Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity.

BACKGROUND: The use of molecular biomarkers for COVID-19 remains unconclusive. The application of a molecular biomarker in combination with clinical ones that could help classifying aggressive patients in first steps of the disease could help clinician and sanitary system a better management of the disease. Here we characterize the role of ACE2, AR, MX1, ERG, ETV5 and TMPRSS2 for trying a better classification of COVID-19 through knowledge of the disease mechanisms. METHODS: A total of 329 blood samples were genotyped in ACE2, MX1 and TMPRSS2. RNA analyses were also performed from 258 available samples using quantitative polymerase chain reaction for genes: ERG, ETV5, AR, MX1, ACE2, and TMPRSS2. Moreover, in silico analysis variant effect predictor, ClinVar, IPA, DAVID, GTEx, STRING and miRDB database was also performed. Clinical and demographic data were recruited from all participants following WHO classification criteria. RESULTS: We confirm the use of ferritin (p < 0.001), D-dimer (p < 0.010), CRP (p < 0.001) and LDH (p < 0.001) as markers for distinguishing mild and severe cohorts. Expression studies showed that MX1 and AR are significantly higher expressed in mild vs severe patients (p < 0.05). ACE2 and TMPRSS2 are involved in the same molecular process of membrane fusion (p = 4.4 × 10-3), acting as proteases (p = 0.047). CONCLUSIONS: In addition to the key role of TMPSRSS2, we reported for the first time that higher expression levels of AR are related with a decreased risk of severe COVID-19 disease in females. Moreover, functional analysis demonstrates that ACE2, MX1 and TMPRSS2 are relevant markers in this disease.

Genetic variants of antioxidant and xenobiotic metabolizing enzymes and their association with prostate cancer: A meta-analysis and functional in silico analysis.

The development and progression of prostate cancer (PCa) depends on complex interactions between genetic, environmental and dietary factors that modulate the carcinogenesis process. Interactions between chemical exposures and genetic polymorphisms in genes encoding xenobiotic metabolizing enzymes (XME), antioxidant enzymes and DNA repair enzymes have been reported as the main drivers of cancer. Thus, a better understanding of the causal risk factors for PCa will provide avenues to identify men at increased risk and will contribute to develop effective detection and prevention methods. We performed a meta-analysis on 17,518 cases and 42,507 controls obtained from 42 studies to determine whether seven SNPs and one CNV pertaining to oxidative stress, xenobiotic detoxification and DNA repair enzymes are associated with the risk of PCa (GPX1 (rs1050450), XRCC1 (rs25487), PON1 (rs662), SOD2 (rs4880), CAT (rs1001179), GSTP1 (rs1695) and CNV GSTM1). A significant increased risk of PCa was found for SOD2 (rs4880) ORGG+GA vs. AA 1.08; 95%CI 1.01-1.15, CAT (rs1001179) ORTT vs. TC+CC 1.39; 95%CI 1.17-1.66, PON1 (rs662) ORCT vs. CC+TT 1.17; 95%CI 1.01-1.35, GSTP1 (rs1695) ORGG vs. GA+AA 1.20; 95%CI 1.05-1.38 and GSTM1 (dual null vs. functional genotype) ORN vs. NN1+NN2 1.34; 95%CI 1.10-1.64. The meta-analysis showed that the CNV GSTM1, and the SNPs GSTP1 (rs1695) and CAT (rs1001179) are strongly associated with a greater risk of PCa and, to a lesser extent, the genetic variants SOD2 (rs4880) and PON1 (rs662). Although several antioxidant enzymes and XME play an important role in the PCa development, other risk factors such as chemical exposures should also be considered to gain insight on PCa risk. The functional in silico analysis showed that the genetic variants studied had no clinical implication regarding malignancy, except for GPX1 (rs1050450) SNP.

Relevance of TMPRSS2, CD163/CD206, and CD33 in clinical severity stratification of COVID-19.

Background: Approximately 13.8% and 6.1% of coronavirus disease 2019 (COVID-19) patients require hospitalization and sometimes intensive care unit (ICU) admission, respectively. There is no biomarker to predict which of these patients will develop an aggressive stage that we could improve their quality of life and healthcare management. Our main goal is to include new markers for the classification of COVID-19 patients. Methods: Two tubes of peripheral blood were collected from a total of 66 (n = 34 mild and n = 32 severe) samples (mean age 52 years). Cytometry analysis was performed using a 15-parameter panel included in the Maxpar® Human Monocyte/Macrophage Phenotyping Panel Kit. Cytometry by time-of-flight mass spectrometry (CyTOF) panel was performed in combination with genetic analysis using TaqMan® probes for ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788) variants. GemStone™ and OMIQ software were used for cytometry analysis. Results: The frequency of CD163+/CD206- population of transitional monocytes (T-Mo) was decreased in the mild group compared to that of the severe one, while T-Mo CD163-/CD206- were increased in the mild group compared to that of the severe one. In addition, we also found differences in CD11b expression in CD14dim monocytes in the severe group, with decreased levels in the female group (p = 0.0412). When comparing mild and severe disease, we also found that CD45- [p = 0.014; odds ratio (OR) = 0.286, 95% CI 0.104-0.787] and CD14dim/CD33+ (p = 0.014; OR = 0.286, 95% CI 0.104-0.787) monocytes were the best options as biomarkers to discriminate between these patient groups. CD33 was also indicated as a good biomarker for patient stratification by the analysis of GemStone™ software. Among genetic markers, we found that G carriers of TMPRSS2 (rs2070788) have an increased risk (p = 0.02; OR = 3.37, 95% CI 1.18-9.60) of severe COVID-19 compared to those with A/A genotype. This strength is further increased when combined with CD45-, T-Mo CD163+/CD206-, and C14dim/CD33+. Conclusions: Here, we report the interesting role of TMPRSS2, CD45-, CD163/CD206, and CD33 in COVID-19 aggressiveness. This strength is reinforced for aggressiveness biomarkers when TMPRSS2 and CD45-, TMPRSS2 and CD163/CD206, and TMPRSS2 and CD14dim/CD33+ are combined.

Type 2 Diabetes-Related Variants Influence the Risk of Developing Prostate Cancer: A Population-Based Case-Control Study and Meta-Analysis.

In this study, we have evaluated whether 57 genome-wide association studies (GWAS)-identified common variants for type 2 diabetes (T2D) influence the risk of developing prostate cancer (PCa) in a population of 304 Caucasian PCa patients and 686 controls. The association of selected single nucleotide polymorphisms (SNPs) with the risk of PCa was validated through meta-analysis of our data with those from the UKBiobank and FinnGen cohorts, but also previously published genetic studies. We also evaluated whether T2D SNPs associated with PCa risk could influence host immune responses by analysing their correlation with absolute numbers of 91 blood-derived cell populations and circulating levels of 103 immunological proteins and 7 steroid hormones. We also investigated the correlation of the most interesting SNPs with cytokine levels after in vitro stimulation of whole blood, peripheral mononuclear cells (PBMCs), and monocyte-derived macrophages with LPS, PHA, Pam3Cys, and Staphylococcus Aureus. The meta-analysis of our data with those from six large cohorts confirmed that each copy of the FTOrs9939609A, HNF1Brs7501939T, HNF1Brs757210T, HNF1Brs4430796G, and JAZF1rs10486567A alleles significantly decreased risk of developing PCa (p = 3.70 × 10-5, p = 9.39 × 10-54, p = 5.04 × 10-54, p = 1.19 × 10-71, and p = 1.66 × 10-18, respectively). Although it was not statistically significant after correction for multiple testing, we also found that the NOTCH2rs10923931T and RBMS1rs7593730 SNPs associated with the risk of developing PCa (p = 8.49 × 10-4 and 0.004). Interestingly, we found that the protective effect attributed to the HFN1B locus could be mediated by the SULT1A1 protein (p = 0.00030), an arylsulfotransferase that catalyzes the sulfate conjugation of many hormones, neurotransmitters, drugs, and xenobiotic compounds. In addition to these results, eQTL analysis revealed that the HNF1Brs7501939, HNF1Brs757210, HNF1Brs4430796, NOTCH2rs10923931, and RBMS1rs7593730 SNPs influence the risk of PCa through the modulation of mRNA levels of their respective genes in whole blood and/or liver. These results confirm that functional TD2-related variants influence the risk of developing PCa, but also highlight the need of additional experiments to validate our functional results in a tumoral tissue context.