Persistently high SARS-CoV-2 positivity rate and incidence for Hispanic/Latinos during state reopening in an urban setting: a retrospective cohort study.

Hispanic/Latino populations are disproportionately impacted by coronavirus disease 2019 (COVID-19) in the United States. The impact of state reopening on COVID-19 in this population after stay-at-home orders is unknown. We evaluated the incidence, prevalence and trends during reopening of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) at a major federally qualified health centre in Providence, Rhode Island. A total of 14 505 patients were tested for SARS-CoV-2 from 19 March to 18 August 2020, of which, data on 13 318 (91.8%) patients were available; 70.0% were Hispanic/Latino, and 2905 were positive for SARS-CoV-2 infection. The urban Hispanic/Latino population was almost five times more likely to test positive for SARS-CoV-2 (risk ratio 4.97, 95% CI 2.59-9.53, P < 0.001) compared to non-Hispanic White. The positivity rates among the urban Hispanic/Latino population remained >10% during all phases of reopening. The trends of the incidence rates showed similar associations to those we observed for positivity rates. Public health interventions to address SARS-CoV-2 in Hispanic/Latino communities are urgently needed, even in latter phases of state reopening.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

An appraisal of lung computer tomography in very early anti-inflammatory treatment of two different ovine ARDS phenotypes.

Mortality and morbidity of Acute Respiratory Distress Syndrome (ARDS) are largely unaltered. A possible new approach to treatment of ARDS is offered by the discovery of inflammatory subphenotypes. In an ovine model of ARDS phenotypes, matching key features of the human subphenotypes, we provide an imaging characterization using computer tomography (CT). Nine animals were randomized into (a) OA (oleic acid, hypoinflammatory; n = 5) and (b) OA-LPS (oleic acid and lipopolysaccharides, hyperinflammatory; n = 4). 48 h after ARDS induction and anti-inflammatory treatment, CT scans were performed at high (H) and then low (L) airway pressure. After CT, the animals were euthanized and lung tissue was collected. OA-LPS showed a higher air fraction and OA a higher tissue fraction, resulting in more normally aerated lungs in OA-LPS in contrast to more non-aerated lung in OA. The change in lung and air volume between H and L was more accentuated in OA-LPS, indicating a higher recruitment potential. Strain was higher in OA, indicating a higher level of lung damage, while the amount of lung edema and histological lung injury were largely comparable. Anti-inflammatory treatment might be beneficial in terms of overall ventilated lung portion and recruitment potential, especially in the OA-LPS group.

BNT162b2 and mRNA-1273 Vaccine Effectiveness against SARS-CoV-2 and Variants in the Urban Underserved Population.

The real world COVID-19 vaccine effectiveness among the urban underserved Hispanic/Latino populations is unknown. We evaluated the mRNA vaccine effectiveness in preventing SARS-CoV-2 infections at a major federally qualified health center in Providence, Rhode Island, and a total of 38,602 patients were included. Time period was used as the SARS-CoV-2 variant proxy. Compared to the unvaccinated group, the adjusted vaccine effectiveness for 2 doses of BNT162b2 and mRNA-1273 were 94.6% and 97.5% respectively against the alpha variant/wild type, which dropped to 64.8% and 65.0% respectively against the delta variant and 31.6% and 25.6% respectively against the omicron variant. However, once received the booster dose (3rd dose) of BNT162b2 and mRNA-1273, the vaccine effectiveness against the omicron variant improved to 79.9% and 71.2% respectively. Improving the COVID-19 vaccine education and encouraging to receive a booster dose may help further reduce the burden of SARS-CoV-2 infection in this population.

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Effect of flow change on brain injury during an experimental model of differential hypoxaemia in cardiogenic shock supported by extracorporeal membrane oxygenation.

Differential hypoxaemia (DH) is common in patients supported by femoral veno-arterial extracorporeal membrane oxygenation (V-A ECMO) and can cause cerebral hypoxaemia. To date, no models have studied the direct impact of flow on cerebral damage. We investigated the impact of V-A ECMO flow on brain injury in an ovine model of DH. After inducing severe cardiorespiratory failure and providing ECMO support, we randomised six sheep into two groups: low flow (LF) in which ECMO was set at 2.5 L min-1 ensuring that the brain was entirely perfused by the native heart and lungs, and high flow (HF) in which ECMO was set at 4.5 L min-1 ensuring that the brain was at least partially perfused by ECMO. We used invasive (oxygenation tension-PbTO2, and cerebral microdialysis) and non-invasive (near infrared spectroscopy-NIRS) neuromonitoring, and euthanised animals after five hours for histological analysis. Cerebral oxygenation was significantly improved in the HF group as shown by higher PbTO2 levels (+ 215% vs - 58%, p = 0.043) and NIRS (67 ± 5% vs 49 ± 4%, p = 0.003). The HF group showed significantly less severe brain injury than the LF group in terms of neuronal shrinkage, congestion and perivascular oedema (p < 0.0001). Cerebral microdialysis values in the LF group all reached the pathological thresholds, even though no statistical difference was found between the two groups. Differential hypoxaemia can lead to cerebral damage after only a few hours and mandates a thorough neuromonitoring of patients. An increase in ECMO flow was an effective strategy to reduce such damages.

Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.

Model-based assessment of replicability for genome-wide association meta-analysis.

Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the "posterior-probability-of-replicability" for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.

An innovative ovine model of severe cardiopulmonary failure supported by veno-arterial extracorporeal membrane oxygenation.

Refractory cardiogenic shock (CS) often requires veno-arterial extracorporeal membrane oxygenation (VA-ECMO) to sustain end-organ perfusion. Current animal models result in heterogenous cardiac injury and frequent episodes of refractory ventricular fibrillation. Thus, we aimed to develop an innovative, clinically relevant, and titratable model of severe cardiopulmonary failure. Six sheep (60 ± 6 kg) were anaesthetized and mechanically ventilated. VA-ECMO was commenced and CS was induced through intramyocardial injections of ethanol. Then, hypoxemic/hypercapnic pulmonary failure was achieved, through substantial decrease in ventilatory support. Echocardiography was used to compute left ventricular fractional area change (LVFAC) and cardiac Troponin I (cTnI) was quantified. After 5 h, the animals were euthanised and the heart was retrieved for histological evaluations. Ethanol (58 ± 23 mL) successfully induced CS in all animals. cTnI levels increased near 5000-fold. CS was confirmed by a drop in systolic blood pressure to 67 ± 14 mmHg, while lactate increased to 4.7 ± 0.9 mmol/L and LVFAC decreased to 16 ± 7%. Myocardial samples corroborated extensive cellular necrosis and inflammatory infiltrates. In conclusion, we present an innovative ovine model of severe cardiopulmonary failure in animals on VA-ECMO. This model could be essential to further characterize CS and develop future treatments.

Vaccination With Sporozoites: Models and Correlates of Protection.

Despite continuous efforts, the century-old goal of eradicating malaria still remains. Multiple control interventions need to be in place simultaneously to achieve this goal. In addition to effective control measures, drug therapies and insecticides, vaccines are critical to reduce mortality and morbidity. Hence, there are numerous studies investigating various malaria vaccine candidates. Most of the malaria vaccine candidates are subunit vaccines. However, they have shown limited efficacy in Phase II and III studies. To date, only whole parasite formulations have been shown to induce sterile immunity in human. In this article, we review and discuss the recent developments in vaccination with sporozoites and the mechanisms of protection involved.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.