A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19

Lam, Sin Man; Zhang, Chao; Wang, Zehua; Zhen, Ni; Zhang, Shaohua; Yang, Siyuan; Huang, Xiahe; Mo, Lesong; Li, Jie; Lee, Bernett; Mei, Mei; Huang, Lei; Shi, Ming; Xu, Zhe; Meng, Fanping; Cao, Wen-Jing; Zhou, Ming‐Ju; Shi, Lei; Chua, Gek Huey; Li, Bowen; Cao, Jiabao; Wang, Jun; Bao, Shilai; Wang, Yingchun; Song, Jin‐Wen; Zhang, Fujie; Wang, Fusheng; Shui, Guanghou

doi:10.1038/s42255-021-00425-4

Cited by 173 publications

(155 citation statements)

References 79 publications

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“…There are several reports documenting lipid profiles of plasma in COVID-19 patients emphasizing marked changes in phospholipids, neutral lipids (particularly, triacylglycerols, cholesterol esters) and non-esterified fatty acids related to the severity and transition from the pro-inflammatory to the resolution phases of the disease, as well as in cured patients [77] , [78] , [79] , [80] . In few cases, lipidomics studies were conducted in COVID-19 BAL samples, thus reflecting lipidomics changes in the lungs of intubated patients.…”

Section: Discussionmentioning

confidence: 99%

Syrian hamsters as a model of lung injury with SARS-CoV-2 infection: Pathologic, physiologic, and detailed molecular profiling

Bednash¹,

Kagan²,

Englert³

et al. 2022

Translational Research

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Section: Discussionmentioning

confidence: 99%

Syrian hamsters as a model of lung injury with SARS-CoV-2 infection: Pathologic, physiologic, and detailed molecular profiling

Bednash¹,

Kagan²,

Englert³

et al. 2022

Translational Research

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“…As such, it is not surprising that lipids are involved in the interplay between viral infection and the host’s response [2]. Viruses enter cells through protein-lipid interactions [4,5], are externalized via lipid vesicles [6], while interactions between viruses and the organism alter mitochondrial metabolism and the microbiota [7-9] that have important implications in body lipid composition. Further, oxidative stress triggered by infection results in oxidized lipids production by specific biosynthetic pathway activation [10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning identified distinct serum lipidomic signatures in hospitalized COVID-19-positive and COVID-19-negative patients

Castañé

Iftimie

Baiges-Gayà

et al. 2021

Preprint

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BackgroundLipids are involved in the interaction between viral infection and the host metabolic and immunological response. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, and with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. Potential COVID-19 biomarkers were identified.MethodsWe analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. Results were interpreted by machine learning.ResultsWe identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients. By contrast, we identified lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids as the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients.ConclusionThis study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, but differentiating them from the healthy population. Also, we identified some lipid species the alterations of which distinguish COVID-19-positive from Covid-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.

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“…[51]. Compared to the hyperinflammatory phase, EVs from the resolution phase induce opposing effects on eukaryotic translation and Notch signaling [52]. However, it is unclear whether these occur in recovered CCP donors and their infectious potential has not been established [53].…”

mentioning

confidence: 99%

COVID-19 Convalescent Plasma Is More than Neutralizing Antibodies: A Narrative Review of Potential Beneficial and Detrimental Co-Factors

Focosi¹,

Franchini

Pirofski

et al. 2021

Viruses

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COVID-19 convalescent plasma (CCP) is currently under investigation for both treatment and post-exposure prophylaxis. The active component of CCP mediating improved outcome is commonly reported as specific antibodies, particularly neutralizing antibodies, with clinical efficacy characterized according to the level or antibody affinity. In this review, we highlight the potential role of additional factors in CCP that can be either beneficial (e.g., AT-III, alpha-1 AT, ACE2+ extracellular vesicles) or detrimental (e.g., anti-ADAMTS13, anti-MDA5 or anti-interferon autoantibodies, pro-coagulant extracellular vesicles). Variations in these factors in CCP may contribute to varied outcomes in patients with COVID-19 and undergoing CCP therapy. We advise careful, retrospective investigation of such co-factors in randomized clinical trials that use fresh frozen plasma in control arms. Nevertheless, it might be difficult to establish a causal link between these components and outcome, given that CCP is generally safe and neutralizing antibody effects may predominate.

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A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19

Cited by 173 publications

References 79 publications

Syrian hamsters as a model of lung injury with SARS-CoV-2 infection: Pathologic, physiologic, and detailed molecular profiling

Syrian hamsters as a model of lung injury with SARS-CoV-2 infection: Pathologic, physiologic, and detailed molecular profiling

Machine learning identified distinct serum lipidomic signatures in hospitalized COVID-19-positive and COVID-19-negative patients

COVID-19 Convalescent Plasma Is More than Neutralizing Antibodies: A Narrative Review of Potential Beneficial and Detrimental Co-Factors

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