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Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets. Increasing amount of public omics data are important and valuable resources for the research community. Here, the authors develop a set of metrics to quantify the attention and impact of biomedical datasets and integrate them into the framework of Omics Discovery Index (OmicsDI).

Maintenance of connective tissue integrity is fundamental to sustain function, requiring protein turnover to repair damaged tissue. However, connective tissue proteome dynamics remain largely undefined, as do differences in turnover rates of individual proteins in the collagen and glycoprotein phases of connective tissue extracellular matrix (ECM). Here, we investigate proteome dynamics in the collagen and glycoprotein phases of connective tissues by exploiting the spatially distinct fascicular (collagen-rich) and interfascicular (glycoprotein-rich) ECM phases of tendon. Using isotope labelling, mass spectrometry and bioinformatics, we calculate turnover rates of individual proteins within rat Achilles tendon and its ECM phases. Our results demonstrate complex proteome dynamics in tendon, with ~1000 fold differences in protein turnover rates, and overall faster protein turnover within the glycoprotein-rich interfascicular matrix compared to the collagen-rich fascicular matrix. These data provide insights into the complexity of proteome dynamics in tendon, likely required to maintain tissue homeostasis. Muscles are anchored to bones through specialized tissues called tendons. Made of bundles of fibers (or fascicles) linked together by an ‘interfascicular’ matrix, healthy tendons are required for organisms to move properly. Yet, these structures are constantly exposed to damage: the interfascicular matrix, in particular, is highly susceptible to injury as it allows the fascicles to slide on each other. One way to avoid damage could be for the body to continually replace proteins in tendons before they become too impaired. However, the way proteins are renewed in these structures is currently not well understood – indeed, it has long been assumed that almost no protein turnover occurs in tendons. In particular, it is unknown whether proteins in the interfascicular matrix have a higher turn over than those in the fascicles. To investigate, Choi, Simpson et al. fed rats on water carrying a molecular label that becomes integrated into new proteins. Analysis of individual proteins from the rats’ tendons showed great variation in protein turnover, with some replaced every few days and others only over several years. This suggests that protein turnover is actually an important part of tendon health. In particular, the results show that turnover is higher in the interfascicular matrix, where damage is expected to be more likely. Protein turnover also plays a part in conditions such as cancer, heart disease and kidney disease. Using approaches like the one developed by Choi, Simpson et al. could help to understand how individual proteins are renewed in a range of diseases, and how to design new treatments.

Vitamin A has diverse biological functions. It is transported in the blood as a complex with retinol binding protein (RBP), but the molecular mechanism by which vitamin A is absorbed by cells from the vitamin A-RBP complex is not clearly understood. We identified in bovine retinal pigment epithelium cells STRA6, a multitransmembrane domain protein, as a specific membrane receptor for RBP. STRA6 binds to RBP with high affinity and has robust vitamin A uptake activity from the vitamin A-RBP complex. It is widely expressed in embryonic development and in adult organ systems. The RBP receptor represents a major physiological mediator of cellular vitamin A uptake.

The voltage-dependent anion channel (VDAC) constitutes the major pathway for the entry and exit of metabolites across the outer membrane of the mitochondria and can serve as a scaffold for molecules that modulate the organelle. We report the crystal structure of a β-barrel eukaryotic membrane protein, the murine VDAC1 (mVDAC1) at 2.3 Å resolution, revealing a high-resolution image of its architecture formed by 19 β-strands. Unlike the recent NMR structure of human VDAC1, the position of the voltage-sensing N-terminal segment is clearly resolved. The α-helix of the N-terminal segment is oriented against the interior wall, causing a partial narrowing at the center of the pore. This segment is ideally positioned to regulate the conductance of ions and metabolites passing through the VDAC pore.

Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo. Transcriptome data provide only a partial picture of disease states. Here, via integration of transcript-, protein abundance and protein turnover data for a mouse model of cardiac hypertrophy, the authors uncover additional disease gene signatures, and show that turnover data sheds unique light on posttranslational regulation.

The branched-chain amino acids (BCAA) are essential amino acids required for protein homeostasis, energy balance, and nutrient signaling. In individuals with deficiencies in BCAA, these amino acids can be preserved through inhibition of the branched-chain-alpha-ketoacid dehydrogenase (BCKD) complex, the rate-limiting step in their metabolism. BCKD is inhibited by phosphorylation of its E1alpha subunit at Ser293, which is catalyzed by BCKD kinase. During BCAA excess, phosphorylated Ser293 (pSer293) becomes dephosphorylated through the concerted inhibition of BCKD kinase and the activity of an unknown intramitochondrial phosphatase. Using unbiased, proteomic approaches, we have found that a mitochondrial-targeted phosphatase, PP2Cm, specifically binds the BCKD complex and induces dephosphorylation of Ser293 in the presence of BCKD substrates. Loss of PP2Cm completely abolished substrate-induced E1alpha dephosphorylation both in vitro and in vivo. PP2Cm-deficient mice exhibited BCAA catabolic defects and a metabolic phenotype similar to the intermittent or intermediate types of human maple syrup urine disease (MSUD), a hereditary disorder caused by defects in BCKD activity. These results indicate that PP2Cm is the endogenous BCKD phosphatase required for nutrient-mediated regulation of BCKD activity and suggest that defects in PP2Cm may be responsible for a subset of human MSUD.

Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Methods for quantitative assessments of protein kinetics are lacking, and despite knowledge gained from single-protein studies, integrative views of the coordinated behavior of multiple proteins in cardiac remodeling are scarce. Here, we developed a workflow that integrates deuterium oxide (2H2O) labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. Using this workflow, we characterized the in vivo turnover kinetics of 2,964 proteins in a mouse model of β-adrenergic-induced cardiac remodeling. The data provided a quantitative and longitudinal view of cardiac remodeling at the molecular level, revealing widespread kinetic regulations in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. We translated the workflow to human studies, creating a reference dataset of 496 plasma protein turnover rates from 4 healthy adults. The approach is applicable to short, minimal label enrichment and can be performed on as little as a single biopsy, thereby overcoming critical obstacles to clinical investigations. The protein turnover quantitation experiments and computational workflow described here should be widely applicable to large-scale biomolecular investigations of human disease mechanisms with a temporal perspective.

Background Alu elements are major contributors to lineage-specific new exons in primate and human genomes. Recent studies indicate that some Alu exons have high transcript inclusion levels or tissue-specific splicing profiles, and may play important regulatory roles in modulating mRNA degradation or translational efficiency. However, the contribution of Alu exons to the human proteome remains unclear and controversial. The prevailing view is that exons derived from young repetitive elements, such as Alu elements, are restricted to regulatory functions and have not had adequate evolutionary time to be incorporated into stable, functional proteins. Results We adopt a proteotranscriptomics approach to systematically assess the contribution of Alu exons to the human proteome. Using RNA sequencing, ribosome profiling, and proteomics data from human tissues and cell lines, we provide evidence for the translational activities of Alu exons and the presence of Alu exon derived peptides in human proteins. These Alu exon peptides represent species-specific protein differences between primates and other mammals, and in certain instances between humans and closely related primates. In the case of the RNA editing enzyme ADARB1, which contains an Alu exon peptide in its catalytic domain, RNA sequencing analyses of A-to-I editing demonstrate that both the Alu exon skipping and inclusion isoforms encode active enzymes. The Alu exon derived peptide may fine tune the overall editing activity and, in limited cases, the site selectivity of ADARB1 protein products. Conclusions Our data indicate that Alu elements have contributed to the acquisition of novel protein sequences during primate and human evolution.