Improving the Understanding of the Immunopathogenesis of Lymphopenia as a Correlate of SARS-CoV-2 Infection Risk and Disease Progression in African Patients: Protocol for a Cross-sectional Study

The Differentially Expressed Genes Responsible for the Development of T Helper 9 Cells From T Helper 2 Cells in Various Disease States: Immuno-Interactomics Study

BACKGROUND

T helper (Th) 9 cells are a novel subset of Th cells that develop independently from Th2 cells and are characterized by the secretion of interleukin (IL)-9. Studies have suggested the involvement of Th9 cells in variable diseases such as allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, and pulmonary hypoplasia), metabolic diseases (eg, acute leukemia, myelocytic leukemia, breast cancer, lung cancer, melanoma, pancreatic cancer), neuropsychiatric disorders (eg, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus, systemic scleroderma, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, atopic dermatitis, eczema), and infectious diseases (eg, tuberculosis, hepatitis). However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases.

OBJECTIVE

This study aims to identify significant differentially altered genes in the conversion of Th2 to Th9 cells, and their regulating microRNAs (miRs) from publicly available Gene Expression Omnibus data sets of the mouse model using in silico analysis to unravel various pathogenic pathways involved in disease processes.

METHODS

Using differentially expressed genes (DEGs) identified from 2 publicly available data sets (GSE99166 and GSE123501) we performed functional enrichment and network analyses to identify pathways, protein-protein interactions, miR-messenger RNA associations, and disease-gene associations related to significant differentially altered genes implicated in the conversion of Th2 to Th9 cells.

RESULTS

We extracted 260 common downregulated, 236 common upregulated, and 634 common DEGs from the expression profiles of data sets GSE99166 and GSE123501. Codifferentially expressed ILs, cytokines, receptors, and transcription factors (TFs) were enriched in 7 crucial Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology. We constructed the protein-protein interaction network and predicted the top regulatory miRs involved in the Th2 to Th9 differentiation pathways. We also identified various metabolic, allergic and pulmonary, neuropsychiatric, autoimmune, and infectious diseases as well as carcinomas where the differentiation of Th2 to Th9 may play a crucial role.

CONCLUSIONS

This study identified hitherto unexplored possible associations between Th9 and disease states. Some important ILs, including CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9; receptors, including IL-12RB1, IL-4RA (interleukin 9 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte-associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196); and TFs, including FOXP3 (forkhead box P3), IRF8 (interferon regulatory factor 8), FOXP2 (forkhead box P2), RORA (RAR-related orphan receptor alpha), AHR (aryl-hydrocarbon receptor), MAF (avian musculoaponeurotic fibrosarcoma oncogene homolog), SMAD6 (SMAD family member 6), JUN (Jun proto-oncogene), JAK2 (Janus kinase 2), EP300 (E1A binding protein p300), ATF6 (activating transcription factor 6), BTAF1 (B-TFIID TATA-box binding protein associated factor 1), BAFT (basic leucine zipper transcription factor), NOTCH1 (neurogenic locus notch homolog protein 1), GATA3 (GATA binding protein 3), SATB1 (special AT-rich sequence binding protein 1), BMP7 (bone morphogenetic protein 7), and PPARG (peroxisome proliferator-activated receptor gamma, were able to identify significant differentially altered genes in the conversion of Th2 to Th9 cells. We identified some common miRs that could target the DEGs. The scarcity of studies on the role of Th9 in metabolic diseases highlights the lacunae in this field. Our study provides the rationale for exploring the role of Th9 in various metabolic disorders such as diabetes mellitus, diabetic nephropathy, hypertensive disease, ischemic stroke, steatohepatitis, liver fibrosis, obesity, adenocarcinoma, glioblastoma and glioma, malignant neoplasm of stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, prostate carcinoma, and stomach carcinoma.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Blood concentrations of proapoptotic sFas and antiapoptotic Bcl2 and COVID-19 patient mortality

Background: There are no data on circulating concentrations of sFas (proapoptotic protein of extrinsic pathway) and Bcl2 (antiapoptotic protein of intrinsic pathway) in COVID-19 patients. Thus, our objective study was to determine whether an association exists between serum concentrations of sFas and Bcl2 and COVID-19 patient mortality.Methods: This observational and prospective study of COVID-19 patients was performed in eight Intensive Care Units (ICU) from Canary Islands (Spain). Serum levels of sFas and Bcl2 at ICU admission were determined. Mortality at 30 days was the end-point study.Results: Surviving patients (n = 42) compared to non-surviving (n = 11) had lower APACHE-II (p < 0.001), lower SOFA (p = 0.004), lower serum sFas levels (p = 0.001) and higher serum Bcl2 levels (p < 0.001). Logistic regression showed an association between high serum sFas levels and mortality after controlling for APACHE-II (OR = 1.004; 95% CI = 1.101-1.007; p = 0.01) or SOFA (OR = 1.003; 95% CI = 1.101-1.106; p = 0.004), and between low serum Bcl2 levels and mortality after controlling for APACHE-II (OR = 0.927; 95% CI = 0.873-0.984; p = 0.01) or SOFA (OR = 0.949; 95% CI = 0.913-0.987; p = 0.01).Conclusions: Thus, to the best of our knowledge, this is the first study reporting blood levels of sFas and Bcl2 in COVID-19 patients and its association with mortality.