Monocyte single cell-type gene expression measured in peripheral blood by DIRECT LS-TA method: the ratio-based biomarkers of ( <i>IFI27/PSAP</i> ) showed superior performance than interferon score in triage patients with viral infection
Bibliographic record
Abstract
Abstract A rapid method to triage febrile patients into different categories of etiologies remains a significant challenge even nowadays, when many molecular tests for pathogens are available. Routine serum protein tests like C-reactive protein and procalcitonin have limited specificity. Host response gene signatures are promising biomarkers but they usually require assaying many genes, e.g. 7 genes are commonly used to calculate the interferon (IFN) score. However, these gene panels fail to capture cell-type-specific host responses. Measuring gene expression of a specified single cell population, like monocytes, offers enhanced biological insight. However, it currently requires laborious cell sorting or costly single-cell sequencing techniques, limiting its clinical applicability. This study aims to develop a simple ratio-based biomarker (RBB) representing monocyte-specific host response to viral infection called DIRECT LS-TA method. A simple ratio of 2 genes (both are shortlist monocyte informative genes) quantified in peripheral blood (PB) samples correlated with gene expression in purified monocytes in the corresponding individual. These RBBs cover 3 interferon-stimulated genes (ISGs): IFI27/PSAP , IFI44L/PSAP and SIGLEC1/PSAP . They are compared to the conventional multi-gene IFN score in the differentiation of viral infection. Public gene expression datasets from NCBI GEO were used to shortlist monocyte-informative genes that can be used as the RBB in PB. The DIRECT LS-TA RBB was calculated as the ratio of the target ISG transcript abundance (TA) to that of another reference gene ( PSAP or CTSS) directly quantified from bulk PB data (e.g., Log( IFI27/PSAP ) in WB). The correlation (expressed by coefficient of determination, R²) between these DIRECT LS-TA RBBs and the gold-standard target gene TA measured in purified monocytes was assessed. The diagnostic performance of selected RBBs ( IFI27/PSAP , IFI44L/PSAP , SIGLEC1/PSAP) was compared against the conventional 8-gene IFN score for differentiating viral infections from controls. Direct LS-TA RBBs measured in PB showed strong correlation with gold-standard gene expression measured in purified monocytes (R 2 ranged from 0.53 for the target gene IFI27 to >0.9 for the target gene IFI44L ). This high level of correlation supports that this simple RBB (DIRECT LS-TA) method can replace the tedious cell sorting approach to obtain single-cell-type gene expression data. All DIRECT LS-TA results of ISGs were raised during viral infection. The best clinical performance in triaging viral infection patients was achieved by IFI27/PSAP or IFI27/CTSS across all datasets. For example, in the GSE111368 dataset, IFI27/PSAP achieved an AUC of 0.94 (95% CI 0.90-0.97) with 88% sensitivity and 95% specificity, surpassing the IFN score’s AUC of 0.90 (95% CI 0.85-0.94) with 79% sensitivity and 93% specificity. Conclusion The DIRECT LS-TA method, utilizing the format of simple two-gene ratio-based biomarkers like IFI27/PSAP , provides a robust and accurate measure of monocyte-specific interferon pathway activation directly from peripheral blood. The superior performance of the DIRECT LS-TA method makes it a promising, readily implementable tool for clinical triage. Its ability to provide single-cell-type specific information, rapid turnaround using standard qPCR/dPCR technology, and enhanced biological specificity make it a valuable molecular host response assessment.
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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 | 0.001 |
| 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) | 0.002 | 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 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".