Regulation of host gene expression by gastrointestinal tract microbiota in Chinook Salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Differences in gut microbiome composition are linked with health, disease and ultimately host fitness; however, the molecular mechanisms underlying that relationship are not well characterized. Here, we modified the fish gut microbiota using antibiotic and probiotic feed treatments to address the effect of host microbiome on gene expression patterns. Chinook salmon (Oncorhynchus tshawytscha) gut gene expression was evaluated using whole transcriptome sequencing (RNA-Seq) on hindgut mucosa samples from individuals treated with antibiotic, probiotic and control diets to determine differentially expressed (DE) host genes. Fifty DE host genes were selected for further characterization using nanofluidic qPCR chips. We used 16S rRNA gene metabarcoding to characterize the rearing water and host gut microbiome (bacterial) communities. Daily administration of antibiotics and probiotics resulted in significant changes in fish gut and aquatic microbiota as well as more than 100 DE genes in the antibiotic and probiotic treatment fish, relative to healthy controls. Normal microbiota depletion by antibiotics mostly led to downregulation of different aspects of immunity and upregulation of apoptotic process. In the probiotic treatment, genes related to post-translation modification and inflammatory responses were up-regulated relative to controls. Our qPCR results revealed significant effects of treatment (antibiotic and probiotic) on rabep2, aifm3, manf, prmt3 gene transcription. Moreover, we found significant associations between members of Lactobacillaceae and Bifidobacteriaceae with host gene expression patterns. Overall, our analysis showed that the microbiota had significant impacts on many host signalling pathways, specifically targeting immune, developmental and metabolic processes. Our characterization of some of the molecular mechanisms involved in microbiome-host interactions will help develop new strategies for preventing/ treating microbiome disruption-related diseases.
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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".