yocardial infarction and stroke, the leading causes of global morbidity and mortality, are caused by atherosclerosis, which originates from inflammatory, lipid, endocrine, metabolic and hemodynamic disturbances. Indeed, multiple and parallel malfunctions in metabolic organs are responsible for the complex molecular disease processes of cardiometabolic disorders (CMDs) leading to CAD 1 . For example, the liver plays a central role in determining plasma lipid levels by regulating lipoprotein synthesis and lipoprotein remnant uptake, whereas adipose tissues and skeletal muscle (SKLM) facilitate lipolysis. Similarly, blood glucose levels depend on a delicate interplay of hepatic glucose production, insulin production in pancreatic beta cells and insulin sensitivity in peripheral glycolytic tissues. Alterations in lipid or glucose metabolism may lead to obesity, which in turn may promote the development of type 2 diabetes mellitus, hypertension, systemic inflammation 2,3 and, eventually, CAD.Thus far, the role of these and other risk factors in causing the initiation and progression of CAD have typically only been considered in isolated pathways. A systemic view 4-7 of the combined high-dimensional, multiorgan metabolic processes that perturb the biology of the arterial wall has, however, not been described. Systems studies based on integrative analyses of DNA and RNA sequencing (RNA-seq) data, unlike studies focusing on DNA alone, such as genome-wide association studies (GWAS), hold promise to go beyond studies of individual genetic risk loci and candidate genes in isolated pathways by capturing the combined impact of exogenous and genetic risk factors 8-10 . To achieve this, RNA-seq data are typically first used to infer gene coexpression modules 5 ,

Background Antibodies to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) have been shown to neutralize the virus in-vitro and prevent disease in animal challenge models upon re-exposure. However, current understanding of SARS-CoV-2 humoral dynamics and longevity is conflicting. Methods The Co-Stars study prospectively enrolled 3679 healthcare workers to comprehensively characterize the kinetics of SARS-CoV-2 spike (S), receptor-binding-domain (RBD) and nucleoprotein (N) antibodies in parallel. Participants screening seropositive had serial monthly serological testing for a maximum of 7 months with the Mesoscale Discovery Assay. Survival analysis determined the proportion of sero-reversion while two hierarchical Gamma models predicted the upper- and lower-bounds of long-term antibody trajectory. Results A total of 1163 monthly samples were provided from 349 seropositive participants. At 200 days post-symptoms, >95% of participants had detectable S-antibodies compared to 75% with detectable N-antibodies. S-antibody was predicted to remain detectable in 95% of participants until 465 days [95%CI 370-575] using a ‘continuous-decay’ model and indefinitely using a ‘decay-to-plateau’ model to account for antibody secretion by long-lived plasma cells. S-antibody titers correlated strongly with surrogate neutralization in-vitro (R 2=0.72). N-antibodies, however, decayed rapidly with a half-life of 60 days [95%CI 52-68]. Conclusions The Co-STAR's study data presented here provides evidence for long-term persistence of neutralizing S-antibodies. This has important implications for the duration of functional immunity following SARS-CoV-2 infection. In contrast, the rapid decay of N-antibodies must be considered in future seroprevalence studies and public health decision-making. This is the first study to establish a mathematical framework capable of predicting long-term humoral dynamics following SARS-CoV-2 infection.

Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. The two major subtypes of RE are myopia (nearsightedness) and hyperopia (farsightedness), which represent opposite ends of the distribution of the quantitative measure of spherical refraction. We performed a fixed effects meta-analysis of genome-wide association results of myopia and hyperopia from 9 studies of European-derived populations: AREDS, KORA, FES, OGP-Talana, MESA, RSI, RSII, RSIII and ERF. One genome-wide significant region was observed for myopia, corresponding to a previously identified myopia locus on 8q12 (p = 1.25×10−8), which has been reported by Kiefer et al. as significantly associated with myopia age at onset and Verhoeven et al. as significantly associated to mean spherical-equivalent (MSE) refractive error. We observed two genome-wide significant associations with hyperopia. These regions overlapped with loci on 15q14 (minimum p value = 9.11×10−11) and 8q12 (minimum p value 1.82×10−11) previously reported for MSE and myopia age at onset. We also used an intermarker linkage- disequilibrium-based method for calculating the effective number of tests in targeted regional replication analyses. We analyzed myopia (which represents the closest phenotype in our data to the one used by Kiefer et al.) and showed replication of 10 additional loci associated with myopia previously reported by Kiefer et al. This is the first replication of these loci using myopia as the trait under analysis. “Replication-level” association was also seen between hyperopia and 12 of Kiefer et al.'s published loci. For the loci that show evidence of association to both myopia and hyperopia, the estimated effect of the risk alleles were in opposite directions for the two traits. This suggests that these loci are important contributors to variation of refractive error across the distribution.

RNA editing modifies transcripts and may alter their regulation or function. In humans, the most common modification is adenosine to inosine (A-to-I). We examined the global characteristics of RNA editing in 4,301 human tissue samples. More than 1.6 million A-to-I edits were identified in 62% of all protein-coding transcripts. mRNA recoding was extremely rare; only 11 novel recoding sites were uncovered. Thirty single nucleotide polymorphisms from genome-wide association studies were associated with RNA editing; one that influences type 2 diabetes (rs2028299) was associated with editing in ARPIN. Twenty-five genes, including LRP11 and PLIN5, had editing sites that were associated with plasma lipid levels. Our findings provide new insights into the genetic regulation of RNA editing and establish a rich catalogue for further exploration of this process.

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