COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine

Abstract

During the COVID-19 pandemic, it became apparent that precision medicine relies heavily on biological multi-omics discoveries. High throughput omics technologies, such as host genomics, transcriptomics, proteomics, epigenomics, metabolomics/lipidomics, and microbiomics, have become an integral part of precision diagnostics. The large number of data generated by omics technologies allows for the identification of vulnerable demographic populations that are susceptible to poor disease outcomes. Additionally, these data help to pinpoint the omics-based biomarkers that are currently driving advancements in precision and preventive medicine, such as early diagnosis and disease prognosis, individualized treatments, and vaccination. This report summarizes COVID-19-omic studies, highlights the results of completed and ongoing omics investigations in individuals who have experienced severe disease outcomes, and examines the impact that repurposed/novel antiviral drugs, targeted immunotherapeutics, and vaccines have had on individual and public health.

Molecular Diagnostics of Cryptococcus spp. and Immunomics of Cryptococcosis-Associated Immune Reconstitution Inflammatory Syndrome

“…Furthermore, the administration of mRNA vaccines for newly emerged infectious diseases may precipitate type I/II interferon production by lymphocytes and monocyte/macrophages, which highlights the possibility of inadvertently inciting an inflammatory reaction akin to IRIS [99]. Thus, it is important to continue studying the IRIS-like inflammation, expanding the omics portfolio to include genomics, metabolomics, and, moreover, dual host-pathogen RNA-sequencing methodologies [100,101]. In the pursuit of a comprehensive understanding of immune reconstitution inflammatory syndrome, a reliance on high-throughput laboratory methodologies in understanding antimicrobial resistance, computer-aided drug design, and the discovery of antimicrobial nanomaterials are imperative [1,[102][103][104].…”

“…Since IRIS-like inflammation is largely driven by microbial antigens, there has been an everincreasing need for novel machine learning models to predict neutralizing therapeutic peptides that would lessen immune response [105]. The integration of IRIS multiomics data modeling and predictive analytics emerges as indispensable in formulating evidence-based solutions to the multifaceted challenges encountered in the clinical, biomedical, and public health domains [100,106].…”