Integrated Analysis of microRNAs and Metabolomics in Rat’s Serum Reveals Multi-action Modes of Qingfei Paidu Decoction for COVID-19 Treatment
Bibliographic record
Abstract
Abstract Background During the fight against coronavirus disease 2019 (COVID-19) in China, Qingfei Paidu decoction (QFPDD) has been widely applied to treat COVID-19 patients. Retrospective studies showed that QFPDD could improve clinical outcomes of COVID-19. Thus, it is necessary and interesting to explore the action mode of QFPDD for further application and development.MethodsSprague-Dawley (SD) rats were randomly divided into two groups, QFPDD (n=9) and control (n=10) groups. They were parallelly treated for 12 days with QFPDD and warm distilled water, respectively. At the endpoint, the microRNA (miRNA or miR) profiles in serum were detected to identify differently expressed miRNAs (DEMs). Then, the action mode of QFPDD were explored via review of potential roles of DEMs and functional enrichment analysis of their targets (e.g., GO enrichment and KEGG pathway analysis), especially focusing on the aspects of immunity, inflammation, virus infection and pulmonary fibrosis. Core genes were identified based on KEGG pathway analysis. Metabolomics were detected in serum and significantly changed metabolites (SCMs), especially the metabolic substrates and products of enzyme of core gene were identified as biomarkers to validate the regulation of DEMs to enzyme activity of core gene through metabolomic analysis and linear correlation analysis between SCMs and DEMs. Results23 DEMs were identified in the serum between QFPDD and control groups, with 1636 predicted genes. Reported evidence has showed that both the DEMs and their target genes involve regulation of immunity, inflammation, virus infection and pulmonary fibrosis. Phospholipase C, gamma 1 (Plcg1) was identified as a core gene and predicted to be upregulated attributed to downregulation of novel-89-mature. The levels of three SCMs, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)), PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) and PC(16:1(9Z)/16:1(9Z)), which were the metabolic substrates of phospholipase C, were significantly reduced in QFPDD group, in addition, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)) and PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) presented positively linear correlation with the expression level of novel-89-mature. The level of phosphorylcholine, a product of PCs metabolized by phospholipase C, was significantly elevated in QFPDD group. Conclusion QFPDD can induce modification of miRNAs profile, and subsequently multi-regulate the immunity, inflammation, virus infection and pulmonary fibrosis in vivo, playing an important role for the positive outcomes of COVID-19 patients treated by QFPDD in China.
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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) | 0.001 | 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".