Genetic and Phenotypic Investigations of Viral Subpopulations Detected in Different Tissues of Laying Hens Following Infectious Bronchitis Virus Infection
Bibliographic record
Abstract
Infectious bronchitis virus (IBV) commonly produces a range of genetic sequences during replication, particularly in the spike 1 (S1)-coding portion of the S gene, leading to distinct subpopulations within the broader viral population. It has been shown that certain microenvironments exert selective pressure on the S1-coding sequences and their encoded proteins, influencing the selection of viral subpopulations in these environments. In this study, high-throughput next-generation sequencing (NGS) was used to analyze the S1-coding sequences from tissues of the respiratory, digestive, renal, and reproductive systems of specific pathogen-free (SPF) laying hens. These tissues were collected nine days after infection with the California 1737/04 (CA1737/04) IBV strain, which is known to cause varying degrees of pathology in these tissues. Using a specific bioinformatics pipeline, 27 single nucleotide variants (SNVs) were detected in the S1-coding sequences derived from different tissues. These SNVs shaped multiple subpopulations (SP1-SP15), with SP1 being the core subpopulation present in all tissues, while others were tissue-specific. The IBV RNA loads in the tissues were negatively correlated with the number of SNVs or the Shannon entropy values, and phylogenetic analysis revealed a genetic divergence in the S1-coding sequences from certain tissues with lower viral RNA loads, particularly those from the trachea and ovary. Furthermore, the SNVs were associated with nonsynonymous mutations, primarily located in hypervariable region 2 (HVR 2) within the N-terminal domain of S1 (S1-NTD), except for those in SP7, which was exclusive to the trachea and contained changes in HVR 3 in the C-terminal domain of S1 (S1-CTD). Overall, this study adds to the existing knowledge about IBV evolution by highlighting the role of tissue-specific environments in shaping viral genetic diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".