Levofloxacin induces differential effects in the transcriptome between the gut, peripheral and axial joints in the Spondyloarthritis DBA/1 mice: Improvement of intestinal dysbiosis and the overall inflammatory process
Bibliographic record
Abstract
To analyze the effect of levofloxacin-induced intestinal microbiota modifications on intestinal, joint, and systemic inflammation in the DBA/1 mice with spontaneous arthritis. The study included two groups of mice, one of which received levofloxacin. The composition and structure of the microbiota were determined in the mice's stool using 16S rRNA sequencing; the differential taxa and metabolic pathway between mice treated with levofloxacin and control mice were also defied. The effect of levofloxacin was evaluated in the intestines, hind paws, and spines of mice through DNA microarray transcriptome and histopathological analyses; systemic inflammation was measured by flow cytometry. Levofloxacin decreased the pro-inflammatory bacteria, including Prevotellaceae, Odoribacter, and Blautia, and increased the anti-inflammatory Muribaculaceae in mice's stool. Histological analysis confirmed the intestinal inflammation in control mice, while in levofloxacin-treated mice, inflammation was reduced; in the hind paws and spines, levofloxacin also decreased the inflammation. Microarray showed the downregulation of genes and signaling pathways relevant in spondyloarthritis, including several cytokines and chemokines. Levofloxacin-treated mice showed differential transcriptomic profiles between peripheral and axial joints and intestines. Levofloxacin decreased the expression of TNF-α, IL-23a, and JAK3 in the three tissues, but IL-17 behaved differently in the intestine and the joints. Serum TNF-α was also reduced in levofloxacin-treated mice. Our results suggest that the microbiota modification aimed at reducing pro-inflammatory and increasing anti-inflammatory bacteria could potentially be a coadjuvant in treating inflammatory arthropathies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".