RESUMO
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.
Assuntos
Bancos de Espécimes Biológicos , Proteínas Sanguíneas , Bases de Dados Factuais , Genômica , Saúde , Proteoma , Proteômica , Humanos , Sistema ABO de Grupos Sanguíneos/genética , Proteínas Sanguíneas/análise , Proteínas Sanguíneas/genética , COVID-19/genética , Descoberta de Drogas , Epistasia Genética , Fucosiltransferases/metabolismo , Predisposição Genética para Doença , Plasma/química , Pró-Proteína Convertase 9/metabolismo , Proteoma/análise , Proteoma/genética , Parcerias Público-Privadas , Locos de Características Quantitativas , Reino UnidoRESUMO
The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery. Recognizing the need for large-scale human genetics data, as well as the unique value of the data access and contribution terms of the UKB, the UKB-ESC was formed. As a result, exome data from 200,643 UKB enrollees are now available. These data include ~10 million exonic variants-a rich resource of rare coding variation that is particularly valuable for drug discovery. The UKB-ESC precompetitive collaboration has further strengthened academic and industry ties and has provided teams with an opportunity to interact with and learn from the wider research community.
Assuntos
Bancos de Espécimes Biológicos , Descoberta de Drogas , Sequenciamento do Exoma , Genética Humana , Pesquisa , Descoberta de Drogas/métodos , Genômica/métodos , Humanos , Reino UnidoRESUMO
BACKGROUND: In recent years, there has been a growing interest in automating small-molecule bioanalytical sample preparations specifically using the Hamilton MicroLab(®) STAR liquid-handling platform. In the most extensive work reported thus far, multiple small-molecule sample preparation assay types (protein precipitation extraction, SPE and liquid-liquid extraction) have been integrated into a suite that is composed of graphical user interfaces and Hamilton scripts. Using that suite, bioanalytical scientists have been able to automate various sample preparation methods to a great extent. However, there are still areas that could benefit from further automation, specifically, the full integration of analytical standard and QC sample preparation with study sample extraction in one continuous run, real-time 2D barcode scanning on the Hamilton deck and direct Laboratory Information Management System database connectivity. RESULTS: We developed a new small-molecule sample-preparation automation system that improves in all of the aforementioned areas. CONCLUSION: The improved system presented herein further streamlines the bioanalytical workflow, simplifies batch run design, reduces analyst intervention and eliminates sample-handling error.
