Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis.
Hu MengArjun SenguptaEmanuela RicciottiAntonijo MrčelaDivij MathewLiudmila L MazaleuskayaSoumita GhoshThomas G BrooksAlexandra P TurnerAlessa Soares SchanoskiNicholas F LahensAi Wen TanAshley WoolforkGreg GrantKatalin SusztakAndrew G LetiziaStuart C SealfonE John WherryKrzysztof LaudanskiAalim M WeljieNuala B MeyerGarret A FitzGeraldPublished in: bioRxiv : the preprint server for biology (2023)
Lipids may influence cellular penetrance by pathogens and the immune response that they evoke. Here we find a broad based lipidomic storm driven predominantly by secretory (s) phospholipase A 2 (sPLA 2 ) dependent eicosanoid production occurs in patients with sepsis of viral and bacterial origin and relates to disease severity in COVID-19. Elevations in the cyclooxygenase (COX) products of arachidonic acid (AA), PGD 2 and PGI 2 , and the AA lipoxygenase (LOX) product, 12-HETE, and a reduction in the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients, correlate with the inflammatory response and link to disease severity. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflect disease severity in COVID-19. AA and LA metabolites and LPC-O-16:0 linked variably to the immune response. These studies yield prognostic biomarkers and therapeutic targets for patients with sepsis, including COVID-19. An interactive purpose built interactive network analysis tool was developed, allowing the community to interrogate connections across these multiomic data and generate novel hypotheses.
Keyphrases
- sars cov
- coronavirus disease
- immune response
- respiratory syndrome coronavirus
- inflammatory response
- acute kidney injury
- intensive care unit
- network analysis
- healthcare
- dendritic cells
- chronic kidney disease
- newly diagnosed
- microbial community
- staphylococcus aureus
- high throughput
- prognostic factors
- wastewater treatment
- escherichia coli
- data analysis
- antimicrobial resistance
- antibiotic resistance genes