Thymosin-α1 binds with ACE and downregulates the expression of ACE2 in human respiratory epithelia.

BACKGROUND: Thymosin-α1 has been implicated into the treatment of novel respiratory virus Coronavirus Disease 2019 (COVID-19), but the underlying mechanisms are still disputable. AIM: Herein we aimed to reveal a previously unrecognized mechanism that thymosin-α1 prevents COVID-19 by binding with angiotensin-converting enzyme (ACE), which was inspired from the tool of network pharmacology. METHODS: KEGG pathway enrichment of thymosin-α1 treating COVID-19 was analyzed by Database of Functional Annotation Bioinformatics Microarray Analysis, then core targets were validated by ligand binding kinetics assay and fluorometric detection of ACE and ACE2 enzymatic activity. The production of angiotensin I, angiotensin II, angiotensin (1-7) and angiotensin (1-9) were detected by enzyme linked immunosorbent assay. RESULTS: We found that thymosin-α1 impaired the expressions of angiotensin-converting enzyme 2 and angiotensin (1-7) of human lung epithelial cells in a dose-dependent way (p < 0.001). In contrast, thymosin-α1 had no impact on their ACE and angiotensin (1-9) expressions but significantly inhibited the enzymatic activity of ACE (p > 0.05). CONCLUSION: The bioinformatic findings of network pharmacology and the corresponding pharmacological validations have revealed that thymosin-α1 treatment could decrease ACE2 expression in human lung epithelial cells, which strengthens the potential clinical applications of thymosin-α1 to prevent severe acute respiratory syndrome coronavirus 2 infection.

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles.

Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways. This muscle subtype has the capacity for controlled or regulated contraction and relaxation. Airway smooth muscles are a unique type of smooth muscles that constitute the effective, adjustable, and reactive wall that covers most areas of the entire airway from the trachea to lung tissues. Infection with SARS-CoV-2, which caused the world-wide COVID-19 pandemic, involves airway smooth muscles and their surrounding inflammatory environment. Therefore, airway smooth muscles and related inflammatory factors may play an irreplaceable role in the initiation and progression of several severe diseases. Many previous studies have attempted to reveal the potential relationships between interleukins and airway smooth muscle cells only on the omics level, and the continued existence of numerous false-positive optimal genes/transcripts cannot reflect the actual effective biological mechanisms underlying interleukin-based activation effects on airway smooth muscles. Here, on the basis of newly presented machine learning-based computational approaches, we identified specific regulatory factors and a series of rules that contribute to the activation and stimulation of airway smooth muscles by IL-13, IL-17, or the combination of both interleukins on the epigenetic and/or transcriptional levels. The detected discriminative factors (genes) and rules can contribute to the identification of potential regulatory mechanisms linking airway smooth muscle tissues and inflammatory factors and help reveal specific pathological factors for diseases associated with airway smooth muscle inflammation on multiomics levels.

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.