Transcriptomic Analysis Exploring the Molecular Mechanisms of Hanchuan Zupa Granules in Alleviating Asthma in Rat

Zi-Su-Zi decoction improves airway hyperresponsiveness in cough-variant asthma rat model through PI3K/AKT1/mTOR, JAK2/STAT3 and HIF-1α/NF-κB signaling pathways

ETHNOPHARMACOLOGICAL RELEVANCE

Cough-variant asthma (CVA) is one of the most common causes of chronic cough. Its pathogenesis is closely related to chronic airway inflammation and airway hyperresponsiveness. CVA belongs to the category of "wind cough" in Traditional Chinese medicine (TCM). Zi-Su-Zi decoction (ZSD) is a Chinese herbal formula that is clinically used for the treatment of cough and asthma, especially CVA. However, the mechanism of action remains unclear.

AIM OF THE STUDY

In this study, we aimed to explore the potential mechanism by which ZSD improves CVA airway hyperresponsiveness.

MATERIALS AND METHODS

The targets of ZSD in CVA were studied using a Network pharmacology. The main chemical components of ZSD were detected and analyzed using ultra-high-pressure liquid chromatography (UHPLC-MS/MS). In animal experiments, the rat model of CVA was established using Ovalbumin (OVA)/Aluminum hydroxide (AL(OH)3) sensitization. Moreover, the experiment also evaluated cough symptoms, percentage of eosinophils (EOS%), pulmonary function tests, histopathological sections, blood cytokine levels, mRNA and protein levels.

RESULTS

The results showed that Network pharmacology suggested 276 targets of ZSD and CVA and found that ZSD treatment with CVA was closely related to the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. UHPLC-MS/MS revealed that ZSD contained 52 main chemical components. Compared with the model group, the cough symptoms of the rats in the different ZSD concentration groups were relieved, the EOS% index was lowered, and body weight was increased. HE staining showed that ZSD reduced airway inflammation, edema and hyperplasia, thereby improving the pathological structure of lung tissue, and the effect of high-dose ZSD was especially significant. Our most important finding was that ZSD blocked the entry of hypoxia-inducible factor-1α (HIF-1α), signal transducer and activator of transcription-3 (STAT3) and nuclear factor kappa-B (NF-κB) into the nucleus by interfering with PI3K/AKT1/mechanistic target of rapamycin (mTOR), and janus kinase 2 (JAK2) signaling factors. Consequently, inhibiting the release of cytokines and immunoglobulin-E, thereby reducing airway hyperresponsiveness (AHR) and partially reverses airway remodeling.

CONCLUSIONS

This study showed that ZSD can improve airway hyperresponsiveness and partially reverse airway remodeling by inhibiting the PI3K/AKT1/mTOR, JAK2/STAT3 and HIF-1α/NF-κB signaling pathways. Therefore, ZSD is an effective prescription for the treatment of CVA.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.