Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity
Show others and affiliations
Number of Authors: 172022 (English)In: Cell systems, ISSN 2405-4712, Vol. 13, no 8, p. 665-681Article in journal (Refereed) Published
Abstract [en]

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 metabolo-mics 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 sub-stantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and person-alized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as a-ketoglutarate, succi-nate, 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.

Place, publisher, year, edition, pages
2022. Vol. 13, no 8, p. 665-681
Keywords [en]
COVID-19, similarity network fusion, personalized genome-scale metabolic model
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-209347DOI: 10.1016/j.cels.2022.06.006ISI: 000844051100002PubMedID: 35933992Scopus ID: 2-s2.0-85135506746OAI: oai:DiVA.org:su-209347DiVA, id: diva2:1696272
Available from: 2022-09-16 Created: 2022-09-16 Last updated: 2022-09-16Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Benfeitas, Rui

Search in DiVA

By author/editor
Benfeitas, Rui
By organisation
Department of Biochemistry and BiophysicsScience for Life Laboratory (SciLifeLab)
Biological Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf