Our objective was to determine the role of mitochondrial damage in the pathogenicity of K. pneumoniae on bMECs, by assessing several classical indicators of mitochondrial dysfunction, as well as differentially expressed genes (DEGs). Two K. pneumoniae strains (HLJ-D2 and HB-AF5), isolated from cows with clinical mastitis (CM), were used to infect bMECs (MAC-T line) cultured in vitro. In whole-transcriptome analysis of bMECs at 6 h post-infection (hpi), there were 3453 up-regulated and 3470 down-regulated genes for HLJ-D2, whereas for HB-AF5, there were 2891 up-regulated and 3278 down-regulated genes (P < 0.05). Based on GO term enrichment of differentially expressed genes (DEGs), relative to the controls, the primary categories altered in K. pneumoniae-infected bMECs included cellular macromolecule metabolism, metabolic process, binding, molecular function, etc. Infections increased (P < 0.05) malondialdehyde concentrations and formation of reactive oxygen species in bMECs. Additionally, both bacterial strains decreased (P < 0.05) total antioxidant capacity in bMECs at 6 and 12 hpi. Furthermore, infections decreased (P < 0.05) mitochondrial membrane potential and increased (P < 0.01) mitochondrial calcium concentrations. Finally, severe mitochondrial swelling and vacuolation, as well as mitochondrial rupture and cristae degeneration, were detected in infected bMECs. In conclusion, K. pneumoniae infections induced profound mitochondrial damage and dysfunction in bMECs; we inferred that this caused cellular damage and contributes to the pathogenesis of K. pneumoniae-induced CM in dairy cows.
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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 | 0.001 |
| 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".