Proteomics analysis reveals heat shock proteins involved in caprine parainfluenza virus type 3 infection
Bibliographic record
Abstract
BACKGROUND: Caprine parainfluenza virus type 3 (CPIV3) is major pathogen of goat herds causing serious respiratory tract disease and economic losses to the goat industry in China. We analyzed the differential proteomics of CPIV3-infected Madin-Darby bovine kidney (MDBK) cells using quantitative iTRAQ coupled LC-MS/MS. In addition, four DEPs were validated by qRT-PCR and western blot analysis. RESULTS: Quantitative proteomics analysis revealed 163 differentially expressed proteins (DEPs) between CPIV3-infected and mock-infected groups (p-value < 0.05 and fold change > 1.2), among which 91 were down-regulated and 72 were up-regulated. Gene ontology (GO) analysis showed that these DEPs were involved in molecular functions, cellular components and biological processes. Biological functions in which the DEPs were involved in included diseases, genetic information processing, metabolism, environmental information processing, cellular processes, and organismal systems. STRING analysis revealed that four heat shock proteins (HSPs) included HSPA5, HSPA1B, HSP90B1 and HSPA6 may be associated with proliferation of CPIV3 in MDBK cells. qRT-PCR and western blot analysis showed that the selected HSPs were identical to the quantitative proteomics data. CONCLUSION: To our knowledge, this is the first report of the proteomic changes in MDBK cells after CPIV3 infection.
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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".