Fawang Du,1,* Hanchao Wang,2,* Zhihong Chen,3,* Wei Xiong,1 Qin Wang,4 Bo Li,1 Rong Li,1 Li Li,1 Yongchun Shen,5 Tao Zhu1 

1Department of Respiratory Medicine and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, 629000, People’s Republic of China; 2GK Health and Medical Big Data Research Center of Suining, Suining Central Hospital, Suining, Sichuan, 629000, People’s Republic of China; 3Department of Respiratory Medicine and Critical Care Medicine, Zhongshan Hospital of Fudan University, and Shanghai Institute of Respiratory Disease, Shanghai, 20032, People’s Republic of China; 4Department of Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, People’s Republic of China; 5Department of Respiratory Medicine and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Tao Zhu, Head of Respiratory Medicine and Critical Care Medicine, Suining Central Hospital, NO. 27. North Dongping Street, Hedong District, Suining, Sichuan, People’s Republic of China, Tel +86-18884887280, Email zhutao063020@163.com Yongchun Shen, Respiratory Medicine and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, People’s Republic of China, Tel +86 13540758657, Email shen_yongchun@126.com

Rational: Asthma severity assessment is essential for asthma management. Transcriptomics contributes substantially to asthma pathogenesis. Then, this study aimed to explore asthma severity-associated transcriptomics profile and promising biomarkers for asthma severity prediction.
Methods: In discovery cohort, induced sputum cells from 3 non-severe and 3 severe asthma patients were collected and analyzed using RNA-seq. Multivariate analysis was performed to explore asthma severity-associated transcriptomics profile and differential expressed genes (DEGs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for pathway enrichment analysis. Subsequently, based on the previous study and clinical experience, the mRNA expressions of 6 overlapped asthma severity-associated DEGs and C3 in induced sputum cells and serum C3 were verified in validation cohort.
Results: Distinct asthma severity-associated transcriptomics profile was identified in induced sputum cells in discovery cohort. Then, 345 DEGs were found, of which 38 terms and 32 pathways were enriched using GO and KEGG, respectively. In validation cohort, the mRNA expressions of ZNF331, CD163, MACC1, ADAMTS2, and C3 were increased, and RYR1 and NRXN3 were decreased in induced sputum cells in severe asthma. Meanwhile, the AUC of ROC was 0.890 for serum C3 in asthma severity prediction, with the best cut-off of 1.272 g/L.
Conclusion: Collectively, this study provides the first identification of the association between induced sputum cells transcriptomics profile and asthma severity, indicating the potential value of transcriptomics for asthma management. The study also reveals the promising value of serum C3 for predicting asthma severity in clinical practice.

Keywords: asthma severity, induced sputum, RNA-seq, serum C3, ACT scores, FeNO