A highly conserved program of neuronal microexons is misregulated in autistic brains.

Alternative splicing (AS) generates vast transcriptomic and proteomic complexity. However, which of the myriad of detected AS events provide important biological functions is not well understood. Here, we define the largest program of functionally coordinated, neural-regulated AS described to date in mammals. Relative to all other types of AS within this program, 3-15 nucleotide "microexons" display the most striking evolutionary conservation and switch-like regulation. These microexons modulate the function of interaction domains of proteins involved in neurogenesis. Most neural microexons are regulated by the neuronal-specific splicing factor nSR100/SRRM4, through its binding to adjacent intronic enhancer motifs. Neural microexons are frequently misregulated in the brains of individuals with autism spectrum disorder, and this misregulation is associated with reduced levels of nSR100. The results thus reveal a highly conserved program of dynamic microexon regulation associated with the remodeling of protein-interaction networks during neurogenesis, the misregulation of which is linked to autism.

3DLigandSite: structure-based prediction of protein-ligand binding sites.

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.

Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation.

Genome3D (https://www.genome3d.eu) is a freely available resource that provides consensus structural annotations for representative protein sequences taken from a selection of model organisms. Since the last NAR update in 2015, the method of data submission has been overhauled, with annotations now being 'pushed' to the database via an API. As a result, contributing groups are now able to manage their own structural annotations, making the resource more flexible and maintainable. The new submission protocol brings a number of additional benefits including: providing instant validation of data and avoiding the requirement to synchronise releases between resources. It also makes it possible to implement the submission of these structural annotations as an automated part of existing internal workflows. In turn, these improvements facilitate Genome3D being opened up to new prediction algorithms and groups. For the latest release of Genome3D (v2.1), the underlying dataset of sequences used as prediction targets has been updated using the latest reference proteomes available in UniProtKB. A number of new reference proteomes have also been added of particular interest to the wider scientific community: cow, pig, wheat and mycobacterium tuberculosis. These additions, along with improvements to the underlying predictions from contributing resources, has ensured that the number of annotations in Genome3D has nearly doubled since the last NAR update article. The new API has also been used to facilitate the dissemination of Genome3D data into InterPro, thereby widening the visibility of both the annotation data and annotation algorithms.

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants.

The interpretation of human genetic variation is one of the greatest challenges of modern genetics. New approaches are urgently needed to prioritize variants, especially those that are rare or lack a definitive clinical interpretation. We examined 10,136,597 human missense genetic variants from GnomAD, ClinVar and UniProt. We were able to perform large-scale atom-based mapping and phenotype interpretation of 3,960,015 of these variants onto 18,874 experimental and 84,818 in house predicted three-dimensional coordinates of the human proteome. We demonstrate that 14% of amino acid substitutions from the GnomAD database that could be structurally analysed are predicted to affect protein structure (n = 568,548, of which 566,439 rare or extremely rare) and may, therefore, have a yet unknown disease-causing effect. The same is true for 19.0% (n = 6266) of variants of unknown clinical significance or conflicting interpretation reported in the ClinVar database. The results of the structural analysis are available in the dedicated web catalogue Missense3D-DB ( http://missense3d.bc.ic.ac.uk/ ). For each of the 4 M variants, the results of the structural analysis are presented in a friendly concise format that can be included in clinical genetic reports. A detailed report of the structural analysis is also available for the non-experts in structural biology. Population frequency and predictions from SIFT and PolyPhen are included for a more comprehensive variant interpretation. This is the first large-scale atom-based structural interpretation of human genetic variation and offers geneticists and the biomedical community a new approach to genetic variant interpretation.

Modeling dark- and light-induced crystal structures and single-crystal optical absorption spectra of ruthenium-based complexes that undergo SO2-linkage photoisomerization.

A family of coordination complexes of the type [Ru(SO2)(NH3)4X]m+Yn - (m, n = 1 or 2) exhibit optical switching capabilities in their single-crystal states. This striking effect is caused by the light-induced formation of SO2-linkage photoisomers, which are metastable if kept at suitably cool temperatures. We modeled the dark- and light-induced states of these large crystalline complexes via plane-wave (PW)- and molecular-orbital (MO)-based density functional theory (DFT) and time-dependent DFT in order to calculate their structural and optical properties; the calculated results are compared with experimental data. We show that the PW-DFT-based periodic models replicate the structural properties of these complexes more effectively than the MO-DFT-based molecular-fragment models, observing only small deviations in key bond lengths relative to the experimentally derived crystal structures. The periodic models were also found to more effectively simulate trends seen in experimental optical absorption spectra, with optical absorbance and coverage of the visible region increasing with the formation of the photoinduced geometries. The contribution of the metastable photoisomeric species more heavily focuses on the lower-energy end of the spectra. Spectra generated from the molecular-fragment models are limited by the geometry of the fragment used and the number of excited-state roots considered in those calculations. In general, periodic models outperform the molecular-fragment models owing to their ability to better appreciate the periodic phenomena that are present in these crystalline materials as opposed to MO approaches, which are finite methods. We thus demonstrate that PW-DFT-based periodic models should be considered as a more than viable method for simulating the optical and electronic properties of these single-crystal optical switches.

Application of docking methodologies to modeled proteins.

Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.

Phylotranscriptomic Insights into the Diversification of Endothermic Thunnus Tunas.

Birds, mammals, and certain fishes, including tunas, opahs and lamnid sharks, are endothermic, conserving internally generated, metabolic heat to maintain body or tissue temperatures above that of the environment. Bluefin tunas are commercially important fishes worldwide, and some populations are threatened. They are renowned for their endothermy, maintaining elevated temperatures of the oxidative locomotor muscle, viscera, brain and eyes, and occupying cold, productive high-latitude waters. Less cold-tolerant tunas, such as yellowfin tuna, by contrast, remain in warm-temperate to tropical waters year-round, reproducing more rapidly than most temperate bluefin tuna populations, providing resiliency in the face of large-scale industrial fisheries. Despite the importance of these traits to not only fisheries but also habitat utilization and responses to climate change, little is known of the genetic processes underlying the diversification of tunas. In collecting and analyzing sequence data across 29,556 genes, we found that parallel selection on standing genetic variation is associated with the evolution of endothermy in bluefin tunas. This includes two shared substitutions in genes encoding glycerol-3 phosphate dehydrogenase, an enzyme that contributes to thermogenesis in bumblebees and mammals, as well as four genes involved in the Krebs cycle, oxidative phosphorylation, ß-oxidation, and superoxide removal. Using phylogenetic techniques, we further illustrate that the eight Thunnus species are genetically distinct, but found evidence of mitochondrial genome introgression across two species. Phylogeny-based metrics highlight conservation needs for some of these species.

Identification of disease-associated loci using machine learning for genotype and network data integration.

MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. RESULTS: We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals' ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user's research needs. AVAILABILITY AND IMPLEMENTATION: An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology.

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research.

Focused Cardiac Ultrasound in Dyspnea of Unclear Etiology in the Emergency Department: Utility of the FLUID Score.

OBJECTIVES: We evaluated the utility of focused cardiac ultrasound to predict the length of stay in patients presenting to the emergency department with dyspnea of unclear etiology. METHODS: Patients with focused cardiac ultrasound examinations performed in the emergency department for dyspnea over a 34-month period were retrospectively identified. Patients were excluded if they had a prior diagnosis of heart failure, dialysis requirement, or an etiology of dyspnea unrelated to the volume status. Left ventricular function was categorized as normal or reduced, and the inferior vena cava was categorized as normal or increased volume. A fast limited ultrasound to investigate dyspnea (FLUID) score was calculated by adding 1 point for reduced left ventricular function and 1 point for increased volume, producing a score of 0, 1, or 2. RESULTS: There were 123 patients included. There was a significant correlation between the FLUID score and length of stay, with longer stays for higher scores (FLUID score 0, 7.4 hours median; FLUID score 1, 2.34 days; FLUID score 2, 5.56 days; analysis of variance P < .001). Of the other variables collected, only age, hypertension, diabetes, heart rate, and brain natriuretic peptide were significantly correlated with the length of stay. A multivariate analysis including those variables and the FLUID score showed that the FLUID score was the strongest independent predictor of the length of stay. CONCLUSIONS: Focused cardiac ultrasound and calculation of a FLUID score for patients with undifferentiated dyspnea can be a powerful tool to predict the hospital length of stay.

Sepsis-Induced Cardiomyopathy: a Comprehensive Review.

PURPOSE OF REVIEW: To briefly review epidemiology and pathophysiology of SICM and provide a more extensive review of the data on diagnostic and management strategies. RECENT FINDINGS: SICM is likely underdiagnosed and that has mortality implications. Current evidence supports speckle tracking echocardiography to identify decreased contractility irrespective of left ventricular ejection fraction for the diagnosis of SICM. There continues to be a dearth of large clinical trials evaluating the treatment of SICM and current consensus focuses on supportive measures such as vasopressors and inotropes. Sepsis is a significant cause of mortality, and sepsis-induced cardiomyopathy has both prognostic and management implications for these patients. Individualized work-up and management of these patients is crucial to improving outcomes.

PhenoRank: reducing study bias in gene prioritization through simulation.

Motivation: Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10-16). Availability and implementation: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information: Supplementary data are available at Bioinformatics online.

Elevated Cardiac Troponin in Clinical Scenarios Beyond Obstructive Coronary Artery Disease.

In this systematic review article, we aim to summarize the most up-to-date evidence regarding elevations of cardiac troponin, especially in clinical scenarios other than obstructive coronary artery disease. The accurate interpretation of raised cardiac troponin is challenging because it relies on unconfirmed postulations and dogmatic knowledge (e.g., the exclusive provenience of cardiac troponin from cardiac myocytes), based on which every troponin elevation is assumed to definitely indicate myocardial damage. Indeed, the investigation of the pathophysiologic mechanism leading to the release in the bloodstream of cardiac biomarkers should be the first step of the diagnostic process to fully understand the clinical significance of the elevated serum levels and identify the best management. A prominent effort should be put in place to identify the contribution of potential confounding factors, both cardiac and non-cardiac in etiology, with the ability to affect synthesis and clearance of cardiac biomarkers. Regardless of the underlying cause, it is well established that cardiovascular biomarkers are increasingly useful to further risk stratification and prognosticate patients. Accordingly, we sought to clarify the meaning and impact of elevated cardiac troponin in those frequently encountered real-world scenarios presenting clinicians with a diagnostic dilemma, with the final goal of facilitating the diagnosis and help optimize individually tailored treatment strategies.

k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets.

k-SLAM is a highly efficient algorithm for the characterization of metagenomic data. Unlike other ultra-fast metagenomic classifiers, full sequence alignment is performed allowing for gene identification and variant calling in addition to accurate taxonomic classification. A k-mer based method provides greater taxonomic accuracy than other classifiers and a three orders of magnitude speed increase over alignment based approaches. The use of alignments to find variants and genes along with their taxonomic origins enables novel strains to be characterized. k-SLAM's speed allows a full taxonomic classification and gene identification to be tractable on modern large data sets. A pseudo-assembly method is used to increase classification accuracy by up to 40% for species which have high sequence homology within their genus.

Properties of human genes guided by their enrichment in rare and common variants.

We analyzed 563,099 common (minor allele frequency, MAF≥0.01) and rare (MAF < 0.01) genetic variants annotated in ExAC and UniProt and 26,884 disease-causing variants from ClinVar and UniProt occurring in the coding region of 17,975 human protein-coding genes. Three novel sets of genes were identified: those enriched in rare variants (n = 32 genes), in common variants (n = 282 genes), and in disease-causing variants (n = 800 genes). Genes enriched in rare variants have far greater similarities in terms of biological and network properties to genes enriched in disease-causing variants, than to genes enriched in common variants. However, in half of the genes enriched in rare variants (AOC2, MAMDC4, ANKHD1, CDC42BPB, SPAG5, TRRAP, TANC2, IQCH, USP54, SRRM2, DOPEY2, and PITPNM1), no disease-causing variants have been identified in major, publicly available databases. Thus, genetic variants in these genes are strong candidates for disease and their identification, as part of sequencing studies, should prompt further in vitro analyses.

Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights.

Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analyzed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological, and circulatory diseases and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared with non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (P < 0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared with non-pleiotropic proteins and a better understanding of their characteristics and genetic variants can greatly aid in the interpretation of genetic studies and drug design.

Genome3D: exploiting structure to help users understand their sequences.

Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.

Three-Dimensional Model of Human Nicotinamide Nucleotide Transhydrogenase (NNT) and Sequence-Structure Analysis of its Disease-Causing Variations.

Defective mitochondrial proteins are emerging as major contributors to human disease. Nicotinamide nucleotide transhydrogenase (NNT), a widely expressed mitochondrial protein, has a crucial role in the defence against oxidative stress. NNT variations have recently been reported in patients with familial glucocorticoid deficiency (FGD) and in patients with heart failure. Moreover, knockout animal models suggest that NNT has a major role in diabetes mellitus and obesity. In this study, we used experimental structures of bacterial transhydrogenases to generate a structural model of human NNT (H-NNT). Structure-based analysis allowed the identification of H-NNT residues forming the NAD binding site, the proton canal and the large interaction site on the H-NNT dimer. In addition, we were able to identify key motifs that allow conformational changes adopted by domain III in relation to its functional status, such as the flexible linker between domains II and III and the salt bridge formed by H-NNT Arg882 and Asp830. Moreover, integration of sequence and structure data allowed us to study the structural and functional effect of deleterious amino acid substitutions causing FGD and left ventricular non-compaction cardiomyopathy. In conclusion, interpretation of the function-structure relationship of H-NNT contributes to our understanding of mitochondrial disorders.

AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis.

BACKGROUND: Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery. RESULTS: AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site. CONCLUSIONS: Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.