GPR15 expressed in T lymphocytes from RA patients is involved in leukocyte chemotaxis to the synovium.

The rheumatoid arthritis (RA) inflammatory process occurs in the joints where immune cells are attracted into the synovium to promote remodeling and tissue damage. GPR15 is a G protein-coupled receptor (GPCR) located on chromosome 3 and has similarity in its sequence with chemokine receptors. Recent evidence indicates that GPR15 may be associated with modulation of the chronic inflammatory response. We evaluated the expression of GPR15 and GPR15L in blood and synovial tissue samples from RA patients, as well as to perform a functional migration assay in response to GPR15L. The expression of GPR15 and c10orf99/gpr15l mRNA was analyzed by RT-qPCR. Samples of synovial fluid and peripheral blood were analyzed for CD45+CD3+CD4+GPR15+ and CD45+CD3+CD8+GPR15+ T cell frequency comparing RA patients versus control subjects by flow cytometry. Migration assays were performed using PBMCs isolated from these individuals in response to the synthetic GPR15 ligand. Statistical analysis included Kruskal-Wallis test, T-test, or Mann-Whitney U test, according to data distribution. A higher expression in the mRNA for GPR15 was identified in early RA subjects. The frequencies of CD4+/CD8+ GPR15+ T lymphocytes are higher in RA patients comparing with healthy subjects. Also, the frequency CD4+/CD8+ GPR15+ T lymphocytes are higher in synovial fluid of established RA patients comparing with OA patients. GPR15 and GPR15L are present in the synovial tissue of RA patients and GPR15L promotes migration of PBMCs from RA patients and healthy subjects. Our results suggest that GPR15/GPR15L have a pathogenic role in RA and their antagonizing could be a therapeutic approach in RA.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach.

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).