SARS-CoV-2 infection paralyzes cytotoxic and metabolic functions of the immune cells

Singh, Yogesh; Trautwein, Christoph; Fendel, Rolf; Krickeberg, Naomi; Berezhnoy, Georgy; Bissinger, Rosi; Ossowski, Stephan; Salker, Madhuri S.; Casadei, Nicolas; Rieß, Olaf

doi:10.1016/j.heliyon.2021.e07147

Cited by 15 publications

(11 citation statements)

References 78 publications

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“…The profound alterations of the hematopoietic stem cell maturation are observed in COVID-19 patients (i.e., the expansion of the “granulocyte-monocyte progenitor” and the “erythroid progenitor” cell pools, at the expense of the lymphoid cell pool) [ 80 ]. In line with this, the emergence of monocytes expressing low levels of HLA-DR and anti-inflammatory molecules, together with neutrophils expressing the immune checkpoint protein PD-L1 [ 81 ] coupled with the exhaustion and anergy of T cells, are all hallmarks of severe COVID-19 [ 82 ]. Consistently, the relative Lymphocyte count was identified to be associated with disease severity by all the feature correlation algorithms we employed.…”

Section: Discussionmentioning

confidence: 99%

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

Beltrami

Martino

Dalla

et al. 2022

IJMS

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the persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a “non-severe” and 80 had a “severe” outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a “severe” outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

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Section: Discussionmentioning

confidence: 99%

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

Beltrami

Martino

Dalla

et al. 2022

IJMS

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“…Contradictory data have been published on T8 cell perforin expression and cytotoxicity in COVID-19 patients. Some articles report low levels of perforin-positive T cells ( 30 , 31 ) and T cell cytotoxicity ( 32 ), whereas others describe high levels of perforin-positive T cells ( 33 , 34 ) and T cell cytotoxicity ( 35 ). In the present study, we found a high prevalence of perforin-positive T4 cells and T8 cells in patients hospitalized with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Singh et al. reported a low proportion of T8 cells expressing granzyme A and coexpressing granzyme A and perforin, but they recruited patients with mild and moderate COVID-19 ( 31 ). Results may also depend on the T8 subpopulation analyzed.…”

Section: Discussionmentioning

confidence: 99%

Low perforin expression in CD8+ T lymphocytes during the acute phase of severe SARS-CoV-2 infection predicts long COVID

Kundura

Cezar²,

André³

et al. 2022

Front. Immunol.

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T cell cytotoxicity plays a major role in antiviral immunity. Anti-SARS-CoV-2 immunity may determine acute disease severity, but also the potential persistence of symptoms (long COVID). We therefore measured the expression of perforin, a cytotoxic mediator, in T cells of patients recently hospitalized for SARS-CoV-2 infection. We recruited 54 volunteers confirmed as being SARS-CoV-2-infected by RT-PCR and admitted to Intensive Care Units (ICUs) or non-ICU, and 29 age- and sex-matched healthy controls (HCs). Amounts of intracellular perforin and granzyme-B, as well as cell surface expression of the degranulation marker CD107A were determined by flow cytometry. The levels of 15 cytokines in plasma were measured by Luminex. The frequency of perforin-positive T4 cells and T8 cells was higher in patients than in HCs (9.9 ± 10.1% versus 4.6 ± 6.4%, p = 0.006 and 46.7 ± 20.6% vs 33.3 ± 18.8%, p = 0.004, respectively). Perforin expression was neither correlated with clinical and biological markers of disease severity nor predictive of death. By contrast, the percentage of perforin-positive T8 cells in the acute phase of the disease predicted the onset of long COVID one year later. A low T8 cytotoxicity in the first days of SARS-CoV-2 infection might favor virus replication and persistence, autoimmunity, and/or reactivation of other viruses such as Epstein-Barr virus or cytomegalovirus, paving the way for long COVID. Under this hypothesis, boosting T cell cytotoxicity during the acute phase of the infection could prevent delayed sequelae.

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“…The SLC2A5 gene was reported as one of many genes differentially expressed related to a severe neurological progression in COVID-19 patients [ 59 ]. It also demonstrated high levels of fructose in PBMC [ 60 ] and serum metabolome of SARS-CoV-2 patients [ 61 ]. The high levels of this metabolite can also induce glycolysis in SARS-CoV-2-infected human monocytes [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease

et al. 2022

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus’ pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease. Supplementary information The online version contains supplementary material available at 10.1007/s42770-022-00875-2.

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SARS-CoV-2 infection paralyzes cytotoxic and metabolic functions of the immune cells

Cited by 15 publications

References 78 publications

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

Low perforin expression in CD8+ T lymphocytes during the acute phase of severe SARS-CoV-2 infection predicts long COVID

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease

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