Proteoform-Specific Insights into Cellular Proteome Regulation
Bibliographic record
Abstract
Knowledge regarding compositions of proteomes at the proteoform level enhances insights into cellular phenotypes. A strategy is described herein for discovery of proteoform-specific information about cellular proteomes. This strategy involved analysis of data obtained by bottom-up mass spectrometry of multiple protein OGE separations on a fraction by fraction basis. The strategy was exemplified using five matched sets of lysates of uninfected and human respiratory syncytial virus-infected A549 cells. Template matching demonstrated that 67.3% of 10475 protein profiles identified focused to narrow pI windows indicative of efficacious focusing. Furthermore, correlation between experimental and theoretical pI gradients indicated reproducible focusing. Based on these observations a proteoform profiling strategy was developed to identify proteoforms, detect proteoform diversity and discover potential proteoform regulation. One component of this strategy involved examination of the focusing profiles for protein groups. A novel concordance analysis facilitated differentiation between proteoforms, including proteoforms generated by alternate splicing and proteolysis. Evaluation of focusing profiles and concordance analysis were applicable to cells from a single and/or multiple biological states. Statistical analyses identified proteoform variation between biological states. Regulation relevant to cellular responses to human respiratory syncytial virus was revealed. Western blotting and Protomap analyses validated the proteoform regulation. Discovery of STAT1, WARS, MX1, and HSPB1 proteoform regulation by human respiratory syncytial virus highlighted the impact of the profiling strategy. Novel truncated proteoforms of MX1 were identified in infected cells and phosphorylation driven regulation of HSPB1 proteoforms was correlated with infection. The proteoform profiling strategy is generally applicable to investigating interactions between viruses and host cells and the analysis of other biological systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".