NMR structure of the chimeric hybrid duplex r(gcaguggc)⋅r(gcca)d(CTGC) comprising the tRNA-DNA junction formed during initiation of HIV-1 reverse transcription

A high-quality NMR solution structure of the chimeric hybrid duplex r(gcaguggc)⋅r(gcca)d(CTGC) was determined using the program DYANA with its recently implemented new module FOUND, which performs exhaustive conformational grid searches for dinucleotides. To ensure conservative data interpretation, the use of 1H-1H lower distance limit constraints was avoided. The duplex comprises the tRNA-DNA junction formed during the initiation of HIV-1 reverse transcription. It forms an A-type double helix that exhibits distinct structural deviations from a standard A-conformation. In particular, the minor groove is remarkably narrow, and its width decreases from about 7.5Å in the RNA/RNA stem to about 4.5Å in the RNA/DNA segment. This is unexpected, since minor groove widths for A-RNA and RNA/DNA hybrid duplexes of ∼11Å and ∼8.5Å, respectively, were previously reported. The present, new structure supports that reverse transcriptase-associated RNaseH specificity is related primarily to conformational adaptability of the nucleic acid in 'induced-fit'-type interactions, rather than the minor groove width of a predominantly static nucleic acid duple

Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition

Cholesteryl ester transfer protein (CETP) inhibition reduces vascular event risk, but confusion surrounds its effects on low-density lipoprotein (LDL) cholesterol. Here, we clarify associations of genetic inhibition of CETP on detailed lipoprotein measures and compare those to genetic inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR). We used an allele associated with lower CETP expression (rs247617) to mimic CETP inhibition and an allele associated with lower HMGCR expression (rs12916) to mimic the well-known effects of statins for comparison. The study consists of 65,427 participants of European ancestries with detailed lipoprotein subclass profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% reduction in relative risk of coronary heart disease (CHD). We also examined observational associations of the lipoprotein subclass measures with risk of incident CHD in 3 population-based cohorts totalling 616 incident cases and 13,564 controls during 8-year follow-up. Genetic inhibition of CETP and HMGCR resulted in near-identical associations with LDL cholesterol concentration estimated by the Friedewald equation. Inhibition of HMGCR had relatively consistent associations on lower cholesterol concentrations across all apolipoprotein B-containing lipoproteins. In contrast, the associations of the inhibition of CETP were stronger on lower remnant and very-low-density lipoprotein (VLDL) cholesterol, but there were no associations on cholesterol concentrations in LDL defined by particle size (diameter 18-26 nm) (-0.02 SD LDL defined by particle size; 95% CI: -0.10 to 0.05 for CETP versus -0.24 SD, 95% CI -0.30 to -0.18 for HMGCR). Inhibition of CETP was strongly associated with lower proportion of triglycerides in all high-density lipoprotein (HDL) particles. In observational analyses, a higher triglyceride composition within HDL subclasses was associated with higher risk of CHD, independently of total cholesterol and triglycerides (strongest hazard ratio per 1 SD higher triglyceride composition in very large HDL 1.35; 95% CI: 1.18-1.54). In conclusion, CETP inhibition does not appear to affect size-specific LDL cholesterol but is likely to lower CHD risk by lowering concentrations of other atherogenic, apolipoprotein B-containing lipoproteins (such as remnant and VLDLs). Inhibition of CETP also lowers triglyceride composition in HDL particles, a phenomenon reflecting combined effects of circulating HDL, triglycerides, and apolipoprotein B-containing particles and is associated with a lower CHD risk in observational analyses. Our results reveal that conventional composite lipid assays may mask heterogeneous effects of emerging lipid-altering therapies.Peer reviewe

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

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

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

peer reviewe

Development of a Rule-Based Method for the Assessment of Protein Druggability

Target selection is a critical step in the majority of modern drug discovery programs. The viability of a drug target depends on two components: biological relevance and chemical tractability. The concept of druggability was introduced to describe the second component, and it is defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity. To investigate the rules that govern druggability, we developed an algorithm to isolate and characterize the binding pockets of protein targets. Using this algorithm, we performed a comparative analysis between the relevant pockets of 60 targets of approved drugs and a diverse set of 440 ligand-binding pockets. As a result, we defined a preferred property space for druggable pockets based on five key properties (volume, depth, enclosure, percentage of charged residues and hydrophobicity), and we represented it with a set of simple rules. These rules may be applicable in the future to evaluate the chemical tractability of prospective targets

Development of a Rule-Based Method for the Assessment of Protein Druggability

Target selection is a critical step in the majority of modern drug discovery programs. The viability of a drug target depends on two components: biological relevance and chemical tractability. The concept of druggability was introduced to describe the second component, and it is defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity. To investigate the rules that govern druggability, we developed an algorithm to isolate and characterize the binding pockets of protein targets. Using this algorithm, we performed a comparative analysis between the relevant pockets of 60 targets of approved drugs and a diverse set of 440 ligand-binding pockets. As a result, we defined a preferred property space for druggable pockets based on five key properties (volume, depth, enclosure, percentage of charged residues and hydrophobicity), and we represented it with a set of simple rules. These rules may be applicable in the future to evaluate the chemical tractability of prospective targets

Development of a Rule-Based Method for the Assessment of Protein Druggability

Target selection is a critical step in the majority of modern drug discovery programs. The viability of a drug target depends on two components: biological relevance and chemical tractability. The concept of druggability was introduced to describe the second component, and it is defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity. To investigate the rules that govern druggability, we developed an algorithm to isolate and characterize the binding pockets of protein targets. Using this algorithm, we performed a comparative analysis between the relevant pockets of 60 targets of approved drugs and a diverse set of 440 ligand-binding pockets. As a result, we defined a preferred property space for druggable pockets based on five key properties (volume, depth, enclosure, percentage of charged residues and hydrophobicity), and we represented it with a set of simple rules. These rules may be applicable in the future to evaluate the chemical tractability of prospective targets