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.

Supervised enhancer prediction with epigenetic pattern recognition and targeted validation.

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.

BACKGROUND: Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. METHODS: We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis. RESULTS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH). CONCLUSIONS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).

Increased enzyme binding to substrate is not necessary for more efficient cellulose hydrolysis.

Substrate binding is typically one of the rate-limiting steps preceding enzyme catalytic action during homogeneous reactions. However, interfacial-based enzyme catalysis on insoluble crystalline substrates, like cellulose, has additional bottlenecks of individual biopolymer chain decrystallization from the substrate interface followed by its processive depolymerization to soluble sugars. This additional decrystallization step has ramifications on the role of enzyme-substrate binding and its relationship to overall catalytic efficiency. We found that altering the crystalline structure of cellulose from its native allomorph I(ß) to III(I) results in 40-50% lower binding partition coefficient for fungal cellulases, but surprisingly, it enhanced hydrolytic activity on the latter allomorph. We developed a comprehensive kinetic model for processive cellulases acting on insoluble substrates to explain this anomalous finding. Our model predicts that a reduction in the effective binding affinity to the substrate coupled with an increase in the decrystallization procession rate of individual cellulose chains from the substrate surface into the enzyme active site can reproduce our anomalous experimental findings.

Membrane-mediated regulation of the intrinsically disordered CD3ϵ cytoplasmic tail of the TCR.

The regulation of T-cell-mediated immune responses depends on the phosphorylation of immunoreceptor tyrosine-based activation motifs (ITAMs) on T-cell receptors. Although many details of the signaling cascades are well understood, the initial mechanism and regulation of ITAM phosphorylation remains unknown. We used molecular dynamics simulations to study the influence of different compositions of lipid bilayers on the membrane association of the CD3ϵ cytoplasmic tails of the T-cell receptors. Our results show that binding of CD3ϵ to membranes is modulated by both the presence of negatively charged lipids and the lipid order of the membrane. Free-energy calculations reveal that the protein-membrane interaction is favored by the presence of nearby basic residues and the ITAM tyrosines. Phosphorylation minimizes membrane association, rendering the ITAM motif more accessible to binding partners. In systems mimicking biological membranes, the CD3ϵ chain localization is modulated by different facilitator lipids (e.g., gangliosides or phosphoinositols), revealing a plausible regulatory effect on activation through the regulation of lipid composition in cell membranes.

Viral escape from neutralizing antibodies in early subtype A HIV-1 infection drives an increase in autologous neutralization breadth.

Antibodies that neutralize (nAbs) genetically diverse HIV-1 strains have been recovered from a subset of HIV-1 infected subjects during chronic infection. Exact mechanisms that expand the otherwise narrow neutralization capacity observed during early infection are, however, currently undefined. Here we characterized the earliest nAb responses in a subtype A HIV-1 infected Rwandan seroconverter who later developed moderate cross-clade nAb breadth, using (i) envelope (Env) glycoproteins from the transmitted/founder virus and twenty longitudinal nAb escape variants, (ii) longitudinal autologous plasma, and (iii) autologous monoclonal antibodies (mAbs). Initially, nAbs targeted a single region of gp120, which flanked the V3 domain and involved the alpha2 helix. A single amino acid change at one of three positions in this region conferred early escape. One immunoglobulin heavy chain and two light chains recovered from autologous B cells comprised two mAbs, 19.3H-L1 and 19.3H-L3, which neutralized the founder Env along with one or three of the early escape variants carrying these mutations, respectively. Neither mAb neutralized later nAb escape or heterologous Envs. Crystal structures of the antigen-binding fragments (Fabs) revealed flat epitope contact surfaces, where minimal light chain mutation in 19.3H-L3 allowed for additional antigenic interactions. Resistance to mAb neutralization arose in later Envs through alteration of two glycans spatially adjacent to the initial escape signatures. The cross-neutralizing nAbs that ultimately developed failed to target any of the defined V3-proximal changes generated during the first year of infection in this subject. Our data demonstrate that this subject's first recognized nAb epitope elicited strain-specific mAbs, which incrementally acquired autologous breadth, and directed later B cell responses to target distinct portions of Env. This immune re-focusing could have triggered the evolution of cross-clade antibodies and suggests that exposure to a specific sequence of immune escape variants might promote broad humoral responses during HIV-1 infection.

Binding and movement of individual Cel7A cellobiohydrolases on crystalline cellulose surfaces revealed by single-molecule fluorescence imaging.

The efficient catalytic conversion of biomass to bioenergy would meet a large portion of energy requirements in the near future. A crucial step in this process is the enzyme-catalyzed hydrolysis of cellulose to glucose that is then converted into fuel such as ethanol by fermentation. Here we use single-molecule fluorescence imaging to directly monitor the movement of individual Cel7A cellobiohydrolases from Trichoderma reesei (TrCel7A) on the surface of insoluble cellulose fibrils to elucidate molecular level details of cellulase activity. The motion of multiple, individual TrCel7A cellobiohydrolases was simultaneously recorded with ∼15-nm spatial resolution. Time-resolved localization microscopy provides insights on the activity of TrCel7A on cellulose and informs on nonproductive binding and diffusion. We measured single-molecule residency time distributions of TrCel7A bound to cellulose both in the presence of and absence of cellobiose the major product and a potent inhibitor of Cel7A activity. Combining these results with a kinetic model of TrCel7A binding provides microscopic insight into interactions between TrCel7A and the cellulose substrate.

A mechanistic understanding of allosteric immune escape pathways in the HIV-1 envelope glycoprotein.

The HIV-1 envelope (Env) spike, which consists of a compact, heterodimeric trimer of the glycoproteins gp120 and gp41, is the target of neutralizing antibodies. However, the high mutation rate of HIV-1 and plasticity of Env facilitates viral evasion from neutralizing antibodies through various mechanisms. Mutations that are distant from the antibody binding site can lead to escape, probably by changing the conformation or dynamics of Env; however, these changes are difficult to identify and define mechanistically. Here we describe a network analysis-based approach to identify potential allosteric immune evasion mechanisms using three known HIV-1 Env gp120 protein structures from two different clades, B and C. First, correlation and principal component analyses of molecular dynamics (MD) simulations identified a high degree of long-distance coupled motions that exist between functionally distant regions within the intrinsic dynamics of the gp120 core, supporting the presence of long-distance communication in the protein. Then, by integrating MD simulations with network theory, we identified the optimal and suboptimal communication pathways and modules within the gp120 core. The results unveil both strain-dependent and -independent characteristics of the communication pathways in gp120. We show that within the context of three structurally homologous gp120 cores, the optimal pathway for communication is sequence sensitive, i.e. a suboptimal pathway in one strain becomes the optimal pathway in another strain. Yet the identification of conserved elements within these communication pathways, termed inter-modular hotspots, could present a new opportunity for immunogen design, as this could be an additional mechanism that HIV-1 uses to shield vulnerable antibody targets in Env that induce neutralizing antibody breadth.

Taste of sugar at the membrane: thermodynamics and kinetics of the interaction of a disaccharide with lipid bilayers.

Sugar recognition at the membrane is critical in various physiological processes. Many aspects of sugar-membrane interaction are still unknown. We take an integrated approach by combining conventional molecular-dynamics simulations with enhanced sampling methods and analytical models to understand the thermodynamics and kinetics of a di-mannose molecule in a phospholipid bilayer system. We observe that di-mannose has a slight preference to localize at the water-phospholipid interface. Using umbrella sampling, we show the free energy bias for this preferred location to be just -0.42 kcal/mol, which explains the coexistence of attraction and exclusion mechanisms of sugar-membrane interaction. Accurate estimation of absolute entropy change of water molecules with a two-phase model indicates that the small energy bias is the result of a favorable entropy change of water molecules. Then, we incorporate results from molecular-dynamics simulation in two different ways to an analytical diffusion-reaction model to obtain association and dissociation constants for di-mannose interaction with membrane. Finally, we verify our approach by predicting concentration dependence of di-mannose recognition at the membrane that is consistent with experiment. In conclusion, we provide a combined approach for the thermodynamics and kinetics of a weak ligand-binding system, which has broad implications across many different fields.

Deducing conformational variability of intrinsically disordered proteins from infrared spectroscopy with Bayesian statistics.

As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts.

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.

Identification of minimally interacting modules in an intrinsically disordered protein.

The conformational characterization of intrinsically disordered proteins (IDPs) is complicated by their conformational heterogeneity and flexibility. If an IDP could somehow be divided into smaller fragments and reconstructed later, theoretical and spectroscopic studies could probe its conformational variability in detail. Here, we used replica molecular-dynamics simulations and network theory to explore whether such a divide-and-conquer strategy is feasible for α-synuclein, a prototypical IDP. We characterized the conformational variability of α-synuclein by conducting >100 unbiased all-atom molecular-dynamics simulations, for a total of >10 µs of trajectories. In these simulations, α-synuclein formed a heterogeneous ensemble of collapsed coil states in an aqueous environment. These states were stabilized by heterogeneous contacts between sequentially distant regions. We find that α-synuclein contains residual secondary structures in the collapsed states, and the heterogeneity in the collapsed state makes it feasible to split α-synuclein into sequentially contiguous minimally interacting fragments. This study reveals previously unknown characteristics of α-synuclein and provides a new (to our knowledge) approach for studying other IDPs.

The B cell response is redundant and highly focused on V1V2 during early subtype C infection in a Zambian seroconverter.

High-titer autologous neutralizing antibody responses have been demonstrated during early subtype C human immunodeficiency virus type 1 (HIV-1) infection. However, characterization of this response against autologous virus at the monoclonal antibody (MAb) level has only recently begun to be elucidated. Here we describe five monoclonal antibodies derived from a subtype C-infected seroconverter and their neutralizing activities against pseudoviruses that carry envelope glycoproteins from 48 days (0 month), 2 months, and 8 months after the estimated time of infection. Sequence analysis indicated that the MAbs arose from three distinct B cell clones, and their pattern of neutralization compared to that in patient plasma suggested that they circulated between 2 and 8 months after infection. Neutralization by MAbs representative of each B cell clone was mapped to two residues: position 134 in V1 and position 189 in V2. Mutational analysis revealed cooperative effects between glycans and residues at these two positions, arguing that they contribute to a single epitope. Analysis of the cognate gp120 sequence through homology modeling places this potential epitope near the interface between the V1 and V2 loops. Additionally, the escape mutation R189S in V2, which conferred resistance against all three MAbs, had no detrimental effect on virus replication in vitro. Taken together, our data demonstrate that independent B cells repeatedly targeted a single structure in V1V2 during early infection. Despite this assault, a single amino acid change was sufficient to confer complete escape with minimal impact on replication fitness.

Quantifying intramolecular binding in multivalent interactions: a structure-based synergistic study on Grb2-Sos1 complex.

Numerous signaling proteins use multivalent binding to increase the specificity and affinity of their interactions within the cell. Enhancement arises because the effective binding constant for multivalent binding is larger than the binding constants for each individual interaction. We seek to gain both qualitative and quantitative understanding of the multivalent interactions of an adaptor protein, growth factor receptor bound protein-2 (Grb2), containing two SH3 domains interacting with the nucleotide exchange factor son-of-sevenless 1 (Sos1) containing multiple polyproline motifs separated by flexible unstructured regions. Grb2 mediates the recruitment of Sos1 from the cytosol to the plasma membrane where it activates Ras by inducing the exchange of GDP for GTP. First, using a combination of evolutionary information and binding energy calculations, we predict an additional polyproline motif in Sos1 that binds to the SH3 domains of Grb2. This gives rise to a total of five polyproline motifs in Sos1 that are capable of binding to the two SH3 domains of Grb2. Then, using a hybrid method combining molecular dynamics simulations and polymer models, we estimate the enhancement in local concentration of a polyproline motif on Sos1 near an unbound SH3 domain of Grb2 when its other SH3 domain is bound to a different polyproline motif on Sos1. We show that the local concentration of the Sos1 motifs that a Grb2 SH3 domain experiences is approximately 1000 times greater than the cellular concentration of Sos1. Finally, we calculate the intramolecular equilibrium constants for the crosslinking of Grb2 on Sos1 and use thermodynamic modeling to calculate the stoichiometry. With these equilibrium constants, we are able to predict the distribution of complexes that form at physiological concentrations. We believe this is the first systematic analysis that combines sequence, structure, and thermodynamic analyses to determine the stoichiometry of the complexes that are dominant in the cellular environment.

Dynamical networks in tRNA:protein complexes.

Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNA(Glu) and an archaeal leucyl-tRNA synthetase (LeuRS):tRNA(Leu) complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.

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.

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.

Genetic signatures in the envelope glycoproteins of HIV-1 that associate with broadly neutralizing antibodies.

A steady increase in knowledge of the molecular and antigenic structure of the gp120 and gp41 HIV-1 envelope glycoproteins (Env) is yielding important new insights for vaccine design, but it has been difficult to translate this information to an immunogen that elicits broadly neutralizing antibodies. To help bridge this gap, we used phylogenetically corrected statistical methods to identify amino acid signature patterns in Envs derived from people who have made potently neutralizing antibodies, with the hypothesis that these Envs may share common features that would be useful for incorporation in a vaccine immunogen. Before attempting this, essentially as a control, we explored the utility of our computational methods for defining signatures of complex neutralization phenotypes by analyzing Env sequences from 251 clonal viruses that were differentially sensitive to neutralization by the well-characterized gp120-specific monoclonal antibody, b12. We identified ten b12-neutralization signatures, including seven either in the b12-binding surface of gp120 or in the V2 region of gp120 that have been previously shown to impact b12 sensitivity. A simple algorithm based on the b12 signature pattern was predictive of b12 sensitivity/resistance in an additional blinded panel of 57 viruses. Upon obtaining these reassuring outcomes, we went on to apply these same computational methods to define signature patterns in Env from HIV-1 infected individuals who had potent, broadly neutralizing responses. We analyzed a checkerboard-style neutralization dataset with sera from 69 HIV-1-infected individuals tested against a panel of 25 different Envs. Distinct clusters of sera with high and low neutralization potencies were identified. Six signature positions in Env sequences obtained from the 69 samples were found to be strongly associated with either the high or low potency responses. Five sites were in the CD4-induced coreceptor binding site of gp120, suggesting an important role for this region in the elicitation of broadly neutralizing antibody responses against HIV-1.

Molecular signatures of ribosomal evolution.

Ribosomal signatures, idiosyncrasies in the ribosomal RNA (rRNA) and/or proteins, are characteristic of the individual domains of life. As such, insight into the early evolution of the domains can be gained from a comparative analysis of their respective signatures in the translational apparatus. In this work, we identify signatures in both the sequence and structure of the rRNA and analyze their contributions to the universal phylogenetic tree using both sequence- and structure-based methods. Domain-specific ribosomal proteins can be considered signatures in their own right. Although it is commonly assumed that they developed after the universal ribosomal proteins, we present evidence that at least one may have been present before the divergence of the organismal lineages. We find correlations between the rRNA signatures and signatures in the ribosomal proteins showing that the rRNA signatures coevolved with both domain-specific and universal ribosomal proteins. Finally, we show that the genomic organization of the universal ribosomal components contains these signatures as well. From these studies, we propose the ribosomal signatures are remnants of an evolutionary-phase transition that occurred as the cell lineages began to coalesce and so should be reflected in corresponding signatures throughout the fabric of the cell and its genome.

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.