Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Riboneogenesis in yeast.

Glucose is catabolized in yeast via two fundamental routes, glycolysis and the oxidative pentose phosphate pathway, which produces NADPH and the essential nucleotide component ribose-5-phosphate. Here, we describe riboneogenesis, a thermodynamically driven pathway that converts glycolytic intermediates into ribose-5-phosphate without production of NADPH. Riboneogenesis begins with synthesis, by the combined action of transketolase and aldolase, of the seven-carbon bisphosphorylated sugar sedoheptulose-1,7-bisphosphate. In the pathway's committed step, sedoheptulose bisphosphate is hydrolyzed to sedoheptulose-7-phosphate by the enzyme sedoheptulose-1,7-bisphosphatase (SHB17), whose activity we identified based on metabolomic analysis of the corresponding knockout strain. The crystal structure of Shb17 in complex with sedoheptulose-1,7-bisphosphate reveals that the substrate binds in the closed furan form in the active site. Sedoheptulose-7-phosphate is ultimately converted by known enzymes of the nonoxidative pentose phosphate pathway to ribose-5-phosphate. Flux through SHB17 increases when ribose demand is high relative to demand for NADPH, including during ribosome biogenesis in metabolically synchronized yeast cells.

Unraveling the message: insights into comparative genomics of the naked mole-rat.

Animals have evolved to survive, and even thrive, in different environments. Genetic adaptations may have indirectly created phenotypes that also resulted in a longer lifespan. One example of this phenomenon is the preternaturally long-lived naked mole-rat. This strictly subterranean rodent tolerates hypoxia, hypercapnia, and soil-based toxins. Naked mole-rats also exhibit pronounced resistance to cancer and an attenuated decline of many physiological characteristics that often decline as mammals age. Elucidating mechanisms that give rise to their unique phenotypes will lead to better understanding of subterranean ecophysiology and biology of aging. Comparative genomics could be a useful tool in this regard. Since the publication of a naked mole-rat genome assembly in 2011, analyses of genomic and transcriptomic data have enabled a clearer understanding of mole-rat evolutionary history and suggested molecular pathways (e.g., NRF2-signaling activation and DNA damage repair mechanisms) that may explain the extraordinarily longevity and unique health traits of this species. However, careful scrutiny and re-analysis suggest that some identified features result from incorrect or imprecise annotation and assembly of the naked mole-rat genome: in addition, some of these conclusions (e.g., genes involved in cancer resistance and hairlessness) are rejected when the analysis includes additional, more closely related species. We describe how the combination of better study design, improved genomic sequencing techniques, and new bioinformatic and data analytical tools will improve comparative genomics and ultimately bridge the gap between traditional model and nonmodel organisms.

Genetics implicates overactive osteogenesis in the development of diffuse idiopathic skeletal hyperostosis.

Diffuse idiopathic skeletal hyperostosis (DISH) is a condition where adjacent vertebrae become fused through formation of osteophytes. The genetic and epidemiological etiology of this condition is not well understood. Here, we implemented a machine learning algorithm to assess the prevalence and severity of the pathology in ~40,000 lateral DXA scans in the UK Biobank Imaging cohort. We find that DISH is highly prevalent, above the age of 45, ~20% of men and ~8% of women having multiple osteophytes. Surprisingly, we find strong phenotypic and genetic association of DISH with increased bone mineral density and content throughout the entire skeletal system. Genetic association analysis identified ten loci associated with DISH, including multiple genes involved in bone remodeling (RUNX2, IL11, GDF5, CCDC91, NOG, and ROR2). Overall, this study describes genetics of DISH and implicates the role of overactive osteogenesis as a key driver of the pathology.

EpiFire: An open source C++ library and application for contact network epidemiology.

BACKGROUND: Contact network models have become increasingly common in epidemiology, but we lack a flexible programming framework for the generation and analysis of epidemiological contact networks and for the simulation of disease transmission through such networks. RESULTS: Here we present EpiFire, an applications programming interface and graphical user interface implemented in C++, which includes a fast and efficient library for generating, analyzing and manipulating networks. Network-based percolation and chain-binomial simulations of susceptible-infected-recovered disease transmission, as well as traditional non-network mass-action simulations, can be performed using EpiFire. CONCLUSIONS: EpiFire provides an open-source programming interface for the rapid development of network models with a focus in contact network epidemiology. EpiFire also provides a point-and-click interface for generating networks, conducting epidemic simulations, and creating figures. This interface is particularly useful as a pedagogical tool.

Joint inference of physiological network and survival analysis identifies factors associated with aging rate.

We describe methodology for joint reconstruction of physiological-survival networks from observational data capable of identifying key survival-associated variables, inferring a minimal physiological network structure, and bridging this network to the Gompertzian survival layer. Using synthetic network structures, we show that the method is capable of identifying aging variables in cohorts as small as 5,000 participants. Applying the methodology to the observational human cohort, we find that interleukin-6, vascular calcification, and red-blood distribution width are strong predictors of baseline fitness. More important, we find that red blood cell counts, kidney function, and phosphate level are directly linked to the Gompertzian aging rate. Our model therefore enables discovery of processes directly linked to the aging rate of our species. We further show that this epidemiological framework can be applied as a causal inference engine to simulate the effects of interventions on physiology and longevity.

Global patterns of prognostic biomarkers across disease space.

There is a multitude of pathological conditions that affect human health, yet we currently lack a predictive model for most diseases, and underlying mechanisms that are shared by multiple diseases are poorly understood. We leveraged baseline clinical biomarker data and long-term disease outcomes in UK Biobank to build prognostic multivariate survival models for over 200 most common diseases. We construct a similarity map between biomarker-disease hazard ratios and demonstrate broad patterns of shared similarity in biomarker profiles across the entire disease space. Further aggregation of risk profiles through density based clustering showed that biomarker-risk profiles can be partitioned into few distinct clusters with characteristic patterns representative of broad disease categories. To confirm these risk patterns we built disease co-occurrence networks in the UK Biobank and US HCUP hospitalization databases, and compared similarity in biomarker risk profiles to disease co-occurrence. We show that proximity in the biomarker-disease space is strongly related to the occurrence of disease comorbidity, suggesting biomarker profile patterns can be used for both predicting future outcomes as well as a sensitive mechanism for detecting under-diagnosed disease states.

MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform.

MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.

Calcification of the abdominal aorta is an under-appreciated cardiovascular disease risk factor in the general population.

Calcification of large arteries is a high-risk factor in the development of cardiovascular diseases, however, due to the lack of routine monitoring, the pathology remains severely under-diagnosed and prevalence in the general population is not known. We have developed a set of machine learning methods to quantitate levels of abdominal aortic calcification (AAC) in the UK Biobank imaging cohort and carried out the largest to-date analysis of genetic, biochemical, and epidemiological risk factors associated with the pathology. In a genetic association study, we identified three novel loci associated with AAC (FGF9, NAV9, and APOE), and replicated a previously reported association at the TWIST1/HDAC9 locus. We find that AAC is a highly prevalent pathology, with ~ 1 in 10 adults above the age of 40 showing significant levels of hydroxyapatite build-up (Kauppila score > 3). Presentation of AAC was strongly predictive of future cardiovascular events including stenosis of precerebral arteries (HR~1.5), myocardial infarction (HR~1.3), ischemic heart disease (HR~1.3), as well as other diseases such as chronic obstructive pulmonary disease (HR~1.3). Significantly, we find that the risk for myocardial infarction from elevated AAC (HR ~1.4) was comparable to the risk of hypercholesterolemia (HR~1.4), yet most people who develop AAC are not hypercholesterolemic. Furthermore, the overwhelming majority (98%) of individuals who develop pathology do so in the absence of known pre-existing risk conditions such as chronic kidney disease and diabetes (0.6% and 2.7% respectively). Our findings indicate that despite the high cardiovascular risk, calcification of large arteries remains a largely under-diagnosed lethal condition, and there is a clear need for increased awareness and monitoring of the pathology in the general population.

Stochastic noise in splicing machinery.

The number of known alternative human isoforms has been increasing steadily with the amount of available transcription data. To date, over 100 000 isoforms have been detected in EST libraries, and at least 75% of human genes have at least one alternative isoform. In this paper, we propose that most alternative splicing events are the result of noise in the splicing process. We show that the number of isoforms and their abundance can be predicted by a simple stochastic noise model that takes into account two factors: the number of introns in a gene and the expression level of a gene. The results strongly support the hypothesis that most alternative splicing is a consequence of stochastic noise in the splicing machinery, and has no functional significance. The results are also consistent with error rates tuned to ensure that an adequate level of functional product is produced and to reduce the toxic effect of accumulation of misfolding proteins. Based on simulation of sampling of virtual cDNA libraries, we estimate that error rates range from 1 to 10% depending on the number of introns and the expression level of a gene.

Structural implication of splicing stochastics.

Even though nearly every human gene has at least one alternative splice form, very little is so far known about the structure and function of resulting protein products. It is becoming increasingly clear that a significant fraction of all isoforms are products of noisy selection of splice sites and thus contribute little to actual functional diversity, and may potentially be deleterious. In this study, we examine the impact of alternative splicing on protein sequence and structure in three datasets: alternative splicing events conserved across multiple species, alternative splicing events in genes that are strongly linked to disease and all observed alternative splicing events. We find that the vast majority of all alternative isoforms result in unstable protein conformations. In contrast to that, the small subset of isoforms conserved across species tends to maintain protein structural integrity to a greater extent. Alternative splicing in disease-associated genes produces unstable structures just as frequently as all other genes, indicating that selection to reduce the effects of alternative splicing on this set is not especially pronounced. Overall, the properties of alternative spliced proteins are consistent with the outcome of noisy selection of splice sites by splicing machinery.

Metabolomic analysis and visualization engine for LC-MS data.

Metabolomic analysis by liquid chromatography-high-resolution mass spectrometry results in data sets with thousands of features arising from metabolites, fragments, isotopes, and adducts. Here we describe a software package, Metabolomic Analysis and Visualization ENgine (MAVEN), designed for efficient interactive analysis of LC-MS data, including in the presence of isotope labeling. The software contains tools for all aspects of the data analysis process, from feature extraction to pathway-based graphical data display. To facilitate data validation, a machine learning algorithm automatically assesses peak quality. Users interact with raw data primarily in the form of extracted ion chromatograms, which are displayed with overlaid circles indicating peak quality, and bar graphs of peak intensities for both unlabeled and isotope-labeled metabolite forms. Click-based navigation leads to additional information, such as raw data for specific isotopic forms or for metabolites changing significantly between conditions. Fast data processing algorithms result in nearly delay-free browsing. Drop-down menus provide tools for the overlay of data onto pathway maps. These tools enable animating series of pathway graphs, e.g., to show propagation of labeled forms through a metabolic network. MAVEN is released under an open source license at http://maven.princeton.edu.

Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer.

We present a liquid chromatography-mass spectrometry (LC-MS) method that capitalizes on the mass-resolving power of the orbitrap to enable sensitive and specific measurement of known and unanticipated metabolites in parallel, with a focus on water-soluble species involved in core metabolism. The reversed phase LC method, with a cycle time 25 min, involves a water-methanol gradient on a C18 column with tributylamine as the ion pairing agent. The MS portion involves full scans from 85 to 1000 m/z at 1 Hz and 100,000 resolution in negative ion mode on a stand alone orbitrap ("Exactive"). The median limit of detection, across 80 metabolite standards, was 5 ng/mL with the linear range typically >or=100-fold. For both standards and a cellular extract from Saccharomyces cerevisiae (Baker's yeast), the median inter-run relative standard deviation in peak intensity was 8%. In yeast exact, we detected 137 known compounds, whose (13)C-labeling patterns could also be tracked to probe metabolic flux. In yeast engineered to lack a gene of unknown function (YKL215C), we observed accumulation of an ion of m/z 128.0351, which we subsequently confirmed to be oxoproline, resulting in annotation of YKL215C as an oxoprolinase. These examples demonstrate the suitability of the present method for quantitative metabolomics, fluxomics, and discovery metabolite profiling.

A domino effect in antifolate drug action in Escherichia coli.

Mass spectrometry technologies for measurement of cellular metabolism are opening new avenues to explore drug activity. Trimethoprim is an antibiotic that inhibits bacterial dihydrofolate reductase (DHFR). Kinetic flux profiling with (15)N-labeled ammonia in Escherichia coli reveals that trimethoprim leads to blockade not only of DHFR but also of another critical enzyme of folate metabolism: folylpoly-gamma-glutamate synthetase (FP-gamma-GS). Inhibition of FP-gamma-GS is not directly due to trimethoprim. Instead, it arises from accumulation of DHFR's substrate dihydrofolate, which we show is a potent FP-gamma-GS inhibitor. Thus, owing to the inherent connectivity of the metabolic network, falling DHFR activity leads to falling FP-gamma-GS activity in a domino-like cascade. This cascade results in complex folate dynamics, and its incorporation in a computational model of folate metabolism recapitulates the dynamics observed experimentally. These results highlight the potential for quantitative analysis of cellular metabolism to reveal mechanisms of drug action.

Coupling of DNA binding and helicase activity is mediated by a conserved loop in the MCM protein.

Minichromosome maintenance (MCM) helicases are the presumptive replicative helicases, thought to separate the two strands of chromosomal DNA during replication. In archaea, the catalytic activity resides within the C-terminal region of the MCM protein. In Methanothermobacter thermautotrophicus the N-terminal portion of the protein was shown to be involved in protein multimerization and binding to single and double stranded DNA. MCM homologues from many archaeal species have highly conserved predicted amino acid similarity in a loop located between beta7 and beta8 in the N-terminal part of the molecule. This high degree of conservation suggests a functional role for the loop. Mutational analysis and biochemical characterization of the conserved residues suggest that the loop participates in communication between the N-terminal portion of the helicase and the C-terminal catalytic domain. Since similar residues are also conserved in the eukaryotic MCM proteins, the data presented here suggest a similar coupling between the N-terminal and catalytic domain of the eukaryotic enzyme.

The promise and reality of therapeutic discovery from large cohorts.

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

SNPs3D: candidate gene and SNP selection for association studies.

BACKGROUND: The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level. DESCRIPTION: The resource http://www.SNPs3D.org has three primary modules. One module identifies which genes are candidates for involvement in a specified disease. A second module provides information about the relationships between sets of candidate genes. The third module analyzes the likely impact of non-synonymous SNPs on protein function. Disease/candidate gene relationships and gene-gene relationships are derived from the literature using simple but effective text profiling. SNP/protein function relationships are derived by two methods, one using principles of protein structure and stability, the other based on sequence conservation. Entries for each gene include a number of links to other data, such as expression profiles, pathway context, mouse knockout information and papers. Gene-gene interactions are presented in an interactive graphical interface, providing rapid access to the underlying information, as well as convenient navigation through the network. Use of the resource is illustrated with aspects of the inflammatory response and hypertension. CONCLUSION: The combination of SNP impact analysis, a knowledge based network of gene relationships and candidate genes, and access to a wide range of data and literature allow a user to quickly assimilate available information, and so develop models of gene-pathway-disease interaction.

Enhanced Antitumor Activity of an Anti-5T4 Antibody-Drug Conjugate in Combination with PI3K/mTOR inhibitors or Taxanes.

PURPOSE: Targeted treatment of solid or liquid tumors with antibody-drug conjugates (ADCs) can lead to promising clinical benefit. The aim of the study is to investigate combination regimens of auristatin-based ADCs in preclinical models of cancer. EXPERIMENTAL DESIGN: An auristatin-based anti-5T4 antibody conjugate (5T4-ADC) and auristatin payloads were combined with the dual PI3K/mTOR catalytic site inhibitor PF-05212384 (PF-384) or taxanes in a panel of tumor cell lines. Drug interactions in vitro were evaluated using cell viability assays, apoptosis induction, immunofluorescence, mitotic index, and immunoblotting. Breast cancer cells treated with auristatin analogue or 5T4-ADC were profiled by total- and phospho-proteomics. Antitumor efficacy of selected combinations was evaluated in 5T4-positive human breast or lung tumor xenografts in vivo. RESULTS: In vitro, auristatin-based agents displayed strong synergistic or additive activity when combined with PF-384 or taxanes, respectively. Further, treatment of 5T4-ADC plus PF-384 resulted in stronger induction of apoptosis and cell line-specific attenuation of pAKT and pGSK. Interestingly, proteomic analysis revealed unique effects of auristatins on multiple components of mRNA translation. Addition of PF-384 further amplified effects of 5T4-ADC on translational components, providing a potential mechanism of synergy between these drugs. In human tumor xenografts, dual targeting with 5T4-ADC/PF-384 or 5T4-ADC/paclitaxel produced substantially greater antitumor effects with longer average survival as compared with monotherapy treatments. CONCLUSIONS: Our results provide a biologic rationale for combining 5T4-ADC with either PI3K/mTOR pathway inhibitors or taxanes and suggest that mechanisms underlying the synergy may be attributed to cellular effects of the auristatin payload.

Tumor cells chronically treated with a trastuzumab-maytansinoid antibody-drug conjugate develop varied resistance mechanisms but respond to alternate treatments.

Antibody-drug conjugates (ADC) are emerging as clinically effective therapy. We hypothesized that cancers treated with ADCs would acquire resistance mechanisms unique to immunoconjugate therapy and that changing ADC components may overcome resistance. Breast cancer cell lines were exposed to multiple cycles of anti-Her2 trastuzumab-maytansinoid ADC (TM-ADC) at IC80 concentrations followed by recovery. The resistant cells, 361-TM and JIMT1-TM, were characterized by cytotoxicity, proteomic, transcriptional, and other profiling. Approximately 250-fold resistance to TM-ADC developed in 361-TM cells, and cross-resistance was observed to other non-cleavable-linked ADCs. Strikingly, these 361-TM cells retained sensitivity to ADCs containing cleavable mcValCitPABC-linked auristatins. In JIMT1-TM cells, 16-fold resistance to TM-ADC developed, with cross-resistance to other trastuzumab-ADCs. Both 361-TM and JIMT1-TM cells showed minimal resistance to unconjugated mertansine (DM1) and other chemotherapeutics. Proteomics and immunoblots detected increased ABCC1 (MRP1) drug efflux protein in 361-TM cells, and decreased Her2 (ErbB2) in JIMT1-TM cells. Proteomics also showed alterations in various pathways upon chronic exposure to the drug in both cell models. Tumors derived from 361-TM cells grew in mice and were refractory to TM-ADC compared with parental cells. Hence, acquired resistance to trastuzumab-maytansinoid ADC was generated in cultured cancer cells by chronic drug treatment, and either increased ABCC1 protein or reduced Her2 antigen were primary mediators of resistance. These ADC-resistant cell models retain sensitivity to other ADCs or standard-of-care chemotherapeutics, suggesting that alternate therapies may overcome acquired ADC resistance. Mol Cancer Ther; 14(4); 952-63. ©2015 AACR.

Evaluation of disorder predictions in CASP5.

This paper reports an analysis of the accuracy of predictions of structural disorder received as part of the CASP5 experiment. Six groups made predictions of disorder. The predictions of the four most active groups have been compared with the experimental results, in terms of the sensitivity and specificity of the methods. All four methods succeed in detecting over half the disordered residues in the targets, with a generally low rate of over-prediction. Two of the methods perform significantly better when the structure of a related protein is available. There is a trade-off between the fraction of disordered residues detected and the extent of over-prediction, and groups have adopted different compromises in this respect. Comparison of performance at the same over-prediction rates highlights the role of related structures in some methods rather than others, with different groups achieving the highest sensitivity for different target sets. Over-all, the methods are clearly of considerable use in identifying potential disorder.