Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Feyaerts, Dorien; Hédou, Julien; Gillard, Joshua; Chen, Han; Tsai, Eileen S.; Peterson, Laura S.; Ando, Kazuo; Manohar, Monali; Do, Evan; Dhondalay, Gopal Krishna; Fitzpatrick, Jessica; Artandi, Maja; Chang, Iris; Snow, Theo; Chinthrajah, R. Sharon; Warren, Christopher; Wittman, Richard; Meyerowitz, Justin; Ganio, Edward A.; Stelzer, Ina A.; Han, Xiaoyuan; Verdonk, Franck; Gaudilliere, Dyani; Mukherjee, Nilanjan; Tsai, Amy S.; Rumer, Kristen K.; Jacobsen, Danielle; Bjornson-Hooper, Zachary B; Jiang, Sizun; Saavedra, Sergio Fragoso; Valdés‐Ferrer, Sergio Iván; Kelly, John D.; Furman, David; Aghaeepour, Nima; Angst, Martin S.; Boyd, Scott D.; Pinsky, Benjamin A.; Nolan, Garry P.; Nadeau, Kari; Gaudillière, Brice; McIlwain, David R.

doi:10.1016/j.xcrm.2022.100680

Cited by 29 publications

(28 citation statements)

References 111 publications

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“…Several plasma proteomic datasets [ 9 – 15 , 17 , 19 , 20 ] were accumulated since the pandemic has started. We compared our dataset with five COVID‐19 plasma proteome studies [ 9 , 10 , 11 , 12 , 13 ] to find consistent alterations across different datasets (Figure 2A , Supporting Information File 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…The impact of SARS‐CoV‐2 on human physiology can be monitored as plasma contains disease‐related biomarkers circulating in the blood that are released from different organs [ 16 ]. Plasma‐based proteomics studies compared the proteomic changes after SARS‐CoV‐2 infection between patients and controls from a single measurement [ 9 , 12 – 15 , 18 , 19 ] or with multiple measurements in a time‐dependent manner [ 10 , 11 , 17 , 20 ]. In general, inflammatory response, immune system‐related proteins [ 9 , 17 ], metabolic reconstitution, tissue repair‐related proteins, and regulators of coagulation [ 9 , 21 ] have been shown to be deregulated after infection [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Plasma proteomics identify potential severity biomarkers from COVID‐19 associated network

Sahin

Yurtseven

Dadmand

et al. 2022

Proteomics Clinical Apps

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Purpose Coronavirus disease 2019 (COVID‐19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS‐CoV‐2) infection‐dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS‐CoV‐2 infection. Experimental Design We systematically analyzed plasma proteomes of COVID‐19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID‐19 associated network that was further extended to connect viral and host proteins. Results Across all COVID‐19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID‐19 severity. Most importantly, we extended the COVID‐19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins. Conclusions and Clinical Relevance Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID‐19 prognosis. Beyond the list of plasma proteins, our disease‐associated network unravels altered pathways, and the possible therapeutic targets in SARS‐CoV‐2 infection by connecting human and viral proteins. Follow‐up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plasma proteomics identify potential severity biomarkers from COVID‐19 associated network

Sahin

Yurtseven

Dadmand

et al. 2022

Proteomics Clinical Apps

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“…In this study, we analyzed the plasma protein pro les of Long-COVID outpatients, and compared with ageand sex-matched acutely ill COVID-19 inpatients and healthy control subjects. The plasma proteome was deconvoluted into cell-type pro les and signaling pathways, as well as speci c organ systems based on previously curated biomarkers [20][21][22][23][24][25][26][27][28][29]. Several biomarkers were dramatically elevated in Long-COVID patients and taken together these markers pinpointed immune triggered vascular proliferation which is mediated by hypoxia with a strong effect on brain and heart function [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function

Iosef

Knauer²,

Nicholson

et al. 2023

Preprint

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Aims Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aims to determine the underlying mechanisms, and to inform prognosis and treatment. Methods Plasma proteome from Long-COVID outpatients was analyzed in comparison to acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of approximately 3000 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. Results Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cells with a resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This resetting of cell phenotypes was reflected in vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Levels of ANGPT1 and VEGFA were validated by serological methods in different patient cohorts. Silent signaling of transforming growth factor-β1 with elevated EP300 favored not only vascular inflammation, but also tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway was predicted that progressed from COVID-19 to Long-COVID. The vasculo-proliferative process identified in Long-COVID was associated with significant changes in the organ-specific proteome reflective of neurological and cardiometabolic dysfunction. Conclusions Taken together, our study uncovered a vasculo-proliferative process in Long-COVID initiated by prior hypoxia, and identified potential organ-specific prognostic biomarkers and therapeutic targets.

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“…The timing in which the datasets are combined (early-fusion vs. late-fusion) and the applied regularization and penalization differ between analysis approaches. A commonly used approach is based on stacked generalization in which predictive models are built on each of the individual data layers before incorporating the most predictive features from each omic dataset into one overarching model [ 1 , 3 , 120 ]. Establishing predictive models for each data layer prior to combining them into an integrated model using stacked generalization can increase the predictive power, as this approach can account for feature intercorrelation within data layers and differences in the number of measurements between data layers.…”

Section: Integrated Multiomic Modeling To Identify New Biological Cro...mentioning

confidence: 99%

“…Recently, advances in high-throughput transcriptomic, proteomic, metabolomic, and cytomic technologies have enabled the characterization of the complexity of localized and systemic diseases [ 1 – 5 ]. In this review, we focus on the interplay between oral pathology triggers and the host’s immune response mechanisms in the development, maintenance, progression, and resolution of pathological processes in the oral cavity.…”

Section: Introductionmentioning

confidence: 99%

Towards multiomic analysis of oral mucosal pathologies

et al. 2023

Self Cite

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Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host’s immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system’s role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures.

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Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Cited by 29 publications

References 111 publications

Plasma proteomics identify potential severity biomarkers from COVID‐19 associated network

Plasma proteomics identify potential severity biomarkers from COVID‐19 associated network

Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function

Towards multiomic analysis of oral mucosal pathologies

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