Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity
Anoop T. Ambikan, Hong Yang, Shuba Krishnan, Sara Svensson Akusjärvi, Soham Gupta, Magda Lourda, Maike Sperk, Muhammad Arif, Cheng Zhang, Hampus Nordqvist, Sivasankaran Munusamy Ponnan, Anders Sönnerborg, Carl Johan Treutiger, Liam O’Mahony, Adil Mardinoğlu, Rui Benfeitas, Ujjwal Neogi
Abstract
The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.