Structure-based prediction and characterization of photo-crosslinking in native protein-RNA complexes.

UV-crosslinking of protein and RNA in direct contacts has been widely used to study protein-RNA complexes while our understanding of the photo-crosslinking mechanisms remains poor. This knowledge gap is due to the challenge of precisely mapping the crosslink sites in protein and RNA simultaneously in their native sequence and structural contexts. Here we systematically analyze protein-RNA interactions and photo-crosslinking by bridging crosslinked nucleotides and amino acids mapped using different assays with protein-RNA complex structures. We developed a computational method PxR3D-map which reliably predicts crosslink sites using structural information characterizing protein-RNA interaction interfaces. Analysis of the informative features revealed that photo-crosslinking is facilitated by base stacking with not only aromatic residues, but also dipeptide bonds that involve glycine, and distinct mechanisms are utilized by different RNA-binding domains. Our work suggests protein-RNA photo-crosslinking is highly selective in the cellular environment, which can guide data interpretation and further technology development for UV-crosslinking-based assays.

Eicosapentaenoic acid and Arachidonic acid Protection Against Left Ventricle Pathology: the Multi-Ethnic Study of Atherosclerosis.

Background: We have shown that ω3 polyunsaturated fatty acids (PUFAs) reduce risk for heart failure, regardless of ejection fraction status. Ventricular remodeling and reduced ventricular performance precede overt hear failure, however there is little insight into how PUFAs contribute to maladaptive signaling over time. PUFAs are agonists for regulatory activity at g-protein coupled receptors such as Ffar4, and downstream as substrates for monooxygenases (e.g lipoxygenase, cytochrome p450, or cyclooxygenase (COX)) which mediate intracellular adaptive signaling. Methods: Plasma phospholipid PUFA abundance at Exam 1 as mass percent EPA, DHA, and arachidonic acid (AA) from the Multi-Ethnic Study of Atherosclerosis (MESA) were evaluated using pathway modeling to determine the association with time-dependent changes in left ventricular (LV) mass (LVM), end-diastolic LV volume (EDV), and end-systolic volume (ESV) measured by cardiac MRI at Exams 1 and 5. Ejection fraction (EF) and mass:volume (MV) were calculated posteriorly from the first three. Results: 2,877 subjects had available MRI data. Participants with low AA and EPA had accelerated age-dependent declines in LVM. Males with low AA and EPA also had accelerated declines in EDV, but among females there was no PUFA association with EDV declines and exam 5 EDV status was positively associated with AA. Both sexes had nearly the same positive association of AA with changes in ESV. Conclusion: Plasma phospholipid AA and EPA are prospectively associated with indices of heart remodeling, including ventricular remodeling and performance. Combined AA and EPA scarcity was associated with the most accelerated age-related changes and exam 5 status, while the greatest benefits were found among participants with both PUFAs. This suggests that both PUFAs are required for optimal slowing of age-related declines in ventricular function.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here, we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, our framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.