Targeted Proteomics of Human Metapneumovirus in Clinical Samples and Viral Cultures
Bibliographic record
Abstract
The rapid, sensitive, and specific identification of infectious pathogens from clinical isolates is a critical need in the hospital setting. Mass spectrometry (MS) has been widely adopted for identification of bacterial pathogens, although polymerase chain reaction remains the mainstay for the identification of viral pathogens. Here, we explored the capability of MS for the detection of human metapneumovirus (HMPV), a common cause of respiratory tract infections in children. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) sequencing of a single HMPV reference strain (CAN97-83) was used to develop a multiple reaction monitoring (MRM) assay that employed stable isotope-labeled peptide internal standards for quantitation of HMPV. Using this assay, we confirmed the presence of HMPV in viral cultures from 10 infected patients and further assigned genetic lineage based on the presence/absence of variant peptides belonging to the viral matrix and nucleoproteins. Similar results were achieved for primary clinical samples (nasopharyngeal aspirates) from the same individuals. As validation, virus lineages, and variant coding sequences, were confirmed by next-generation sequencing of viral RNA obtained from the culture samples. Finally, separate dilution series of HMPV A and B lineages were used to further refine and assess the robustness of the assay and to determine limits of detection in nasopharyngeal aspirates. Our results demonstrate the applicability of MRM for identification of HMPV, and assignment of genetic lineage, from both viral cultures and clinical samples. More generally, this approach should prove tractable as an alternative to nucleic-acid based sequencing for the multiplexed identification of respiratory virus infections.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".