Th1, Th2 and Th17 inflammatory pathways synergistically predict cardiometabolic protein expression in serum of COVID-19 patients

Michels, James R; Nazrul, Mohammad Shaheed; Adhikari, Sudeep; Wilkins, Dawn; Pavel, Ana B.

doi:10.1039/d2mo00055e

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…LTBP2 expression increases in response to myocardial stressors (e.g., pressure overload [ 96 ] or isoproterenol [ 97 ]) and has been associated with both cardiac [ 96 ] and pulmonary fibrosis [ 39 ]. Consistently, in patients with COVID-19, LTBP2 levels are related to pulmonary fibrosis [ 39 ] and are associated with disease severity [ 98 ]. In adulthood, the KIT receptor and its ligand SCF are mainly expressed by stem cells and mastocytes and regulate their function [ 99 ].…”

Section: Discussionmentioning

confidence: 95%

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: 95%

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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“…Different subtypes of the same type of cells were merged, resulting in a total of 9 types of cells. Th1, th2, and th 17 markers were classi ed based on previous studies across various in ammatory diseases [12][13][14]. Gene set variation analysis (GSVA) was used to compute enrichment scores for Th1, Th2, and Th17 immune pathways [15].…”

Section: Function Enrichment Analysesmentioning

confidence: 99%

Identification of Distinct Immune Signatures and Chemokine Networks in Scalp Inflammatory Diseases

Liu

et al. 2023

Preprint

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Background Alopecia areata (AA), cutaneous lupus erythematosus (CLE), and psoriasis are diseases that often affect the scalp. AA and CLE often lead to hair loss, whereas psoriasis does not. The underlying mechanisms contributing to these differential prognoses remain unclear. Methods Microarray datasets of the three scalp diseases were collected from the GEO database and were integrated by sva R package. Differentially expressed genes (DEGs) were identified by the limma R package. Generally Applicable Gene-set Enrichment (GAGE), CIBERSORT algorithm, and Gene set variation analysis (GSVA) was utilized to access the functional, immune infiltration, and T helper 1/2/17 Chemokine signature changes in diseases with or without hair loss. qRT-PCR, immunofluorescence, and immunohistochemical staining were used to detect gene expression alteration among diseases from patients and mouse models. Results We identified shared gene expression changes associated with T cell chemotaxis and interferon-β response in scalp autoimmune diseases. In addition to the expected reduction in intermediate and keratin filaments, four functional changes associated with alopecia were found, including intestinal immune network for IgA, cell adhesion molecules, natural killer cell-mediated cytotoxicity, and complement and coagulation cascades. Immune infiltration analysis revealed increased infiltration of CD8 + T cells, NK cells, and mast cells in AA and CLE, while CD4 + cells were the predominant infiltrating immune cells in scalp psoriasis. Furthermore, scalp psoriasis exhibited a distinct Th17/Th1 profile, elevated CCL4 levels, and more CCR5 + Foxp3 + cells infiltration around the hair follicle. Conclusion Our study identified shared pathways and immune cells involved in hair loss and revealed a prominent perifollicular infiltration model of CD4 + T cells and an increased CCL4-CCR5 axis in scalp psoriasis, which may contribute to hair preservation in psoriasis patients. These findings provided valuable insights for developing therapeutic strategies for inflammatory alopecia.

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“…Unexpected symptoms described as being caused by a hyperimmune response referred to as the "cytokine storm" have been observed in various organs [11][12][13][14]. Severe respiratory symptoms and inflammation of various organs in the circulatory, digestive, nervous, and endocrine systems are affected [15][16][17][18]. Notably, more than 40% of patients experience neurological symptoms (e.g., chemosensory impairment or brain fog) that have not been reported in previous coronavirus infections [19,20].…”

Section: Introductionmentioning

confidence: 99%

Understanding the cellular pathogenesis of COVID-19 symptoms using organoid technology

Hyun

Kim

2022

Organoid

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Patients with coronavirus disease 2019 (COVID-19), which has recently caused a pandemic, have reported symptoms of coronavirus infection that are not well understood by the medical community in general. After severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, several symptoms, including acute clinical signs and possible sequelae, manifest in multiple organs. It is necessary to precisely identify the cells susceptible to SARS-CoV-2 infection in order to comprehend the mechanism of symptom occurrence, identify molecular targets for therapeutic development, and prevent current or future threats. Following the use of cell lines, animal models, and stem cell-derived symptom-relevant cells, recent research on the pathophysiology of human diseases has utilized organoid models. This article provides a summary of recent research on the tissue- or organ-specific cellular targets of SARS-CoV-2 aiming to understand the pathophysiology of COVID-19.

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Th1, Th2 and Th17 inflammatory pathways synergistically predict cardiometabolic protein expression in serum of COVID-19 patients

Abstract: A predominant source of complication in SARS-CoV-2 patients arises from a severe systemic inflammation that can lead to tissue damage and organ failure. The high inflammatory burden of this viral...

Cited by 4 publications

References 43 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

Identification of Distinct Immune Signatures and Chemokine Networks in Scalp Inflammatory Diseases

Understanding the cellular pathogenesis of COVID-19 symptoms using organoid technology

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