Identification of Cell Types and Transcriptome Landscapes of Porcine Epidemic Diarrhea Virus–Infected Porcine Small Intestine Using Single-Cell RNA Sequencing
Bibliographic record
Abstract
Swine coronavirus-porcine epidemic diarrhea virus (PEDV) with specific susceptibility to pigs has existed for decades, and recurrent epidemics caused by mutant strains have swept the world again since 2010. In this study, single-cell RNA sequencing was used to perform for the first time, to our knowledge, a systematic analysis of pig jejunum infected with PEDV. Pig intestinal cell types were identified by representative markers and identified a new tuft cell marker, DNAH11. Excepting enterocyte cells, the goblet and tuft cells confirmed susceptibility to PEDV. Enrichment analyses showed that PEDV infection resulted in upregulation of cell apoptosis, junctions, and the MAPK signaling pathway and downregulation of oxidative phosphorylation in intestinal epithelial cell types. The T cell differentiation and IgA production were decreased in T and B cells, respectively. Cytokine gene analyses revealed that PEDV infection downregulated CXCL8, CXCL16, and IL34 in tuft cells and upregulated IL22 in Th17 cells. Further studies found that infection of goblet cells with PEDV decreased the expression of MUC2, as well as other mucin components. Moreover, the antimicrobial peptide REG3G was obviously upregulated through the IL33-STAT3 signaling pathway in enterocyte cells in the PEDV-infected group, and REG3G inhibited the PEDV replication. Finally, enterocyte cells expressed almost all coronavirus entry factors, and PEDV infection caused significant upregulation of the coronavirus receptor ACE2 in enterocyte cells. In summary, this study systematically investigated the responses of different cell types in the jejunum of piglets after PEDV infection, which deepened the understanding of viral pathogenesis.
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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 | 0.001 | 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".