tesG expression as a potential clinical biomarker for chronic Pseudomonas aeruginosa pulmonary biofilm infections

Background Pseudomonas aeruginosa infections in the lungs affect millions of children and adults worldwide. To our knowledge, no clinically validated prognostic biomarkers for chronic pulmonary P. aeruginosa infections exist. Therefore, this study aims to identify potential prognostic markers for chronic P. aeruginosa biofilm lung infections. Methods Here, we screened the expression of 11 P. aeruginosa regulatory genes (tesG, algD, lasR, lasA, lasB, pelB, phzF, rhlA, rsmY, rsmZ, and sagS) to identify associations between clinical status and chronic biofilm infection. Results RNA was extracted from 210 sputum samples from patients (n = 70) with chronic P. aeruginosa lung infections (mean age; 29.3–56.2 years; 33 female). Strong biofilm formation was correlated with prolonged hospital stays (212.2 days vs. 44.4 days) and increased mortality (46.2% (18)). Strong biofilm formation is associated with increased tesG expression (P = 0.001), influencing extended intensive care unit (P = 0.002) or hospitalisation stays (P = 0.001), pneumonia risk (P = 0.006), and mortality (P = 0.001). Notably, tesG expression is linked to the modulation of systemic and sputum inflammatory responses and predicts biofilm biomass. Conclusions This study provides the first clinical dataset of tesG expression levels as a predictive biomarker for chronic P. aeruginosa pulmonary infections

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

tesG expression as a potential clinical biomarker for chronic Pseudomonas aeruginosa pulmonary biofilm infections

Background: Pseudomonas aeruginosa infections in the lungs affect millions of children and adults worldwide. To our knowledge, no clinically validated prognostic biomarkers for chronic pulmonary P. aeruginosa infections exist. Therefore, this study aims to identify potential prognostic markers for chronic P. aeruginosa biofilm lung infections. Methods: Here, we screened the expression of 11 P. aeruginosa regulatory genes (tesG, algD, lasR, lasA, lasB, pelB, phzF, rhlA, rsmY, rsmZ, and sagS) to identify associations between clinical status and chronic biofilm infection. Results: RNA was extracted from 210 sputum samples from patients (n = 70) with chronic P. aeruginosa lung infections (mean age; 29.3–56.2 years; 33 female). Strong biofilm formation was correlated with prolonged hospital stays (212.2 days vs. 44.4 days) and increased mortality (46.2% (18)). Strong biofilm formation is associated with increased tesG expression (P = 0.001), influencing extended intensive care unit (P = 0.002) or hospitalisation stays (P = 0.001), pneumonia risk (P = 0.006), and mortality (P = 0.001). Notably, tesG expression is linked to the modulation of systemic and sputum inflammatory responses and predicts biofilm biomass. Conclusions: This study provides the first clinical dataset of tesG expression levels as a predictive biomarker for chronic P. aeruginosa pulmonary infections.Full Tex

Immunogenicity and Risk Factors Associated with Poor Humoral Immune Response of SARS-CoV-2 Vaccines in Recipients of Solid Organ Transplant: A Systematic Review and Meta-Analysis

Importance: Recipients of solid organ transplant (SOT) experience decreased immunogenicity after COVID-19 vaccination. Objective: To summarize current evidence on vaccine responses and identify risk factors for diminished humoral immune response in recipients of SOT. Data Sources: A literature search was conducted from existence of database through December 15, 2021, using MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov. Study Selection: Studies reporting humoral immune response of the COVID-19 vaccines in recipients of SOT were reviewed. Data Extraction and Synthesis: Two reviewers independently extracted data from each eligible study. Descriptive statistics and a random-effects model were used. This report was prepared following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Data were analyzed from December 2021 to February 2022. Main Outcomes and Measures: The total numbers of positive immune responses and percentage across each vaccine platform were recorded. Pooled odds ratios (pORs) with 95% CIs were used to calculate the pooled effect estimates of risk factors for poor antibody response. Results: A total of 83 studies were included for the systematic review, and 29 studies were included in the meta-analysis, representing 11713 recipients of SOT. The weighted mean (range) of total positive humoral response for antispike antibodies after receipt of mRNA COVID-19 vaccine was 10.4% (0%-37.9%) for 1 dose, 44.9% (0%-79.1%) for 2 doses, and 63.1% (49.1%-69.1%) for 3 doses. In 2 studies, 50% of recipients of SOT with no or minimal antibody response after 3 doses of mRNA COVID-19 vaccine mounted an antibody response after a fourth dose. Among the factors associated with poor antibody response were older age (mean [SE] age difference between responders and nonresponders, 3.94 [1.1] years), deceased donor status (pOR, 0.66 [95% CI, 0.53-0.83]; I2= 0%), antimetabolite use (pOR, 0.21 [95% CI, 0.14-0.29]; I2= 70%), recent rituximab exposure (pOR, 0.21 [95% CI, 0.07-0.61]; I2= 0%), and recent antithymocyte globulin exposure (pOR, 0.32 [95% CI, 0.15-0.71]; I2= 0%). Conclusions and Relevance: In this systematic review and meta-analysis, the rates of positive antibody response in solid organ transplant recipients remained low despite multiple doses of mRNA vaccines. These findings suggest that more efforts are needed to modulate the risk factors associated with reduced humoral responses and to study monoclonal antibody prophylaxis among recipients of SOT who are at high risk of diminished humoral response.. © 2022 American Medical Association. All rights reserved.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]