HeLa cell response proteome alterations induced by mammalian reovirus T3D infection
Bibliographic record
Abstract
BACKGROUND: Cells are exposed to multiple stressors that induce significant alterations in signaling pathways and in the cellular state. As obligate parasites, all viruses require host cell material and machinery for replication. Virus infection is a major stressor leading to numerous induced modifications. Previous gene array studies have measured infected cellular transcriptomes. More recently, mass spectrometry-based quantitative and comparative assays have been used to complement such studies by examining virus-induced alterations in the cellular proteome. METHODS: We used SILAC (stable isotope labeling with amino acids in cell culture), a non-biased quantitative proteomic labeling technique, combined with 2-D HPLC/mass spectrometry and reciprocal labeling to identify and measure relative quantitative differences in HeLa cell proteins in purified cytosolic and nuclear fractions after reovirus serotype 3 Dearing infection. Protein regulation was determined by z-score analysis of each protein's label distribution. RESULTS: A total of 2856 cellular proteins were identified in cytosolic fractions by 2 or more peptides at >99% confidence and 884 proteins were identified in nuclear fractions. Gene ontology analyses indicated up-regulated host proteins were associated with defense responses, immune responses, macromolecular binding, regulation of immune effector processes, and responses to virus, whereas down-regulated proteins were involved in cell death, macromolecular catabolic processes, and tissue development. CONCLUSIONS: These analyses identified numerous host proteins significantly affected by reovirus T3D infection. These proteins map to numerous inflammatory and innate immune pathways, and provide the starting point for more detailed kinetic studies and delineation of virus-modulated host signaling pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".