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

Serum biomarkers associated with baseline clinical severity in young steroid-naïve Duchenne muscular dystrophy boys

Duchenne muscular dystrophy (DMD) is caused by loss of dystrophin in muscle, and while all patients share the primary gene and biochemical defect, there is considerable patient–patient variability in clinical symptoms. We sought to develop multivariate models of serum protein biomarkers that explained observed variation, using functional outcome measures as proxies for severity. Serum samples from 39 steroid-naïve DMD boys 4 to <7 years enrolled into a clinical trial of vamorolone were studied (NCT02760264). Four assessments of gross motor function were carried out for each participant over a 6-week interval, and their mean was used as response for biomarker models. Weighted correlation network analysis was used for unsupervised clustering of 1305 proteins quantified using SOMAscan^® aptamer profiling to define highly representative and connected proteins. Multivariate models of biomarkers were obtained for time to stand performance (strength phenotype; 17 proteins) and 6 min walk performance (endurance phenotype; 17 proteins) including some shared proteins. Identified proteins were tested with associations of mRNA expression with histological severity of muscle from dystrophinopathy patients (n = 28) and normal controls (n = 6). Strong associations predictive of both clinical and histological severity were found for ERBB4 (reductions in both blood and muscle with increasing severity), SOD1 (reductions in muscle and increases in blood with increasing severity) and CNTF (decreased levels in blood and muscle with increasing severity). We show that performance of DMD boys was effectively modeled with serum proteins, proximal strength associated with growth and remodeling pathways and muscle endurance centered on TGFβ and fibrosis pathways in muscle.

Plasma proteomic profiling reveals biomarkers associated with aortic dilation in patients with bicuspid aortic valve

Background

Bicuspid aortic valve (BAV) is the most common congenital heart anomaly and is prone to cause complications, such as valvular stenosis and thoracic aortic dilation. There is currently no reliable way to predict the progression rate to thoracic aortic aneurysm. Here, we aimed to characterize the proteomic landscape in the plasma of stenotic BAV patients and provide potential biomarkers to predict progressive aortic dilation.

Methods

Plasma samples were obtained from 45 subjects (30 stenotic BAV patients and 15 healthy controls). All samples were properly prepared and analyzed using mass spectrometry (MS)-based label-free quantitative proteomics.

Results

A total of 748 plasma proteins had missingness <50%, and 193 (25.8%) were differentially expressed in the BAV patients. Functions regarding cell junction and actin cytoskeleton were largely enriched. NOTCH3, a Notch receptor known to interact with the BAV-causing gene NOTCH1, was negatively correlated with aortic diameter and was downregulated in BAV patients' plasma and aortic smooth muscle cells. Further, a subset of plasma proteins, including ADAM10, was associated with rapidly progressive aortic dilation in BAV patients.

Conclusions

Our data reveal unique features in the proteomic architecture of stenotic BAV patients' plasma, and we propose the potential of Notch signaling proteins NOTCH3 and ADAM10 in predicting aortic dilation.

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer

Identifying secretory proteins from blood, saliva or other body fluids has become an effective method of diagnosing diseases. Existing secretory protein prediction methods are mainly based on conventional machine learning algorithms and are highly dependent on the feature set from the protein. In this article, we propose a deep learning model based on the capsule network and transformer architecture, SecProCT, to predict secretory proteins using only amino acid sequences. The proposed model was validated using cross-validation and achieved 0.921 and 0.892 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively. Meanwhile, the proposed model was validated on an independent test set and achieved 0.917 and 0.905 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively, which are better than conventional machine learning methods and other deep learning methods for biological sequence analysis. The main contributions of this article are as follows: (1) a deep learning model based on a capsule network and transformer architecture is proposed for predicting secretory proteins. The results of this model are better than the those of existing conventional machine learning methods and deep learning methods for biological sequence analysis; (2) only amino acid sequences are used in the proposed model, which overcomes the high dependence of existing methods on the annotated protein features; (3) the proposed model can accurately predict most experimentally verified secretory proteins and cancer protein biomarkers in blood and saliva.

Cognitive stimulation in the workplace, plasma proteins, and risk of dementia: three analyses of population cohort studies

OBJECTIVES

To examine the association between cognitively stimulating work and subsequent risk of dementia and to identify protein pathways for this association.

DESIGN

Multicohort study with three sets of analyses.

SETTING

United Kingdom, Europe, and the United States.

PARTICIPANTS

Three associations were examined: cognitive stimulation and dementia risk in 107 896 participants from seven population based prospective cohort studies from the IPD-Work consortium (individual participant data meta-analysis in working populations); cognitive stimulation and proteins in a random sample of 2261 participants from one cohort study; and proteins and dementia risk in 13 656 participants from two cohort studies.

MAIN OUTCOME MEASURES

Cognitive stimulation was measured at baseline using standard questionnaire instruments on active versus passive jobs and at baseline and over time using a job exposure matrix indicator. 4953 proteins in plasma samples were scanned. Follow-up of incident dementia varied between 13.7 to 30.1 years depending on the cohort. People with dementia were identified through linked electronic health records and repeated clinical examinations.

RESULTS

During 1.8 million person years at risk, 1143 people with dementia were recorded. The risk of dementia was found to be lower for participants with high compared with low cognitive stimulation at work (crude incidence of dementia per 10 000 person years 4.8 in the high stimulation group and 7.3 in the low stimulation group, age and sex adjusted hazard ratio 0.77, 95% confidence interval 0.65 to 0.92, heterogeneity in cohort specific estimates I²=0%, P=0.99). This association was robust to additional adjustment for education, risk factors for dementia in adulthood (smoking, heavy alcohol consumption, physical inactivity, job strain, obesity, hypertension, and prevalent diabetes at baseline), and cardiometabolic diseases (diabetes, coronary heart disease, stroke) before dementia diagnosis (fully adjusted hazard ratio 0.82, 95% confidence interval 0.68 to 0.98). The risk of dementia was also observed during the first 10 years of follow-up (hazard ratio 0.60, 95% confidence interval 0.37 to 0.95) and from year 10 onwards (0.79, 0.66 to 0.95) and replicated using a repeated job exposure matrix indicator of cognitive stimulation (hazard ratio per 1 standard deviation increase 0.77, 95% confidence interval 0.69 to 0.86). In analysis controlling for multiple testing, higher cognitive stimulation at work was associated with lower levels of proteins that inhibit central nervous system axonogenesis and synaptogenesis: slit homologue 2 (SLIT2, fully adjusted β -0.34, P<0.001), carbohydrate sulfotransferase 12 (CHSTC, fully adjusted β -0.33, P<0.001), and peptidyl-glycine α-amidating monooxygenase (AMD, fully adjusted β -0.32, P<0.001). These proteins were associated with increased dementia risk, with the fully adjusted hazard ratio per 1 SD being 1.16 (95% confidence interval 1.05 to 1.28) for SLIT2, 1.13 (1.00 to 1.27) for CHSTC, and 1.04 (0.97 to 1.13) for AMD.

CONCLUSIONS

The risk of dementia in old age was found to be lower in people with cognitively stimulating jobs than in those with non-stimulating jobs. The findings that cognitive stimulation is associated with lower levels of plasma proteins that potentially inhibit axonogenesis and synaptogenesis and increase the risk of dementia might provide clues to underlying biological mechanisms.

Thermal Pain Thresholds Are Significantly Associated with Plasma Proteins of the Immune System in Chronic Widespread Pain-An Exploratory Pilot Study Using Multivariate and Network Analyses

Chronic widespread pain (CWP), including fibromyalgia (FM), is characterized by generalized musculoskeletal pain. An important clinical feature is widespread increased pain sensitivity such as lowered pain thresholds for different stimuli such as heat (HPT) and cold (CPT). There is a growing interest in investigating the activated neurobiological mechanisms in CWP. This explorative proteomic study investigates the multivariate correlation pattern between plasma and muscle proteins and thermal pain thresholds in CWP and in healthy controls (CON). In addition, we analysed whether the important proteins and their networks for CPT and HPT differed between CWP and CON. We used a proteomic approach and analysed plasma and muscle proteins from women with CWP (n = 15) and CON (n = 23). The associations between the proteins and CPT/HPT were analysed using orthogonal partial least square (OPLS). The protein-protein association networks for the important proteins for the two thermal pain thresholds were analysed using STRING database. CWP had lowered pain thresholds for thermal stimulus. These levels were generally not related to the included clinical variables except in CWP for HPT. Highly interacting proteins mainly from plasma showed strong significant associations with CPT and HPT both in CWP and in CON. Marked differences in the important proteins for the two thermal pain thresholds were noted between CWP and CON; more complex patterns emerged in CWP. The important proteins were part of the immune system (acute phase proteins, complement factors, and immunoglobulin factors) or known to interact with the immune system. As expected, CWP had lowered pain thresholds for thermal stimulus. Although different proteins were important in the two groups, there were similarities. For example, proteins related to the host defence/immunity such as acute phase proteins, complement factors, immunoglobulin factors, and cytokines/chemokines (although not in CON for CPT) were important habitual/tonic factors for thermal pain thresholds. The fact that peripheral proteins contribute to thermal pain thresholds does not exclude that central factors also contribute and that complex interactions between peripheral and central factors determine the registered pain thresholds in CWP.

A proteome-wide genetic investigation identifies several SARS-CoV-2-exploited host targets of clinical relevance

Background

The virus SARS-CoV-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19.

Methods

To identify host proteins that may contribute to the risk of severe COVID-19, we undertook proteome-wide genetic colocalisation tests, and polygenic (pan) and cis-Mendelian randomisation analyses leveraging publicly available protein and COVID-19 datasets.

Results

Our analytic approach identified several known targets (e.g. ABO, OAS1), but also nominated new proteins such as soluble Fas (colocalisation probability >0.9, p=1 × 10-4), implicating Fas-mediated apoptosis as a potential target for COVID-19 risk. The polygenic (pan) and cis-Mendelian randomisation analyses showed consistent associations of genetically predicted ABO protein with several COVID-19 phenotypes. The ABO signal is highly pleiotropic, and a look-up of proteins associated with the ABO signal revealed that the strongest association was with soluble CD209. We demonstrated experimentally that CD209 directly interacts with the spike protein of SARS-CoV-2, suggesting a mechanism that could explain the ABO association with COVID-19.

Conclusions

Our work provides a prioritised list of host targets potentially exploited by SARS-CoV-2 and is a precursor for further research on CD209 and FAS as therapeutically tractable targets for COVID-19.

Funding

MAK, JSc, JH, AB, DO, MC, EMM, MG, ID were funded by Open Targets. J.Z. and T.R.G were funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). JSh and GJW were funded by the Wellcome Trust Grant 206194. This research was funded in part by the Wellcome Trust [Grant 206194]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

RSPO3 is important for trabecular bone and fracture risk in mice and humans

With increasing age of the population, countries across the globe are facing a substantial increase in osteoporotic fractures. Genetic association signals for fractures have been reported at the RSPO3 locus, but the causal gene and the underlying mechanism are unknown. Here we show that the fracture reducing allele at the RSPO3 locus associate with increased RSPO3 expression both at the mRNA and protein levels, increased trabecular bone mineral density and reduced risk mainly of distal forearm fractures in humans. We also demonstrate that RSPO3 is expressed in osteoprogenitor cells and osteoblasts and that osteoblast-derived RSPO3 is the principal source of RSPO3 in bone and an important regulator of vertebral trabecular bone mass and bone strength in adult mice. Mechanistic studies revealed that RSPO3 in a cell-autonomous manner increases osteoblast proliferation and differentiation. In conclusion, RSPO3 regulates vertebral trabecular bone mass and bone strength in mice and fracture risk in humans.

Medical Records-Based Genetic Studies of the Complement System

BACKGROUND

Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts.

METHODS

We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network.

RESULTS

In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; β=0.20; 95% CI, 0.14 to 0.25; P=1.52x10-11) and chr.19p13.3 (C3 locus; rs11569470-G; β=0.19; 95% CI, 0.13 to 0.24; P=1.29x10-8). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; β=0.40; 95% CI, 0.34 to 0.45; P=4.58x10-35). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (β=-0.36; 95% CI, -0.42 to -0.30; P=2.98x10-22) and C4-AL-BS (β=0.25; 95% CI, 0.21 to 0.29; P=8.11x10-23). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation.

CONCLUSIONS

We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.

ANNORE: Genetic fine mapping with functional annotation

Genome-wide association studies (GWAS) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes, and functional annotation from external databases to prioritize the most plausible causal SNPs in each trait-associated locus. Our proposed method improves upon previous fine mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine mapping studies helps to distinguish between variants in high LD, and to identify promising targets for follow-up studies.

FOSL2 Is Involved in the Regulation of Glycogen Content in Chicken Breast Muscle Tissue

The glycogen content in muscle of livestock and poultry animals affects the homeostasis of their body, growth performance, and meat quality after slaughter. FOS-like 2, AP-1 transcription factor subunit (FOSL2) was identified as a candidate gene related to muscle glycogen (MG) content in chicken in our previous study, but the role of FOSL2 in the regulation of MG content remains to be elucidated. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed on differentially expressed genes (DEGs) in breast muscle tissues from the high-MG-content (HMG) group and low-MG-content (LMG) group of Jingxing yellow chickens. Analysis of the 1,171 DEGs (LMG vs. HMG) identified, besides FOSL2, some additional genes related to MG metabolism pathway, namely PRKAG3, CEBPB, FOXO1, AMPK, and PIK3CB. Additionally, WGCNA revealed that FOSL2, CEBPB, MAP3K14, SLC2A14, PPP2CA, SLC38A2, PPP2R5E, and other genes related to the classical glycogen metabolism in the same coexpressed module are associated with MG content. Also, besides finding that FOSL2 expression is negatively correlated with MG content, a possible interaction between FOSL2 and CEBPB was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes) database. Furthermore, we investigated the effects of lentiviral overexpression of FOSL2 on the regulation of the glycogen content in vitro, and the result indicated that FOSL2 decreases the glycogen content in DF1 cells. Collectively, our results confirm that FOSL2 has a key role in the regulation of the MG content in chicken. This finding is helpful to understand the mechanism of MG metabolism regulation in chicken and provides a new perspective for the production of high-quality broiler and the development of a comprehensive nutritional control strategy.

Plasma Proteomics of Renal Function: A Transethnic Meta-Analysis and Mendelian Randomization Study

BACKGROUND

Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed.

METHODS

A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR.

RESULTS

In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR.

CONCLUSIONS

In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.

Genetically increased circulating FUT3 level leads to reduced risk of Idiopathic Pulmonary Fibrosis: a Mendelian Randomisation Study

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal fibrotic interstitial lung disease. Few circulating biomarkers have been identified to have causal effects on IPF.To identify candidate IPF-influencing circulating proteins, we undertook an efficient screen of circulating proteins by applying a two-sample Mendelian randomisation (MR) approach with existing publicly available data. For instruments we used genetic determinants of circulating proteins which reside cis to the encoded gene (cis-SNPs), identified by two genome-wide association studies (GWASs) in European individuals (3301 and 3200 subjects). We then applied MR methods to test if the levels of these circulating proteins influenced IPF susceptibility in the largest IPF GWAS (2668 cases and 8591 controls). We validated the MR results using colocalization analyses to ensure that both the circulating proteins and IPF shared a common genetic signal.MR analyses of 834 proteins found that a one sd increase in circulating FUT3 and FUT5 was associated with a reduced risk of IPF (OR: 0.81, 95%CI: 0.74-0.88, p=6.3×10^-7, and OR: 0.76, 95%CI: 0.68-0.86, p=1.1×10^-5). Sensitivity analyses including multiple-cis SNPs provided similar estimates both for FUT3 (inverse variance weighted [IVW] OR: 0.84, 95%CI: 0.78-0.91, p=9.8×10^-6, MR-Egger OR: 0.69, 95%CI: 0.50-0.97, p=0.03) and FUT5 (IVW OR: 0.84, 95%CI: 0.77-0.92, p=1.4×10^-4, MR-Egger OR: 0.59, 95%CI: 0.38-0.90, p=0.01) FUT3 and FUT5 signals colocalized with IPF signals, with posterior probabilities of a shared genetic signal of 99.9% and 97.7%. Further transcriptomic investigations supported the protective effects of FUT3 for IPF.An efficient MR scan of 834 circulating proteins provided evidence that genetically increased circulating FUT3 level is associated with reduced risk of IPF.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Association between Circulating Protein C Levels and Incident Dementia: The Atherosclerosis Risk in Communities Study

INTRODUCTION

Hemostasis depends on the delicate balance between coagulants and anticoagulants. Higher levels of circulating coagulants have been associated with higher risk of cerebral infarctions and dementia. In contrast, higher levels of circulating protein C, an endogenous anticoagulant, have been associated with lower risk of cerebral infarctions, and the association between protein C levels and the risk of dementia is unknown. The goal of this study was to evaluate the association of circulating protein C levels in midlife and late life with incident dementia.

METHODS

Circulating protein C levels were measured using blood samples collected at the midlife baseline (1987-1989) and the late-life baseline (2011-2013) among 14,462 and 3,614 participants, respectively, in the Atherosclerosis Risk in Communities study. Protein C levels were measured using enzyme-linked immunosorbent assay at midlife and a modified aptamer-based assay at late life. Participants were followed up to 2013 from midlife and up to 2017 from late life. Incident dementia was ascertained during the follow-up periods using in-person cognitive and functional assessment, informant interviews, and International Classification of Diseases codes at hospitalization discharge and on death certificates. Cause-specific Cox regression models were used to evaluate the association between quintiles of circulating protein C and incident dementia.

RESULTS

From midlife (mean age of 54), 1,389 incident dementia events were observed over a median follow-up of 23 years. From late life (mean age of 75), 353 incident dementia events were observed over a median follow-up of 4.9 years. At both midlife and late life, circulating protein C had an inverse association with incident dementia after adjusting for demographic, vascular, and hemostatic risk factors, incident stroke as time-dependent covariate, and incorporating stabilized weights based on propensity scores (quintile 5 vs. quintile 1 as the reference, midlife hazard ratio 0.80, 95% confidence interval 0.66-0.96, p value for trend 0.04; late-life hazard ratio 0.84, 95% confidence interval: 0.55-1.28, p value for trend 0.04).

DISCUSSION/CONCLUSION

Circulating protein C has an inverse association with incident dementia independent of established risk factors, including stroke. Our results suggest studying anticoagulants in addition to coagulants can increase our understanding on the relationship between hemostasis and dementia.

Human plasma proteomic profiles indicative of cardiorespiratory fitness

Maximal oxygen uptake (VO₂max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO₂max, and of increases of VO₂max in response to exercise training (ΔVO₂max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO₂max and ΔVO₂max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual's ΔVO₂max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans.

Multiomic Profiling in Black and White Populations Reveals Novel Candidate Pathways in Left Ventricular Hypertrophy and Incident Heart Failure Specific to Black Adults

BACKGROUND

Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand the underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a White cohort.

METHODS

We measured 1305 plasma proteins using the SomaScan platform in 1772 Black participants (mean age, 56 years; 62% women) in JHS (Jackson Heart Study) with LV mass assessed by 2-dimensional echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in White participants from FHS (Framingham Heart Study; mean age, 54 years; 56% women).

RESULTS

In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including NT-proBNP (N-terminal pro-B-type natriuretic peptide; β=0.04; P=2×10-8; hazard ratio, 1.48; P=0.0001). The strongest association with LV mass was novel: LKHA4 (leukotriene-A4 hydrolase; β=0.05; P=5×10-15). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 (C-X3-C Motif Chemokine Ligand 1) showed a novel association with incident HF (hazard ratio, 1.32; P=0.0002). While established biomarkers such as cystatin C and NT-proBNP showed consistent associations in Black and White individuals, LKHA4 and fractalkine were significantly different between the two groups.

CONCLUSIONS

We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.

Metabolic syndrome and the plasma proteome: from association to causation

Background

The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering.

Methods

Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity.

We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization.

Results

Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71–0.79) in KORA.

Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = − 0.12, Wald-p = 3.63e−13), apolipoprotein B (APOB) (Wald-Ratio = − 0.09, Wald-p = 2.54e−04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e−04).

Conclusions

Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01299-2.

Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions

Mechanisms underlying severe coronavirus disease 2019 (COVID-19) disease remain poorly understood. We analyze several thousand plasma proteins longitudinally in 306 COVID-19 patients and 78 symptomatic controls, uncovering immune and non-immune proteins linked to COVID-19. Deconvolution of our plasma proteome data using published scRNA-seq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who died to severely ill survivors identifies dynamic immune-cell-derived and tissue-associated proteins associated with survival, including exocrine pancreatic proteases. Using derived tissue-specific and cell-type-specific intracellular death signatures, cellular angiotensin-converting enzyme 2 (ACE2) expression, and our data, we infer whether organ damage resulted from direct or indirect effects of infection. We propose a model in which interactions among myeloid, epithelial, and T cells drive tissue damage. These datasets provide important insights and a rich resource for analysis of mechanisms of severe COVID-19 disease.

Pregnancy Serum DLK1 Concentrations Are Associated With Indices of Insulin Resistance and Secretion

CONTEXT

Delta like noncanonical notch ligand 1 (DLK1) is a paternally expressed imprinted gene that encodes an epidermal growth factor repeat-containing transmembrane protein. A bioactive, truncated DLK1 protein is present in the circulation and has roles in development and metabolism.

OBJECTIVE

We sought to investigate links between maternal pregnancy circulating DLK1 concentrations and: (1) maternal and fetal DLK1 genotypes, (2) maternal insulin resistance and secretion, and (3) offspring size at birth.

PATIENTS, DESIGN, AND SETTING

We measured third-trimester maternal serum DLK1 concentrations and examined their associations with parentally transmitted fetal and maternal DLK1 genotypes, indices of maternal insulin resistance and secretion derived from 75-g oral glucose tolerance tests performed around week 28 of pregnancy, and offspring size at birth in 613 pregnancies from the Cambridge Baby Growth Study.

RESULTS

Maternal DLK1 concentrations were associated with the paternally transmitted fetal DLK1 rs12147008 allele (P = 7.8 × 10-3) but not with maternal rs12147008 genotype (P = 0.4). Maternal DLK1 concentrations were positively associated with maternal prepregnancy body mass index (P = 3.5 × 10-6), and (after adjustment for maternal body mass index) with both maternal fasting insulin resistance (Homeostatic Model Assessment of Insulin Resistance: P = 0.01) and measures of maternal insulin secretion in response to oral glucose (insulinogenic index: P = 1.2 × 10-3; insulin disposition index: P = 0.049). Further positive associations were found with offspring weight (P = 0.02) and head circumference at birth (P = 0.04).

CONCLUSION

These results are consistent with a partial paternal or placental origin for the maternal circulating DLK1 which may lead to increased maternal circulating DLK1 concentrations, stimulation of maternal insulin resistance and compensatory hyperinsulinemia during pregnancy, and the promotion of fetal growth.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk

The plasma proteomic changes that precede the onset of dementia could yield insights into disease biology and highlight new biomarkers and avenues for intervention. We quantified 4,877 plasma proteins in nondemented older adults in the Atherosclerosis Risk in Communities cohort and performed a proteome-wide association study of dementia risk over five years (n = 4,110; 428 incident cases). Thirty-eight proteins were associated with incident dementia after Bonferroni correction. Of these, 16 were also associated with late-life dementia risk when measured in plasma collected nearly 20 years earlier, during mid-life. Two-sample Mendelian randomization causally implicated two dementia-associated proteins (SVEP1 and angiostatin) in Alzheimer's disease. SVEP1, an immunologically relevant cellular adhesion protein, was found to be part of larger dementia-associated protein networks, and circulating levels were associated with atrophy in brain regions vulnerable to Alzheimer's pathology. Pathway analyses for the broader set of dementia-associated proteins implicated immune, lipid, metabolic signaling and hemostasis pathways in dementia pathogenesis.

Integrative biochemical, proteomics and metabolomics cerebrospinal fluid biomarkers predict clinical conversion to multiple sclerosis

Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalized medicine approach. We sought to identify biomarkers from biochemical, metabolic and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2-10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict the occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis.

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

Genetic Risk Assessment for Atherosclerotic Cardiovascular Disease: A Guide for the General Cardiologist

Genetic testing for cardiovascular (CV) disease has had a profound impact on the diagnosis and evaluation of monogenic causes of CV disease, such as hypertrophic and familial cardiomyopathies, long QT syndrome, and familial hypercholesterolemia (FH). The success in genetic testing for monogenic diseases has prompted special interest in utilizing genetic information in the risk assessment of more common diseases such as atherosclerotic cardiovascular disease (ASCVD). Polygenic risk scores (PRS) have been developed to assess the risk of coronary artery disease (CAD) that now include millions of single-nucleotide polymorphisms (SNPs) that have been identified through genome-wide association studies (GWAS). While these PRS have demonstrated a strong association with CAD in large cross-sectional population studies, there remains intense debate regarding the added value that PRS contribute to existing clinical risk prediction models such as the pooled cohort equations (PCEs). In this review, we provide a brief background of genetic testing for monogenic drivers of CV disease and then focus on the recent developments in genetic risk assessment of ASCVD, including the use of PRS. We outline the genetic testing that is currently available to all cardiologists in the clinic and discuss the evolving sphere of specialized cardiovascular genetics programs (CVGPs) that integrate the expertise of cardiologists, geneticists, and genetic counselors. Finally, we review the possible implications that PRS and pharmacogenomic data may soon have on clinical practice in the care for patients with or at risk of developing ASCVD.

The effect of angiotensin-converting enzyme levels on COVID-19 susceptibility and severity: a Mendelian randomization study

Background

There has been uncertainty about the safety or benefit of angiotensin-converting enzyme (ACE) inhibitors during the COVID-19 pandemic. We used Mendelian randomization using genetic determinants of serum-ACE levels to test whether decreased ACE levels increase susceptibility to SARS-CoV-2 infection or COVID-19 severity, while reducing potential bias from confounding and reverse causation in observational studies.

Methods

Genetic variants strongly associated with ACE levels, which were nearby the ACE gene, were identified from the ORIGIN trial and a separate genome-wide association study (GWAS) of ACE levels from the AGES cohort. The ORIGIN trial included 4147 individuals of European and Latino ancestries. Sensitivity analyses were performed using a study of 3200 Icelanders. Cohorts from the COVID-19 Host Genetics Initiative GWAS of up to 960 186 individuals of European ancestry were used for COVID-19 susceptibility, hospitalization and severe-disease outcome.

Results

Genetic variants were identified that explain between 18% and 37% of variance in ACE levels. Using genetic variants from the ORIGIN trial, a standard-deviation decrease in ACE levels was not associated with an increase in COVID-19 susceptibility [odds ratio (OR): 1.02, 95% confidence interval (CI): 0.90, 1.15], hospitalization (OR: 0.86, 95% CI: 0.68, 1.08) or severe disease (OR: 0.74, 95% CI: 0.51, 1.06). Using genetic variants from the AGES cohort, the result was similar for susceptibility (OR: 0.98, 95% CI: 0.89, 1.09), hospitalization (OR: 0.86, 95% CI: 0.66, 1.11) and severity (OR: 0.75, 95% CI: 0.50, 1.14). Multiple-sensitivity analyses led to similar results.

Conclusion

Genetically decreased serum ACE levels were not associated with susceptibility to, or severity of, COVID-19 disease. These data suggest that individuals taking ACE inhibitors should not discontinue therapy during the COVID-19 pandemic.

Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk

Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain-containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.

Genetic control of the human brain proteome

We generated an online brain pQTL resource for 7,376 proteins through the analysis of genetic and proteomic data derived from post-mortem samples of the dorsolateral prefrontal cortex of 330 older adults. The identified pQTLs tend to be non-synonymous variation, are over-represented among variants associated with brain diseases, and replicate well (77%) in an independent brain dataset. Comparison to a large study of brain eQTLs revealed that about 75% of pQTLs are also eQTLs. In contrast, about 40% of eQTLs were identified as pQTLs. These results are consistent with lower pQTL mapping power and greater evolutionary constraint on protein abundance. The latter is additionally supported by observations of pQTLs with large effects' tending to be rare, deleterious, and associated with proteins that have evidence for fewer protein-protein interactions. Mediation analyses using matched transcriptomic and proteomic data provided additional evidence that pQTL effects are often, but not always, mediated by mRNA. Specifically, we identified roughly 1.6 times more mRNA-mediated pQTLs than mRNA-independent pQTLs (550 versus 341). Our pQTL resource provides insight into the functional consequences of genetic variation in the human brain and a basis for novel investigations of genetics and disease.

A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity

To identify circulating proteins influencing Coronavirus Disease 2019 (COVID-19) susceptibility and severity, we undertook a two-sample Mendelian randomization (MR) study, rapidly scanning hundreds of circulating proteins while reducing bias due to reverse causation and confounding. In up to 14,134 cases and 1.2 million controls, we found that an s.d. increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (odds ratio (OR) = 0.54, P = 7 × 10^-8), hospitalization (OR = 0.61, P = 8 × 10^-8) and susceptibility (OR = 0.78, P = 8 × 10^-6). Measuring OAS1 levels in 504 individuals, we found that higher plasma OAS1 levels in a non-infectious state were associated with reduced COVID-19 susceptibility and severity. Further analyses suggested that a Neanderthal isoform of OAS1 in individuals of European ancestry affords this protection. Thus, evidence from MR and a case-control study support a protective role for OAS1 in COVID-19 adverse outcomes. Available pharmacological agents that increase OAS1 levels could be prioritized for drug development.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

Convergence of biomarkers and risk factor trait loci of coronary artery disease at 3p21.31 and HLA region

Here we seek to identify molecular biomarkers that mediate the effect of risk factors on coronary artery disease (CAD). We perform a SNP-based multiomics data analysis to find biomarkers (probes) causally associated with the risk of CAD within known genomic loci for its risk factors. We identify 78 biomarkers, the majority (64%) of which are methylation probes. We detect the convergence of several CNS and lifestyle trait loci and their biomarkers at the 3p21.31 and human leukocyte antigen (HLA) regions. The 3p21.31 locus was the most populated region in the convergence of biomarkers and risk factors. In this region, we noted as the BSN gene becomes methylated the level of stomatin (STOM) in blood increases and this contributes to higher risk of CAD. In the HLA locus, we identify several methylation biomarkers associated with various CAD risk factors. SNPs in the CFB gene display a trans-regulatory impact on the GRIA4 protein level. A methylation site upstream of the APOE gene is associated with a higher protein level of S100A13 which in turn leads to higher LDL-C and greater CAD risk. We find UHRF1BP1 and ILRUN mediate the effect of obesity on CAD whereas methylation sites within NOS3 and CKM mediate the effect of their associated-risk factors on CAD. This study provides further insight into the biology of CAD and identifies a list of biomarkers that mediate the impact of risk factors on CAD. A SNP-based initiative can unite data from various fields of omics into a single network of knowledge.

Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans

Using samples from the New England Centenarian Study (NECS), we sought to characterize the serum proteome of 77 centenarians, 82 centenarians' offspring, and 65 age-matched controls of the offspring (mean ages: 105, 80, and 79 years). We identified 1312 proteins that significantly differ between centenarians and their offspring and controls (FDR < 1%), and two different protein signatures that predict longer survival in centenarians and in younger people. By comparing the centenarian signature with 2 independent proteomic studies of aging, we replicated the association of 484 proteins of aging and we identified two serum protein signatures that are specific of extreme old age. The data suggest that centenarians acquire similar aging signatures as seen in younger cohorts that have short survival periods, suggesting that they do not escape normal aging markers, but rather acquire them much later than usual. For example, centenarian signatures are significantly enriched for senescence-associated secretory phenotypes, consistent with those seen with younger aged individuals, and from this finding, we provide a new list of serum proteins that can be used to measure cellular senescence. Protein co-expression network analysis suggests that a small number of biological drivers may regulate aging and extreme longevity, and that changes in gene regulation may be important to reach extreme old age. This centenarian study thus provides additional signatures that can be used to measure aging and provides specific circulating biomarkers of healthy aging and longevity, suggesting potential mechanisms that could help prolong health and support longevity.

Broadly effective metabolic and immune recovery with C5 inhibition in CHAPLE disease

Complement hyperactivation, angiopathic thrombosis and protein-losing enteropathy (CHAPLE disease) is a lethal disease caused by genetic loss of the complement regulatory protein CD55, leading to overactivation of complement and innate immunity together with immunodeficiency due to immunoglobulin wasting in the intestine. We report in vivo human data accumulated using the complement C5 inhibitor eculizumab for the medical treatment of patients with CHAPLE disease. We observed cessation of gastrointestinal pathology together with restoration of normal immunity and metabolism. We found that patients rapidly renormalized immunoglobulin concentrations and other serum proteins as revealed by aptamer profiling, re-established a healthy gut microbiome, discontinued immunoglobulin replacement and other treatments and exhibited catch-up growth. Thus, we show that blockade of C5 by eculizumab effectively re-establishes regulation of the innate immune complement system to substantially reduce the pathophysiological manifestations of CD55 deficiency in humans.

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10-4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

Synaptic processes and immune-related pathways implicated in Tourette syndrome

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

The ABO histo-blood group, endothelial activation, and acute respiratory distress syndrome risk in critical illness

BACKGROUNDThe ABO histo-blood group is defined by carbohydrate modifications and is associated with risk for multiple diseases, including acute respiratory distress syndrome (ARDS). We hypothesized that genetically determined blood subtype A1 is associated with increased risk of ARDS and markers of microvascular dysfunction and coagulation.METHODSWe conducted analyses in 3 cohorts of critically ill trauma and sepsis patients (n = 3710) genotyped on genome-wide platforms to determine the association of the A1 blood type genotype with ARDS risk. We subsequently determined whether associations were present in FUT2-defined nonsecretors who lack ABO antigens on epithelium, but not endothelium. In a patient subgroup, we determined the associations of blood type with plasma levels of endothelial glycoproteins and disseminated intravascular coagulation (DIC). Lastly, we tested whether blood type A was associated with less donor lung injury recovery during human ex vivo lung perfusion (EVLP).RESULTSThe A1 genotype was associated with a higher risk of moderate to severe ARDS relative to type O in all 3 populations. In sepsis, this relationship was strongest in nonpulmonary infections. The association persisted in nonsecretors, suggesting a vascular mechanism. The A1 genotype was also associated with higher DIC risk as well as concentrations of thrombomodulin and von Willebrand factor, which in turn were associated with ARDS risk. Blood type A was also associated with less lung injury recovery during EVLP.CONCLUSIONWe identified a replicable association between ABO blood type A1 and risk of ARDS among the critically ill, possibly mediated through microvascular dysfunction and coagulation.FUNDINGNIH HL122075, HL125723, HL137006, HL137915, DK097307, HL115354, HL101779, and the University of Pennsylvania McCabe Fund Fellowship Award.

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.

Genetic Analyses of Common Infections in the Avon Longitudinal Study of Parents and Children Cohort

The burden of infections on an individual and public health is profound. Many observational studies have shown a link between infections and the pathogenesis of disease; however a greater understanding of the role of host genetics is essential. Children from the longitudinal birth cohort, the Avon Longitudinal Study of Parents and Children, had 14 antibodies measured in plasma at age 7: Alpha-casein protein, beta-casein protein, cytomegalovirus, Epstein-Barr virus, feline herpes virus, Helicobacter pylori, herpes simplex virus 1, influenza virus subtype H1N1, influenza virus subtype H3N2, measles virus, Saccharomyces cerevisiae, Theiler's virus, Toxoplasma gondii, and SAG1 protein domain, a surface antigen of Toxoplasma gondii measured for greater precision. We performed genome-wide association analyses of antibody levels against these 14 infections (N = 357 - 5010) and identified three genome-wide signals (P < 5×10^-8), two associated with measles virus antibodies and one with Toxoplasma gondii antibodies. In an association analysis focused on the human leukocyte antigen (HLA) region of the genome, we further detected 15 HLA alleles at a two-digit resolution and 23 HLA alleles at a four-digit resolution associated with five antibodies, with eight HLA alleles associated with Epstein-Barr virus antibodies showing strong evidence of replication in UK Biobank. We discuss how our findings from antibody levels complement other studies using self-reported phenotypes in understanding the architecture of host genetics related to infections.

It's in Our Blood: A Glimpse of Personalized Medicine

Recent advances in protein profiling technology has facilitated simultaneous measurement of thousands of proteins in large population studies, exposing the depth and complexity of the plasma and serum proteomes. This revealed that proteins in circulation were organized into regulatory modules under genetic control and closely associated with current and future common diseases. Unlike networks in solid tissues, serum protein networks comprise members synthesized across different tissues of the body. Genetic analysis reveals that this cross-tissue regulation of the serum proteome participates in systemic homeostasis and mirrors the global disease state of individuals. Here, we discuss how application of this information in routine clinical evaluations may transform the future practice of medicine.

Investigation of Genetic Variations of IL6 and IL6R as Potential Prognostic and Pharmacogenetics Biomarkers: Implications for COVID-19 and Neuroinflammatory Disorders

In the present study, we investigated the distribution of genetic variations in IL6 and IL6R genes, which may be employed as prognostic and pharmacogenetic biomarkers for COVID-19 and neurodegenerative diseases. The study was performed on 271 samples representative of the Italian general population and identified seven variants (rs140764737, rs142164099, rs2069849, rs142759801, rs190436077, rs148171375, rs13306435) in IL6 and five variants (rs2228144, rs2229237, rs2228145, rs28730735, rs143810642) within IL6R, respectively. These variants have been predicted to affect the expression and binding ability of IL6 and IL6R. Ingenuity Pathway Analysis (IPA) showed that IL6 and IL6R appeared to be implicated in several pathogenetic mechanisms associated with COVID-19 severity and mortality as well as with neurodegenerative diseases mediated by neuroinflammation. Thus, the availability of IL6-IL6R-related biomarkers for COVID-19 may be helpful to counteract harmful complications and prevent multiorgan failure. At the same time, IL6-IL6R-related biomarkers could also be useful for assessing the susceptibility and progression of neuroinflammatory disorders and undertake the most suitable treatment strategies to improve patients' prognosis and quality of life. In conclusion, this study showed how IL6 pleiotropic activity could be exploited to meet different clinical needs and realize personalized medicine protocols for chronic, age-related and modern public health emergencies.

Serum levels of ACE2 are higher in patients with obesity and diabetes

Objective

As severity of outcome in COVID‐19 is disproportionately higher among individuals with obesity, smokers, patients with hypertension, kidney disease, chronic pulmonary disease, coronary heart disease (CHD), and/or type 2 diabetes (T2D), serum levels of ACE2, the cellular entry point for the coronavirus SARS‐CoV‐2, were examined in these high‐risk groups.

Methods

Associations of ACE2 levels to smokers and patients with hypertension, T2D, obesity, CHD, or COPD were investigated in a single center population‐based study of 5457 Icelanders from the Age, Gene/Environment Susceptibility Reykjavík Study (AGES‐RS) of the elderly (mean age 75 ± 6 years), using multiple linear regression analysis.

Results

Serum levels of ACE2 were higher in smokers and individuals with T2D and/or obesity while they were unaffected in the other patient groups.

Conclusion

ACE2 levels are higher in some patient groups with comorbidities linked to COVID‐19 including obesity and T2D and as such may have an emerging role as a circulating biomarker for severity of outcome in the disease.

Genetic architecture of host proteins involved in SARS-CoV-2 infection

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/ ).

Whole-genome sequencing analysis of the cardiometabolic proteome

The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5×) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations (P < 7.45 × 10⁻¹¹) across the allele frequency spectrum, all of which replicate in an independent cohort (n = 1605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.

The human proteome represents a crucial link between complex disease and genetic/environmental factors. Here, the authors investigate 257 cardiometabolic-relevant protein biomarkers in whole genome sequencing data from 1328 individuals, revealing the genetic architecture underlying biomarker variation.

The BDNF-Increasing Allele is Associated With Increased Proportion of Fast-Twitch Muscle Fibers, Handgrip Strength, and Power Athlete Status

Guilherme, JPLF, Semenova, EA, Borisov, OV, Kostryukova, ES, Vepkhvadze, TF, Lysenko, EA, Andryushchenko, ON, Andryushchenko, LB, Lednev, EM, Larin, AK, Bondareva, EA, Generozov, EV, and Ahmetov, II. The BDNF-increasing allele is associated with increased proportion of fast-twitch muscle fibers, handgrip strength, and power athlete status. J Strength Cond Res XX(X): 000-000, 2020-The brain-derived neurotrophic factor (BDNF) is involved in neurogenesis and formation of regenerated myofibers following injury or damage. A recent study suggested that the BDNF overexpression increases the proportion of fast-twitch muscle fibers, while the BDNF deletion promotes a fast-to-slow transition. The purpose of this study was to evaluate the association between the BDNF gene rs10501089 polymorphism (associated with blood BDNF levels), muscle fiber composition, and power athlete status. Muscle fiber composition was determined in 164 physically active individuals (113 men, 51 women). BDNF genotype and allele frequencies were compared between 508 Russian power athletes, 178 endurance athletes, and 190 controls. We found that carriers of the minor A-allele (the BDNF-increasing allele) had significantly higher percentage of fast-twitch muscle fibers than individuals homozygous for the G-allele (males: 64.3 [7.8] vs. 50.3 [15.8]%, p = 0.0015; all subjects: 64.1 ± 7.9 vs. 49.6 ± 14.7%, p = 0.0002). Furthermore, the A-allele was associated (p = 0.036) with greater handgrip strength in a sub-group of physically active subjects (n = 83) and over-represented in power athletes compared with controls (7.7 vs. 2.4%, p = 0.0001). The presence of the A-allele (i.e., AA+AG genotypes) rather than GG genotype increased the odds ratio of being a power athlete compared with controls (odds ratio [OR]: 3.43, p = 0.00071) or endurance athletes (OR: 2.36, p = 0.0081). In conclusion, the rs10501089 A-allele is associated with increased proportion of fast-twitch muscle fibers and greater handgrip strength, and these may explain, in part, the association between the AA/AG genotypes and power athlete status.

Deciphering the Plasma Proteome of Type 2 Diabetes

With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.

A homozygous truncating variant in CCDC186 in an individual with epileptic encephalopathy

Coiled-Coil Domain Containing Protein 186 (CCDC186) is hypothesized to play an important role in the biogenesis of dense-core vesicles in neurons and endocrine cells. Biallelic loss-of-function variants in the encoding gene CCDC186 have been suggested as a candidate gene for a neurodevelopmental phenotype, but only one patient has been described so far. We report a second patient with a CCDC186-associated phenotype presenting with developmental delay, epileptic encephalopathy, and failure to thrive. Exome sequencing identified a homozygous loss-of-function variant in CCDC186 (NM_018017.2) c.767C> G; p.(Ser256Ter) thus providing further evidence to support CCDC186 as a new disease gene for an autosomal recessive neurodevelopmental disorder.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.

Plasma proteomics reveals tissue-specific cell death and mediators of cell-cell interactions in severe COVID-19 patients

COVID-19 has caused over 1 million deaths globally, yet the cellular mechanisms underlying severe disease remain poorly understood. By analyzing several thousand plasma proteins in 306 COVID-19 patients and 78 symptomatic controls over serial timepoints using two complementary approaches, we uncover COVID-19 host immune and non-immune proteins not previously linked to this disease. Integration of plasma proteomics with nine published scRNAseq datasets shows that SARS-CoV-2 infection upregulates monocyte/macrophage, plasmablast, and T cell effector proteins. By comparing patients who died to severely ill patients who survived, we identify dynamic immunomodulatory and tissue-associated proteins associated with survival, providing insights into which host responses are beneficial and which are detrimental to survival. We identify intracellular death signatures from specific tissues and cell types, and by associating these with angiotensin converting enzyme 2 (ACE2) expression, we map tissue damage associated with severe disease and propose which damage results from direct viral infection rather than from indirect effects of illness. We find that disease severity in lung tissue is driven by myeloid cell phenotypes and cell-cell interactions with lung epithelial cells and T cells. Based on these results, we propose a model of immune and epithelial cell interactions that drive cell-type specific and tissue-specific damage in severe COVID-19.

Proteomic assessment of serum biomarkers of longevity in older men

The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.

Clinical and Proteomic Correlates of Plasma ACE2 (Angiotensin-Converting Enzyme 2) in Human Heart Failure

ACE2 (angiotensin-converting enzyme 2) is a key component of the renin-angiotensin-aldosterone system. Yet, little is known about the clinical and biologic correlates of circulating ACE2 levels in humans. We assessed the clinical and proteomic correlates of plasma (soluble) ACE2 protein levels in human heart failure. We measured plasma ACE2 using a modified aptamer assay among PHFS (Penn Heart Failure Study) participants (n=2248). We performed an association study of ACE2 against ≈5000 other plasma proteins measured with the SomaScan platform. Plasma ACE2 was not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 was associated with older age, male sex, diabetes mellitus, a lower estimated glomerular filtration rate, worse New York Heart Association class, a history of coronary artery bypass surgery, and higher pro-BNP (pro-B-type natriuretic peptide) levels. Plasma ACE2 exhibited associations with 1011 other plasma proteins. In pathway overrepresentation analyses, top canonical pathways associated with plasma ACE2 included clathrin-mediated endocytosis signaling, actin cytoskeleton signaling, mechanisms of viral exit from host cells, EIF2 (eukaryotic initiation factor 2) signaling, and the protein ubiquitination pathway. In conclusion, in humans with heart failure, plasma ACE2 is associated with various clinical factors known to be associated with severe coronavirus disease 2019 (COVID-19), including older age, male sex, and diabetes mellitus, but is not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 protein levels are prominently associated with multiple cellular pathways involved in cellular endocytosis, exocytosis, and intracellular protein trafficking. Whether these have a causal relationship with ACE2 or are relevant to novel coronavirus-2 infection remains to be assessed in future studies.