Identification of genetic variants influencing the human plasma proteome

Plasma proteome variation and its genetic determinants in children and adolescents

Our current understanding of the determinants of plasma proteome variation during pediatric development remains incomplete. Here, we show that genetic variants, age, sex and body mass index significantly influence this variation. Using a streamlined and highly quantitative mass spectrometry-based proteomics workflow, we analyzed plasma from 2,147 children and adolescents, identifying 1,216 proteins after quality control. Notably, the levels of 70% of these were associated with at least one of the aforementioned factors, with protein levels also being predictive. Quantitative trait loci (QTLs) regulated at least one-third of the proteins; between a few percent and up to 30-fold. Together with excellent replication in an additional 1,000 children and 558 adults, this reveals substantial genetic effects on plasma protein levels, persisting from childhood into adulthood. Through Mendelian randomization and colocalization analyses, we identified 41 causal genes for 33 cardiometabolic traits, emphasizing the value of protein QTLs in drug target identification and disease understanding.

Population Proteomics: A Tool to Gain Insights Into the Inflamed Periodontium

Periodontitis, characterized by inflammatory loss of tooth-supporting tissues associated with biofilm, is among the most prevalent chronic diseases globally, affecting approximately 50% of the adult population to a moderate extent and cases of severe periodontitis surpassing the one billion mark. Proteomics analyses of blood, serum, and oral fluids have provided valuable insights into the complex processes occurring in the inflamed periodontium. However, until now, proteome analyses have been primarily limited to small groups of diseased versus healthy individuals. The emergence of population-scale analysis of proteomic data offers opportunities to uncover disease-associated pathways, identify potential drug targets, and discover biomarkers. In this review, we will explore the applications of proteomics in population-based studies and discuss the advancements it brings to our understanding of periodontal inflammation. Additionally, we highlight the challenges posed by currently available data and offer perspectives for future applications in periodontal research. This review aims to explain the ongoing efforts in leveraging proteomics for elucidating the complexities of periodontal diseases and paving the way for clinical strategies.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Comparing PCR With High-resolution Melting Analysis for Apolipoprotein E Genotyping in Alzheimer's: A Case-control Study

Introduction

The apolipoprotein E (APOE) genotype has a heterogeneous distribution throughout the world. The present study aimed to characterize the APOE genotype (rs429358, rs7412) in healthy individuals compared with Alzheimer cases in Kerman, southeastern Iran, by two standard mutation scanning methods.

Methods

In this case-control study, 90 Alzheimer patients as a case group and 90 healthy individuals as a control group were examined. APOE genotyping was carried out using high-resolution melting (HRM) analysis assay and multiplex tetra-primer amplification-refractory mutation system polymerase chain reaction (T-ARMS PCR) techniques.

Results

In contrast to Multiplex T-ARMS PCR, HRM analysis was not efficient in rs7412 genotyping. The results of multiplex T-ARMS showed that ɛ2ɛ3 genotype (P=0.006, odd ratio [OR]=0.119) and ɛ2 allele (P=0.004, OR=0.129) were more prevalent in the control group compared with the case ones, whereas ɛ4 allele was associated with borderline risk of Alzheimer disease (P=0.099, OR=1.76).

Conclusion

We concluded that Multiplex T-ARMS PCR could be considered as a better option than HRM analysis for APOE genotyping in terms of speed, accuracy, simplicity, and cheapness in large-scale use. Also, the present study revealed that ɛ2 ɛ3 genotype and ɛ2 allele are protective against Alzheimer whereas the ɛ4 allele cannot be strongly considered as Alzheimer genetic risk factor in Kerman, Iran. The results may help to choose a better technique for APOE genotyping.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility

Identification of protein quantitative trait loci (pQTL) helps understand the underlying mechanisms of diseases and discover promising targets for pharmacological intervention. For most important class of drug targets, genetic evidence needs to be generalizable to diverse populations. Given that the majority of the previous studies were conducted in European ancestry populations, little is known about the protein-associated genetic variants in East Asians. Based on data-independent acquisition mass spectrometry technique, we conduct genome-wide association analyses for 304 unique proteins in 2,958 Han Chinese participants. We identify 195 genetic variant-protein associations. Colocalization and Mendelian randomization analyses highlight 60 gene-protein-phenotype associations, 45 of which (75%) have not been prioritized in Europeans previously. Further cross-ancestry analyses uncover key proteins that contributed to the differences in the obesity-induced diabetes and coronary artery disease susceptibility. These findings provide novel druggable proteins as well as a unique resource for the trans-ancestry evaluation of protein-targeted drug discovery.

Plasma protein levels of young healthy pigs as indicators of disease resilience

Selection for disease resilience, which refers to the ability of an animal to maintain performance when exposed to disease, can reduce the impact of infectious diseases. However, direct selection for disease resilience is challenging because nucleus herds must maintain a high health status. A possible solution is indirect selection of indicators of disease resilience. To search for such indicators, we conducted phenotypic and genetic quantitative analyses of the abundances of 377 proteins in plasma samples from 912 young and visually healthy pigs and their relationships with performance and subsequent disease resilience after natural exposure to a polymicrobial disease challenge. Abundances of 100 proteins were significantly heritable (false discovery rate (FDR) <0.10). The abundance of some proteins was or tended to be genetically correlated (rg) with disease resilience, including complement system proteins (rg = -0.24, FDR = 0.001) and IgG heavy chain proteins (rg = -0.68, FDR = 0.22). Gene set enrichment analyses (FDR < 0.2) based on phenotypic and genetic associations of protein abundances with subsequent disease resilience revealed many pathways related to the immune system that were unfavorably associated with subsequent disease resilience, especially the innate immune system. It was not possible to determine whether the observed levels of these proteins reflected baseline levels in these young and visually healthy pigs or were the result of a response to environmental disturbances that the pigs were exposed to before sample collection. Nevertheless, results show that, under these conditions, the abundance of proteins in some immune-related pathways can be used as phenotypic and genetic predictors of disease resilience and have the potential for use in pig breeding and management.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics

Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a “gem” at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.

Extensive heterogeneity of glycopeptides in plasma revealed by deep glycoproteomic analysis using size-exclusion chromatography

Several plasma glycoproteins are clinically useful as biomarkers in a variety of diseases. Although thousands of proteins are present in plasma, >95% of the plasma proteome by mass is represented by only 22 proteins. This necessitates strategies to deplete the abundant proteins and enrich other subsets of proteins. Although glycoproteins are abundant in plasma, in routine proteomic analyses, glycopeptides are not often investigated. Traditional methods such as lectin-based enrichment of glycopeptides followed by deglycosylation have helped understand the glycoproteome, but they lack any information about the attached glycans. Here, we apply size-exclusion chromatography (SEC) as a simple strategy to enrich intact N-glycopeptides based on their larger size which achieves broad selectivity regardless of the nature of attached glycans. Using this approach, we identified 1317 N-glycopeptides derived from 266 glycosylation sites on 154 plasma glycoproteins. The deep coverage achieved by this approach was evidenced by extensive heterogeneity that was observed. For instance, 20-100 glycopeptides were observed per protein for the 15 most-glycosylated glycoproteins. Notably, we discovered 615 novel glycopeptides of which 39 glycosylation sites (from 38 glycoproteins) were not included in protein databases such as Uniprot and GlyConnectDB. Finally, we also identified 12 novel glycopeptides containing di-sialic acid, which is a rare glycan epitope. Our results demonstrate the utility of SEC for efficient LC-MS/MS-based deep glycoproteomics analysis of human plasma. Overall, the SEC-based method described here is a simple, rapid and high-throughput strategy for characterization of any glycoproteome.

Plasma pooling in combination with amotosalen/UVA pathogen inactivation to increase standardisation and safety of therapeutic plasma units

Objectives

Assessment of the impact of pooling five single‐donor plasma (SDP) units to obtain six pathogen‐reduced therapeutic plasma (PTP) units on standardisation and the retention of labile coagulation factors.

Background

SDP shows a high inter‐donor variability with potential implications for the clinical treatment outcome. Additionally, there is still an existing risk for window‐period transmissions of blood borne pathogens including newly emerging pathogens.

Methods/Materials

Five ABO‐identical SDP units were pooled, treated with the INTERTCEPT™ Blood System (Cerus Corporation, U.S.A.) and split into six PTP units which were frozen and thawed after 30 days. The variability in volume, labile coagulation factor retention and activity was assessed.

Results

The variability of volumes between the PTP units was reduced by 46% compared to SDP units. The variability in coagulation factor content between the PTP units was reduced by 63% compared to SDP units. Moderate, but significant losses of coagulation factors (except for vWF) were observed in PTPs compared to SDPs.

Conclusion

The pooling of five SDP units to obtain six PTP units significantly increases product standardisation with potential implications for safety, economics as well as transfusion‐transmitted pathogen safety, making it an interesting alternative to quarantine SDP (qSDP) and pathogen‐reduced SDP.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Linking molecular targets of Cd in the bloodstream to organ-based adverse health effects

The chronic exposure of human populations to toxic metals remains a global public health concern. Although chronic Cd exposure is linked to kidney damage, osteoporosis and cancer, the underlying biomolecular mechanisms remain incompletely understood. Since other diseases could also be causally linked to chronic Cd exposure, a systems toxicology-based approach is needed to gain new insight into the underlying exposure-disease relationship. This approach requires one to integrate the cascade of dynamic bioinorganic chemistry events that unfold in the bloodstream after Cd enters with toxicological events that unfold in target organs over time. To this end, we have conducted a systematic literature search to identify all molecular targets of Cd in plasma and in red blood cells (RBCs). Based on this information it is impossible to describe the metabolism of Cd and the toxicological relevance of it binding to molecular targets in/on RBCs is elusive. Perhaps most importantly, the role that peptides, amino acids and inorganic ions, including HCO₃^-, Cl^- and HSeO₃^- play in terms of mediating the translocation of Cd to target organs and its detoxification is poorly understood. Causally linking human exposure to this metal with diseases requires a much better integration of the bioinorganic chemistry of Cd that unfolds in the bloodstream with target organs. This from a public health point of view important goal will require collaborations between scientists from different disciplines to untangle the complex mechanisms which causally link Cd exposure to disease.

NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Population-scale proteome variation in human induced pluripotent stem cells

Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.

Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort

BACKGROUND

The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life.

METHODS

We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed.

RESULTS

The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 × 10-11) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections.

CONCLUSIONS

We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.

Pulmonary hypertension: Proteins in the blood

The plasma proteome is rich in information. It comprises proteins that are secreted or lost from cells as they respond to their local environment. Changes in the constitution of the plasma proteome offer a relatively non-invasive report on the health of tissues. This is particularly true of the lung in pulmonary hypertension, given the large surface area of the pulmonary vasculature in direct communication with blood. So far, this is relatively untapped; we have relied on proteins released from the heart, specifically brain natriuretic peptide and troponin, to inform clinical management. New technology allows the measurement of a larger number of proteins that cover a broad range of molecular pathways in a single small aliquot. The emerging data will yield more than just new biomarkers of pulmonary hypertension for clinical use. Integrated with genomics and with the help of new bioinformatic tools, the plasma proteome can provide insight into the causative drivers of pulmonary vascular disease and guide drug development.

QTL Analysis Beyond eQTLs

Expression quantitative trait locus (eQTL) analysis is a powerful method to understand the association between genetic variant and gene expression; it also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene expression. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.

Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers

Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.

Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry

BACKGROUND

Identifying genetic variation associated with plasma protein levels, and the mechanisms by which they act, could provide insight into alterable processes involved in regulation of protein levels. Although protein levels can be affected by genetic variants, their estimation can also be biased by missense variants in coding exons causing technical artifacts. Integrating genome sequence genotype data with mass spectrometry-based protein level estimation could reduce bias, thereby improving detection of variation that affects RNA or protein metabolism.

METHODS

Here, we integrate the blood plasma protein levels of 664 proteins from 165 participants of the Tromsø Study, measured via tandem mass tag mass spectrometry, with whole-exome sequencing data to identify common and rare genetic variation associated with peptide and protein levels (protein quantitative trait loci [pQTLs]). We additionally use literature and database searches to prioritize putative functional variants for each pQTL.

RESULTS

We identify 109 independent associations (36 protein and 73 peptide) and use genotype data to exclude 49 (4 protein and 45 peptide) as technical artifacts. We describe 2 particular cases of rare variation: 1 associated with the complement pathway and 1 with platelet degranulation. We identify putative functional variants and show that pQTLs act through diverse molecular mechanisms that affect both RNA and protein metabolism.

CONCLUSIONS

We show that although the majority of pQTLs exert their effects by modulating RNA metabolism, many affect protein levels directly. Our work demonstrates the extent by which pQTL studies are affected by technical artifacts and highlights how prioritizing the functional variant in pQTL studies can lead to insights into the molecular steps by which a protein may be regulated.

In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta

Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy.

Blood cells travel through the blood vessels in a soupy mixture of proteins called plasma. Most of these proteins are plasma-specific, yet small amounts of proteins can leak into the plasma from other body parts and may provide hints about what is going on elsewhere in the body. This could allow doctors to use plasma samples to assess health or detect disease. But so far developing methods to detect these leaked proteins has proved difficult.

Plasma passing through the placenta can transfer proteins between a pregnant woman and her baby. Learning more about these protein exchanges may help scientists understand how the mother and baby adapt to each other and what triggers child birth. But, so far, they have been hard to study. Using DNA to help trace the origins of proteins found in mother or baby could make it easier.

Now, Pernemalm et al. have used DNA sequencing in combination with protein analysis to identify proteins passed between two pregnant mothers and their babies. Comparing the genetic sequences of each mother and child made it possible to trace the origin of the proteins. For example, if a mother had a version of the protein that matched genes the child inherited from its father, they knew it passed from the baby to the mother. This approach found 24 proteins in plasma from two pregnant mothers that had likely passed through the placenta during pregnancy. Pernemalm et al. also analyzed the plasma of 30 healthy individuals and confirmed that it contained several proteins that had likely leaked from other organs, including the lungs and pancreas.

Monitoring protein transfer between pregnant mother and baby may help scientists identify what triggers normal or premature deliveries. One advantage of the technique developed Pernemalm et al. is that it can analyze plasma proteins from large numbers of people, which could enable larger studies. More refinement of the technique may also allow scientists to identify leaked proteins in the plasma that provide an early warning of cancer or other diseases.

Characterization of Proteome Variation During Modern Maize Breeding

The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.

Genetic variants influencing phenotypic variance heterogeneity

Most genetic studies identify genetic variants associated with disease risk or with the mean value of a quantitative trait. More rarely, genetic variants associated with variance heterogeneity are considered. In this study, we have identified such variance single-nucleotide polymorphisms (vSNPs) and examined if these represent biological gene × gene or gene × environment interactions or statistical artifacts caused by multiple linked genetic variants influencing the same phenotype. We have performed a genome-wide study, to identify vSNPs associated with variance heterogeneity in DNA methylation levels. Genotype data from over 10 million single-nucleotide polymorphisms (SNPs), and DNA methylation levels at over 430 000 CpG sites, were analyzed in 729 individuals. We identified vSNPs for 7195 CpG sites (P < 9.4 × 10-11). This is a relatively low number compared to 52 335 CpG sites for which SNPs were associated with mean DNA methylation levels. We further showed that variance heterogeneity between genotypes mainly represents additional, often rare, SNPs in linkage disequilibrium (LD) with the respective vSNP and for some vSNPs, multiple low frequency variants co-segregating with one of the vSNP alleles. Therefore, our results suggest that variance heterogeneity of DNA methylation mainly represents phenotypic effects by multiple SNPs, rather than biological interactions. Such effects may also be important for interpreting variance heterogeneity of more complex clinical phenotypes.

Co-regulatory networks of human serum proteins link genetics to disease

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.

Alzheimer disease pathology and the cerebrospinal fluid proteome

BACKGROUND

Altered proteome profiles have been reported in both postmortem brain tissues and body fluids of subjects with Alzheimer disease (AD), but their broad relationships with AD pathology, amyloid pathology, and tau-related neurodegeneration have not yet been fully explored. Using a robust automated MS-based proteomic biomarker discovery workflow, we measured cerebrospinal fluid (CSF) proteomes to explore their association with well-established markers of core AD pathology.

METHODS

Cross-sectional analysis was performed on CSF collected from 120 older community-dwelling adults with normal (n = 48) or impaired cognition (n = 72). LC-MS quantified hundreds of proteins in the CSF. CSF concentrations of β-amyloid 1-42 (Aβ1-42), tau, and tau phosphorylated at threonine 181 (P-tau181) were determined with immunoassays. First, we explored proteins relevant to biomarker-defined AD. Then, correlation analysis of CSF proteins with CSF markers of amyloid pathology, neuronal injury, and tau hyperphosphorylation (i.e., Aβ1-42, tau, P-tau181) was performed using Pearson's correlation coefficient and Bonferroni correction for multiple comparisons.

RESULTS

We quantified 790 proteins in CSF samples with MS. Four CSF proteins showed an association with CSF Aβ1-42 levels (p value ≤ 0.05 with correlation coefficient (R) ≥ 0.38). We identified 50 additional CSF proteins associated with CSF tau and 46 proteins associated with CSF P-tau181 (p value ≤ 0.05 with R ≥ 0.37). The majority of those proteins that showed such associations were brain-enriched proteins. Gene Ontology annotation revealed an enrichment for synaptic proteins and proteins originating from reelin-producing cells and the myelin sheath.

CONCLUSIONS

We used an MS-based proteomic workflow to profile the CSF proteome in relation to cerebral AD pathology. We report strong evidence of previously reported CSF proteins and several novel CSF proteins specifically associated with amyloid pathology or neuronal injury and tau hyperphosphorylation.

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3–36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data.

Systemic and specific effects of antihypertensive and lipid-lowering medication on plasma protein biomarkers for cardiovascular diseases

A large fraction of the adult population is on lifelong medication for cardiovascular disorders, but the metabolic consequences are largely unknown. This study determines the effects of common anti-hypertensive and lipid lowering drugs on circulating plasma protein biomarkers. We studied 425 proteins in plasma together with anthropometric and lifestyle variables, and the genetic profile in a cross-sectional cohort. We found 8406 covariate-protein associations, and a two-stage GWAS identified 17253 SNPs to be associated with 109 proteins. By computationally removing variation due to lifestyle and genetic factors, we could determine that medication, per se, affected the abundance levels of 35.7% of the plasma proteins. Medication either affected a single, a few, or a large number of protein, and were found to have a negative or positive influence on known disease pathways and biomarkers. Anti-hypertensive or lipid lowering drugs affected 33.1% of the proteins. Angiotensin-converting enzyme inhibitors showed the strongest lowering effect by decreasing plasma levels of myostatin. Cell-culture experiments showed that angiotensin-converting enzyme inhibitors reducted myostatin RNA levels. Thus, understanding the effects of lifelong medication on the plasma proteome is important both for sharpening the diagnostic precision of protein biomarkers and in disease management.

Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms

Inherited genetic variation affects local gene expression and DNA methylation in humans. Most expression quantitative trait loci (cis-eQTLs) occur at the same genomic location as a methylation QTL (cis-meQTL), suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, here we use co-localization methods to identify eQTL-meQTL pairs likely to share a causal variant. We use partial correlation and mediation analyses to identify >400 of these pairs showing evidence of a causal relationship between expression and methylation (i.e., shared mechanism) with many additional pairs we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite associations with expression and methylation, although many SNPs affect multiple CpGs in opposite directions. This work demonstrates the pervasiveness of co-regulated expression and methylation in the human genome. Applying this approach to other types of molecular QTLs can enhance our understanding of regulatory mechanisms.

Epigenome-wide association study reveals differential DNA methylation in individuals with a history of myocardial infarction

Cardiovascular diseases (CVDs) are the leading causes of death worldwide and represent a substantial economic burden on public health care systems. Epigenetic markers have potential as diagnostic markers before clinical symptoms have emerged, and as prognostic markers to inform the choice of clinical intervention. In this study, we performed an epigenome-wide association study (EWAS) for CVDs, to identify disease-specific alterations in DNA methylation. CpG methylation in blood samples from the northern Sweden population health study (NSPHS) (n = 729) was assayed on the Illumina Infinium HumanMethylation450 BeadChip. Individuals with a history of a CVD were identified in the cohort. It included individuals with hypertension (N = 147), myocardial infarction (MI) (N = 48), stroke (N = 27), thrombosis (N = 22) and cardiac arrhythmia (N = 5). Differential DNA methylation was observed at 211 CpG-sites in individuals with a history of MI (q <0.05). These sites represent 196 genes, of which 42 have been described in the scientific literature to be related to cardiac function, cardiovascular disease, cardiogenesis and recovery after ischemic injury. We have shown that individuals with a history of MI have a deviating pattern of DNA methylation at many genomic loci of which a large fraction has previously been linked to CVD. Our results highlight genes that might be important in the pathogenesis of MI or in recovery. In addition, the sites pointed out in this study can serve as candidates for further evaluation as potential biomarkers for MI.

The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10-8, in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms.

Connecting genetic risk to disease end points through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.

The Promises of Quantitative Proteomics in Precision Medicine

Precision medicine approach has a potential to ensure optimum efficacy and safety of drugs at individual patient level. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models could play a significant role in precision medicine by predicting interindividual variability in drug disposition and response. In order to develop robust PBPK/PD models, it is imperative that the critical physiological parameters affecting drug disposition and response and their variability are precisely characterized. Currently used PBPK/PD modeling software, for example, Simcyp and Gastroplus, encompass information such as organ volumes, blood flows to organs, body fat composition, glomerular filtration rate, etc. However, the information on the interindividual variability of the majority of the proteins associated with PK and PD, for example, drug metabolizing enzymes, transporters, and receptors, are not fully incorporated into these PBPK modeling platforms. Such information is significant because the population factors such as age, genotype, disease, and gender can affect abundance or activity of these proteins. To fill this critical knowledge gap, mass spectrometry-based quantitative proteomics has emerged as an important technique to characterize interindividual variability in the protein abundance of drug metabolizing enzymes, transporters, and receptors. Integration of these quantitative proteomics data into in silico PBPK/PD modeling tools will be crucial toward precision medicine.

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins: The Tromsø Study

Supplemental Digital Content is available in the text.

Background—

Genetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes.

Methods and Results—

As part of a larger case–control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein’s respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes.

Conclusions—

We have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.

Signatures of Evolutionary Adaptation in Quantitative Trait Loci Influencing Trace Element Homeostasis in Liver

Essential trace elements possess vital functions at molecular, cellular, and physiological levels in health and disease, and they are tightly regulated in the human body. In order to assess variability and potential adaptive evolution of trace element homeostasis, we quantified 18 trace elements in 150 liver samples, together with the expression levels of 90 genes and abundances of 40 proteins involved in their homeostasis. Additionally, we genotyped 169 single nucleotide polymorphism (SNPs) in the same sample set. We detected significant associations for 8 protein quantitative trait loci (pQTL), 10 expression quantitative trait loci (eQTLs), and 15 micronutrient quantitative trait loci (nutriQTL). Six of these exceeded the false discovery rate cutoff and were related to essential trace elements: 1) one pQTL for GPX2 (rs10133290); 2) two previously described eQTLs for HFE (rs12346) and SELO (rs4838862) expression; and 3) three nutriQTLs: The pathogenic C282Y mutation at HFE affecting iron (rs1800562), and two SNPs within several clustered metallothionein genes determining selenium concentration (rs1811322 and rs904773). Within the complete set of significant QTLs (which involved 30 SNPs and 20 gene regions), we identified 12 SNPs with extreme patterns of population differentiation (FST values in the top 5% percentile in at least one HapMap population pair) and significant evidence for selective sweeps involving QTLs at GPX1, SELENBP1, GPX3, SLC30A9, and SLC39A8. Overall, this detailed study of various molecular phenotypes illustrates the role of regulatory variants in explaining differences in trace element homeostasis among populations and in the human adaptive response to environmental pressures related to micronutrients.

Developments in FTICR-MS and Its Potential for Body Fluid Signatures

Fourier transform mass spectrometry (FTMS) is the method of choice for measurements that require ultra-high resolution. The establishment of Fourier transform ion cyclotron resonance (FTICR) MS, the availability of biomolecular ionization techniques and the introduction of the Orbitrap™ mass spectrometer have widened the number of FTMS-applications enormously. One recent example involves clinical proteomics using FTICR-MS to discover and validate protein biomarker signatures in body fluids such as serum or plasma. These biological samples are highly complex in terms of the type and number of components, their concentration range, and the structural identity of each species, and thus require extensive sample cleanup and chromatographic separation procedures. Clearly, such an elaborate and multi-step sample preparation process hampers high-throughput analysis of large clinical cohorts. A final MS read-out at ultra-high resolution enables the analysis of a more complex sample and can thus simplify upfront fractionations. To this end, FTICR-MS offers superior ultra-high resolving power with accurate and precise mass-to-charge ratio (m/z) measurement of a high number of peptides and small proteins (up to 20 kDa) at isotopic resolution over a wide mass range, and furthermore includes a wide variety of fragmentation strategies to characterize protein sequence and structure, including post-translational modifications (PTMs). In our laboratory, we have successfully applied FTICR “next-generation” peptide profiles with the purpose of cancer disease classifications. Here we will review a number of developments and innovations in FTICR-MS that have resulted in robust and routine procedures aiming for ultra-high resolution signatures of clinical samples, exemplified with state-of-the-art examples for serum and saliva.