Influenza A virus subtype H9N2 infection disrupts the composition of intestinal microbiota of chickens
Bibliographic record
Abstract
The impact of low pathogenic influenza viruses such as subtype H9N2, which infect the respiratory and the gastrointestinal tracts of chickens, on microbial composition are not known. Twenty-day-old specific pathogen-free chickens were assigned to two treatment groups, control (uninfected) and H9N2-infected (challenged via the oral-nasal route). Fecal genomic DNA was extracted, and the V3-V4 regions of the 16S rRNA gene were sequenced using the Illumina Miseq® platform. Sequences were curated using Mothur as described in the MiSeq SOP. Infection of chickens with H9N2 resulted in an increase in phylum Proteobacteria, and differential enrichment with the genera Vampirovibrio, Pseudoflavonifractor, Ruminococcus, Clostridium cluster XIVb and Isobaculum while control chickens were differentially enriched with genera Novosphingobium, Sphingomonas, Bradyrhizobium and Bifidobacterium. Analysis of pre- and post-H9N2 infection of the same chickens showed that, before infection, the fecal microbiota was characterized by Lachnospiracea and Ruminococcaceae family and the genera Clostridium sensu stricto, Roseburia and Lachnospiraceae incertae sedis. However, post-H9N2 infection, class Deltaproteobacteria, orders Clostridiales and Bacteroidiales and the genus Alistipes were differentially enriched. Findings from the current study show that influenza virus infection in chickens results in the shift of the gut microbiota, and the disruption of the host-microbial homeostasis in the gut might be one of the mechanisms by which influenza virus infection is established in chickens.
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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) | 0.000 | 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".