Machine learning based predictive model and systems-level network of host-microbe interactions in post-COVID-19
Anukriti Verma1, Bhawna Rathi1
1Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, 201313, U.P, India.
Abstract:
Mucormycosis, a rare infection is caused by fungi Mucorales. The affiliation of mucormycosis with Coronavirus disease (COVID-19) is a rising issue of concern in India. There have been numerous case reports of association of rhino-cerebral-orbital, angioinvasive, pulmonary, respiratory and gastrointestinal tract related mucormycosis in patients with history of COVID-19. The immune dysregulation, preposterous use of steroids, interleukin-6-directed therapies and mechanical ventilation in COVID-19 immunocompromised individuals hypothesizes and predisposes to advancement of mucormycosis. The gaps in mode of presentation, disease course, diagnosis and treatment of post-COVID-19 mucormycosis requires critical analysis in order to control its morbidity and incidence and for prevention and management of opportunistic infections in COVID-19 patients. Our study performs machine learning, systems biology and bioinformatics analysis of post-COVID-19 mucormycosis in India incorporating multitudinous techniques. Text mining identifies candidate characteristics of post-COVID-19 mucormycosis cases including city, gender, age, symptoms, clinical parameters, microorganisms and treatment. The characteristics are incorporated in a machine learning based disease model resulting in predictive potentiality of characteristics of post-COVID-19 mucormycosis. The characteristics are used to create a host-microbe interaction disease network comprising of interactions between microorganism, host-microbe proteins, non-specific markers, symptoms and drugs resulting in candidate molecules. R1A (Replicase polyprotein 1a) and RPS6 (Ribosomal Protein S6) are yielded as potential drug target and biomarker respectively via potentiality analysis and expression in patients. The potential risk factors, drug target and biomarker can serve as prognostic, early diagnostic and therapeutic molecules in post-COVID-19 mucormycosis requiring further experimental validation and analysis on post-COVID-19 mucormycosis cases.
Insights
Post-COVID-19 mucormycosis is a serious fungal infection linked to immune dysregulation. This study identifies risk factors, a potential drug target (Replicase polyprotein 1a), and a biomarker (Ribosomal Protein S6) for better management.
Area of Science:
- Medical Mycology
- Infectious Diseases
- Computational Biology
Background:
- Mucormycosis, a rare fungal infection, is increasingly associated with Coronavirus disease (COVID-19), particularly in India.
- Cases involve rhino-cerebral-orbital, angioinvasive, pulmonary, and gastrointestinal manifestations.
- Factors like immune dysregulation, steroid use, IL-6 therapies, and mechanical ventilation in COVID-19 patients contribute to mucormycosis development.
Purpose of the Study:
- To analyze the presentation, course, diagnosis, and treatment of post-COVID-19 mucormycosis.
- To identify risk factors, drug targets, and biomarkers for this opportunistic infection.
- To develop predictive models for post-COVID-19 mucormycosis using machine learning and bioinformatics.
Main Methods:
- Machine learning, systems biology, and bioinformatics analyses were employed.
- Text mining identified key characteristics of post-COVID-19 mucormycosis cases.
- A host-microbe interaction network was constructed to identify candidate molecules.
Main Results:
- Candidate characteristics of post-COVID-19 mucormycosis were identified, including demographics, symptoms, and clinical parameters.
- A machine learning model demonstrated predictive potential for these characteristics.
- Replicase polyprotein 1a (R1A) was identified as a potential drug target, and Ribosomal Protein S6 (RPS6) as a potential biomarker.
Conclusions:
- Identified risk factors, drug targets, and biomarkers can aid in the prognosis, early diagnosis, and therapy of post-COVID-19 mucormycosis.
- Further experimental validation is required for these findings.
- This research contributes to understanding and managing opportunistic infections in COVID-19 survivors.
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