Julia Carrasco-Zanini

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.

Abstract Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques—the aptamer-based SomaScan ® v4 assay and the antibody-based Olink assays—to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein–phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer’s disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries.

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Much has been learned about the voltage sensors of ion channels since the x-ray structure of the mammalian voltage-gated potassium channel Kv1.2 was published in 2005. High resolution structural data of a Kv channel enabled the structural interpretation of numerous electrophysiological findings collected in various ion channels, most notably Shaker, and permitted the development of meticulous computational simulations of the activation mechanism. The fundamental premise for the structural interpretation of functional measurements from Shaker is that this channel and Kv1.2 have the same characteristics, such that correlation of data from both channels would be a trivial task. We tested these assumptions by measuring Kv1.2 voltage-dependent gating and charge per channel. We found that the Kv1.2 gating charge is near 10 elementary charges (eo), ∼25% less than the well-established 13-14 eo in Shaker. Next, we neutralized positive residues in the Kv1.2 S4 transmembrane segment to investigate the cause of the reduction of the gating charge and found that, whereas replacing R1 with glutamine decreased voltage sensitivity to ∼50% of the wild-type channel value, mutation of the subsequent arginines had a much smaller effect. These data are in marked contrast to the effects of charge neutralization in Shaker, where removal of the first four basic residues reduces the gating charge by roughly the same amount. In light of these differences, we propose that the voltage-sensing domains (VSDs) of Kv1.2 and Shaker might undergo the same physical movement, but the septum that separates the aqueous crevices in the VSD of Kv1.2 might be thicker than Shaker's, accounting for the smaller Kv1.2 gating charge.