Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

Extensive modulation of the circulating blood proteome by hormonal contraceptive use across two population studies

BACKGROUND

The study of circulating blood proteins in population cohorts offers new avenues to explore lifestyle-related and genetic influences describing and shaping human health.

METHODS

Utilizing high-throughput mass spectrometry, we quantified 148 highly abundant proteins, functioning in the innate and adaptive immune system, coagulation and nutrient transport in 3632 blood plasma, and 500 serum samples from the CHRIS and BASE-II cross-sectional population studies, respectively. Through multiple regression analyses, we aimed to identify the main factors influencing the circulating proteome at population level.

RESULTS

Many demographic covariates and common medications affect the concentration of high-abundant plasma proteins, but the most significant changes are linked to the use of hormonal contraceptives (HCU). HCU particularly alters amongst others the levels of Angiotensinogen and Transcortin. We robustly replicated these findings in the BASE-II cohort. Furthermore, our results indicate that combined hormonal contraceptives with ethinylestradiol have a stronger effect compared to bioidentical estrogens. Our analysis detects no lasting impact of hormonal contraceptives on the plasma proteome.

CONCLUSIONS

HCU is the dominant factor reshaping the high-abundant circulating blood proteome in two population studies. Given the high prevalence of HCU among young women, it is essential to account for this treatment in human proteome studies to avoid misinterpreting its impact as sex- or age-related effects. Although we did not investigate the influence of HCU-induced proteomic changes on human health, our data suggest that future studies on this topic are warranted.

Plasma proteome analysis of rheumatic patients reveals differences in fingerprints based on cardiovascular history: a pilot study

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is much higher than that in the general population. As its etiology is not fully understood, we performed a pilot study using a shotgun proteomic approach to investigate whether the plasma signature in RA patients with CVD might show an altered profile. Subjects with RA were compared to a group of RA patients with a previous cardiovascular event (CVE). The cohort consisted of an RA control group (n = 10) and a group (n = 10) of RA patients with a history of CVD. Samples were collected at least 6 months before the CVE and 3-6 months after the CVE. All subjects were matched to controls for age, sex, and medication use. Plasma depletion of the 14 most abundant proteins was followed by bottom-up shotgun proteomics analysis (LC‒MS/MS). Relative changes in protein/peptide abundance were investigated using classical statistical analyses with Perseus and XG-Boost machine learning to compare between groups and to determine the relative importance of identified proteins, respectively. Principal component analysis (PCA) revealed no difference in the global protein and peptide signatures between the control and CVE groups. A total of 150, 239 and 74 protein ID's showed in comparison between Post Event vs. controls, Event vs. no Event and Pre event vs. Post Event respectively a statistically difference in relative abundance (p < 0.05). Remarkedly a total of 236 proteins ID's showed a statistical significant difference in relative abundance in the PRE-Event group compared to the control group which could also be confirmed by XGboost machine learning. Here, we demonstrated potential differences in the plasma proteome signature of rheumatic patients with cardiovascular events. Interestingly, this signature may be present prior to CVE's. However the conclusions must be drawn with caution, since this is a pilot study and further investigation with larger cohorts is warranted to identify potential risk markers that may predict the relative risk of CVEs in rheumatic diseases.

A targeted LC MS/MS assay of a health surveillance panel and its application to chronic kidney disease

Robust and reproducible assays capable of specific and quantitative monitoring of multiple biologically important proteins amongst the thousands of human plasma proteins can potentially be used to distinguish health versus disease. In this study, we established an LC-MS assay to monitor a Health Surveillance Panel (HSP) comprising 60 circulating plasma proteins selected based on their broad biological functions and assay performance. Plasma samples were prepared for proteomic analysis in an automated process. A scheduled LC-MRM assay with a 30-minute 5% - 35% acetonitrile gradient and 50.5 minutes of total run time was used to quantify the 60 endogenous proteins by monitoring 364 transitions from 117 proteotypic peptides along with their stable isotopic labeled standard peptides in a single assay. For each proteotypic peptide, we selected a quantifier ion and at least two qualifier ions. The quantifier ions have a linear response over a 100-fold range, and the peak area ratios of the three peptide ions were consistent. As proof of concept, we evaluated the performance of our HSP assay in a case-control study of progressive chronic kidney disease (CKD). Reduced plasma concentrations of alpha-2-antiplasmin, antithrombin-III, and immunoglobulin heavy constant alpha 1 correlated with CKD indicated by reduced GFR with p values < 0.05. These results demonstrate that the HSP proteins can be accurately and reproducibly quantified with a high-quality multiplexed MRM assay and the HSP assay can detect disease-associated differences.

Distinctive blood and salivary proteomics signatures in Qatari individuals at high risk for cardiovascular disease

Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality. Timely diagnosis is important in reducing both short and long-term health complications. Saliva has emerged as a potential source for biomarker discovery, offering a non-invasive tool for early detection of individuals at elevated risk for CVD, yet large-scale extensive proteomic analysis using saliva for a comprehensive biomarker discovery remains limited. In an effort to develop a diagnostic tool using saliva samples, our study aims to assess the salivary and plasma proteomes in subjects with high risk of developing CVD using a large-scale proteomic approach. Leveraging on the SOMAscan platform, we analyzed 1,317 proteins in saliva and plasma collected from subjects at a high risk of CVD (HR-CVD) and compared the profiles to subjects with low risk of CVD (LR-CVD). Our analysis revealed significant differences in the plasma and salivary proteins between the two groups. Pathway enrichment analysis of the differentially detected proteins revealed that the immune system activation and extracellular matrix remodeling are the most enriched pathways in the CVD-HR group. Comparing proteomic signatures between plasma and saliva, we found approximately 42 and 17 differentially expressed proteins associated with CVD-HR uniquely expressed in plasma and saliva respectively. Additionally, we identified eight common CVD-risk biomarkers shared between both plasma and saliva, demonstrating promising diagnostic tools for identifying individuals at high risk of developing CVD. In conclusion, saliva proteomics holds a significant promise to identify subjects with a high risk to develop CVD. Further studies are needed to validate our findings.

Head-to-Head Comparison of Aptamer- and Antibody-Based Proteomic Platforms in Human Cerebrospinal Fluid Samples from a Real-World Memory Clinic Cohort

High-throughput proteomic platforms are crucial to identify novel Alzheimer's disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

Emerging Biomarkers and Determinants of Lipoprotein Profiles to Predict CVD Risk: Implications for Precision Nutrition

Biomarkers constitute a valuable tool to diagnose both the incidence and the prevalence of chronic diseases and may help to inform the design and effectiveness of precision nutrition interventions. Cardiovascular disease (CVD) continues to be the foremost cause of death all over the world. While the reasons that lead to increased risk for CVD are multifactorial, dyslipidemias, plasma concentrations of specific lipoproteins, and dynamic measures of lipoprotein function are strong biomarkers to predict and document coronary heart disease incidence. The aim of this review is to provide a comprehensive evaluation of the biomarkers and emerging approaches that can be utilized to characterize lipoprotein profiles as predictive tools for assessing CVD risk, including the assessment of traditional clinical lipid panels, measures of lipoprotein efflux capacity and inflammatory and antioxidant activity, and omics-based characterization of lipoprotein composition and regulators of lipoprotein metabolism. In addition, we discuss demographic, genetic, metagenomic, and lifestyle determinants of lipoprotein profiles-such as age, sex, gene variants and single-nucleotide polymorphisms, gut microbiome profiles, dietary patterns, physical inactivity, obesity status, smoking and alcohol intake, and stress-which are likely to be essential factors to explain interindividual responses to precision nutrition recommendations to mitigate CVD risk.

Affinity-Enriched Plasma Proteomics for Biomarker Discovery in Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the weakening and dilation of the abdominal aorta. Few diagnostic biomarkers have been proposed for this condition. We performed mass spectrometry-based proteomics analysis of affinity-enriched plasma from 45 patients with AAA and 45 matched controls to identify changes to the plasma proteome and potential diagnostic biomarkers. Gene ontology analysis revealed a significant upregulation of the proteins involved in inflammation, coagulation, and extracellular matrix in AAA patients, while proteins related to angiogenesis were among those downregulated. Using recursive feature elimination, we identified a subset of 10 significantly regulated proteins that were highly predictive of AAA. A random forest classifier trained on these proteins achieved an area under the curve (AUC) of 0.93 [95% CI: 0.91-0.95] using cross-validation. Further validation in a larger cohort is necessary to confirm these results.

Risk prediction of ischemic heart disease using plasma proteomics, conventional risk factors and polygenic scores in Chinese and European adults

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841-0.868] vs. 0.845 [0.829-0.860] vs 0.553 [0.528-0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843-0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854-0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD.

Automated multiplexed affinity-based enrichment of peptides for LC-MS/MS plasma proteomics

Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SIGNIFICANCE: Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Soluble Proteoglycans and Proteoglycan Fragments as Biomarkers of Pathological Extracellular Matrix Remodeling

Proteoglycans and their proteolytic fragments diffuse into biological fluids such as plasma, serum, urine, or synovial fluid, where they can be detected by antibodies or mass-spectrometry. Neopeptides generated by the proteolysis of proteoglycans are recognized by specific neoepitope antibodies and can act as a proxy for the activity of certain proteases. Proteoglycan and proteoglycan fragments can be potentially used as prognostic, diagnostic, or theragnostic biomarkers for several diseases characterized by dysregulated extracellular matrix remodeling such as osteoarthritis, rheumatoid arthritis, atherosclerosis, thoracic aortic aneurysms, central nervous system disorders, viral infections, and cancer. Here, we review the main mechanisms accounting for the presence of soluble proteoglycans and their fragments in biological fluids, their potential application as diagnostic, prognostic, or theragnostic biomarkers, and highlight challenges and opportunities ahead of their clinical translation.

The Role of Furin and Its Therapeutic Potential in Cardiovascular Disease Risk

Furin is an important proteolytic enzyme, converting several proteins from inactive precursors to their active forms. Recently, proteo-genomic analyses in European and East Asian populations suggested a causal association of furin with ischaemic heart disease, and there is growing interest in its role in cardiovascular disease (CVD) aetiology. In this narrative review, we present a critical appraisal of evidence from population studies to assess furin's role in CVD risk and potential as a drug target for CVD. Whilst most observational studies report positive associations between furin expression and CVD risk, some studies report opposing effects, which may reflect the complex biological roles of furin and its substrates. Genetic variation in FURIN is also associated with CVD and its risk factors. We found no evidence of current clinical development of furin as a drug target for CVD, although several phase 1 and 2 clinical trials of furin inhibitors as a type of cancer immunotherapy have been completed. The growing field of proteo-genomics in large-scale population studies may inform the future development of furin and other potential drug targets to improve the treatment and prevention of CVD.

Integrative Analyses of Circulating Proteins and Metabolites Reveal Sex Differences in the Associations with Cardiac Function among DCM Patients

Dilated cardiomyopathy (DCM) is characterized by reduced left ventricular ejection fraction (LVEF) and left or biventricular dilatation. We evaluated sex-specific associations of circulating proteins and metabolites with structural and functional heart parameters in DCM. Plasma samples (297 men, 71 women) were analyzed for proteins using Olink assays (targeted analysis) or LC-MS/MS (untargeted analysis), and for metabolites using LC MS/MS (Biocrates AbsoluteIDQ p180 Kit). Associations of proteins (n = 571) or metabolites (n = 163) with LVEF, measured left ventricular end diastolic diameter (LVEDDmeasured), and the dilation percentage of LVEDD from the norm (LVEDDacc. to HENRY) were examined in combined and sex-specific regression models. To disclose protein-metabolite relations, correlation analyses were performed. Associations between proteins, metabolites and LVEF were restricted to men, while associations with LVEDD were absent in both sexes. Significant metabolites were validated in a second independent DCM cohort (93 men). Integrative analyses demonstrated close relations between altered proteins and metabolites involved in lipid metabolism, inflammation, and endothelial dysfunction with declining LVEF, with kynurenine as the most prominent finding. In DCM, the loss of cardiac function was reflected by circulating proteins and metabolites with sex-specific differences. Our integrative approach demonstrated that concurrently assessing specific proteins and metabolites might help us to gain insights into the alterations associated with DCM.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Post-translational modifications linked to preclinical Alzheimer's disease-related pathological and cognitive changes

INTRODUCTION

In this study, we leverage proteomic techniques to identify communities of proteins underlying Alzheimer's disease (AD) risk among clinically unimpaired (CU) older adults.

METHODS

We constructed a protein co-expression network using 3869 cerebrospinal fluid (CSF) proteins quantified by SomaLogic, Inc., in a cohort of participants along the AD clinical spectrum. We then replicated this network in an independent cohort of CU older adults and related these modules to clinically-relevant outcomes.

RESULTS

We discovered modules enriched for phosphorylation and ubiquitination that were associated with abnormal amyloid status, as well as p-tau181 (M4: β = 2.44, p < 0.001, M7: β = 2.57, p < 0.001) and executive function performance (M4: β = -2.00, p = 0.005, M7: β = -2.39, p < 0.001).

DISCUSSION

In leveraging CSF proteomic data from individuals spanning the clinical spectrum of AD, we highlight the importance of post-translational modifications for early cognitive and pathological changes.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

A Mass Spectrometry-Based Proteome Study of Twin Pairs Discordant for Incident Acute Myocardial Infarction within Three Years after Blood Sampling Suggests Novel Biomarkers

Acute myocardial infarction (AMI) is a major cause of mortality and morbidity worldwide, yet biomarkers for AMI in the short- or medium-term are lacking. We apply the discordant twin pair design, reducing genetic and environmental confounding, by linking nationwide registry data on AMI diagnoses to a survey of 12,349 twins, thereby identifying 39 twin pairs (48-79 years) discordant for their first-ever AMI within three years after blood sampling. Mass spectrometry of blood plasma identified 715 proteins. Among 363 proteins with a call rate > 50%, imputation and stratified Cox regression analysis revealed seven significant proteins (FDR < 0.05): FGD6, MCAM, and PIK3CB reflected an increased level in AMI twins relative to their non-AMI co-twins (HR > 1), while LBP, IGHV3-15, C1RL, and APOC4 reflected a decreased level in AMI twins relative to their non-AMI co-twins (HR < 1). Additional 50 proteins were nominally significant (p < 0.05), and bioinformatics analyses of all 57 proteins revealed biology within hemostasis, coagulation cascades, the immune system, and the extracellular matrix. A protein-protein-interaction network revealed Fibronectin 1 as a central hub. Finally, technical validation confirmed MCAM, LBP, C1RL, and APOC3. We put forward novel biomarkers for incident AMI, a part of the proteome field where markers are surprisingly rare and where additional studies are highly needed.

Exploration of potential biomarkers for early bladder cancer based on urine proteomics

Background

Bladder cancer is a common malignant tumor of the urinary system. The progression of the condition is associated with a poor prognosis, so it is necessary to identify new biomarkers to improve the diagnostic rate of bladder cancer.

Methods

In this study, 338 urine samples (144 bladder cancer, 123 healthy control, 32 cystitis, and 39 upper urinary tract cancer samples) were collected, among which 238 samples (discovery group) were analyzed by LC-MS. The urinary proteome characteristics of each group were compared with those of bladder cancer, and the differential proteins were defined by bioinformatics analysis. The pathways and functional enrichments were annotated. The selected proteins with the highest AUC score were used to construct a diagnostic panel. One hundred samples (validation group) were used to test the effect of the panel by ELISA.

Results

Compared with the healthy control, cystitis and upper urinary tract cancer samples, the number of differential proteins in the bladder cancer samples was 325, 158 and 473, respectively. The differentially expressed proteins were mainly related to lipid metabolism and iron metabolism and were involved in the proliferation, metabolism and necrosis of bladder cancer cells. The AUC of the panel of APOL1 and ITIH3 was 0.96 in the discovery group. ELISA detection showed an AUC of 0.92 in the validation group.

Conclusion

This study showed that urinary proteins can reflect the pathophysiological changes in bladder cancer and that important molecules can be used as biomarkers for bladder cancer screening. These findings will benefit the application of the urine proteome in clinical research.

Proteomics associated with coronary high-risk plaques by optical coherence tomography

Biomarkers are widely used for the diagnosis and monitoring of cardiovascular disease. However, markers for coronary high-risk plaques have not been identified. The aim of this study was to identify proteins specific to coronary high-risk plaques. Fifty-one patients (71.2 ± 11.1 years, male: 66.7%) who underwent intracoronary optical coherence tomography imaging and provided blood specimens for proteomic analysis were prospectively enrolled. A total of 1470 plasma proteins were analyzed per patient using the Olink® Explore 1536 Reagent Kit. In patients with thin-cap fibroatheroma, the protein expression of Calretinin (CALB2), Corticoliberin (CRH) and Alkaline phosphatase, placental type (ALPP) were significantly increased, while the expression of Neuroplastin (NPTN), Folate receptor gamma (FOLR3) and Serpin A12 (SERPINA12) were significantly decreased. In patients with macrophage infiltration, the protein expressions of Fatty acid-binding protein, intestinal (FABP2), and Fibroblast growth factor 21 (FGF21) were significantly decreased. In patients with lipid-rich plaques, the protein expression of Interleukin-17 C (IL17C) was significantly increased, while the expression of Fc receptor-like protein 3 (FCRL3) was significantly decreased. These proteins might be useful markers in identifying patients with coronary high-risk plaques. Clinical Trial Registration: https://www.umin.ac.jp/ctr/ , UMIN000041692.

Selective glomerular hypofiltration syndrome

The estimated glomerular filtration rate (eGFR) provides insight into cardiovascular disease (CVD) risk stratification and proactive management. Accumulating evidence suggests that combining eGFR calculated from serum cystatin C (eGFRcys) and from serum creatinine (eGFRcrea) improves CVD risk stratification over eGFRcrea alone. The term selective glomerular hypofiltration syndrome (SGHS) or shrunken pore syndrome has been proposed to define an eGFRcys:eGFRcrea ratio <1, which is hypothesized to result from a reduced glomerular filtration of 5- to 30-kDa molecules as compared with smaller molecules. SGHS may be identified in people with normal or reduced measured GFR, but the prevalence depends on the cut-off value of the eGFRcys:eGFRcrea ratio used, which is not yet standardized. SGHS is strongly associated with increased CVD and mortality risks and it may offer an opportunity to expand our understanding of the mechanisms linking GFR disorders with CVD risk (e.g. an altered plasma proteome), which may guide treatment decisions. However, muscle wasting may also contribute to a reduced eGFRcys:eGFRcrea ratio and there are open questions regarding the pathophysiology of a reduced eGFRcys:eGFRcrea ratio, the reference cut-off values of the ratio to define the syndrome and its clinical implications. We now critically review the SGHS concept, its pathophysiological basis and links to CVD and the potential consequences for clinical practice and propose a research agenda.

Plasma Proteomics to Identify Drug Targets for Ischemic Heart Disease

BACKGROUND

Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases.

OBJECTIVES

The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments.

METHODS

We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls).

RESULTS

Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD.

CONCLUSIONS

Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Blood Biomarkers for Healthy Aging

Measuring the abundance of biological molecules and their chemical modifications in blood and tissues has been the cornerstone of research and medical diagnoses for decades. Although the number and variety of molecules that can be measured have expanded exponentially, the blood biomarkers routinely assessed in medical practice remain limited to a few dozen, which have not substantially changed over the last 30-40 years. The discovery of novel biomarkers would allow, for example, risk stratification or monitoring of disease progression or the effectiveness of treatments and interventions, improving clinical practice in myriad ways. In this review, we combine the biomarker discovery concept with geroscience. Geroscience bridges aging research and translation to clinical applications by combining the framework of medical gerontology with high-technology medical research. With the development of geroscience and the rise of blood biomarkers, there has been a paradigm shift from disease prevention and cure to promoting health and healthy aging. New -omic technologies have played a role in the development of blood biomarkers, including epigenetic, proteomic, metabolomic, and lipidomic markers, which have emerged as correlates or predictors of health status, from disease to exceptional health.

High-Multiplex Aptamer-Based Serum Proteomics to Identify Candidate Serum Biomarkers of Oral Squamous Cell Carcinoma

Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies.

Correlation Between Cardiac Index, Plasma Troponin I, Myocardial Histopathology, CPB and AoX Duration in Glutamine versus No Glutamine Administered Patients with Low Ejection Fraction Undergoing Elective On-Pump CABG Surgery: Secondary Analysis of an RCT

Purpose

On-pump coronary artery bypass graft (CABG) causes myocardial ischemia, through the cardiopulmonary bypass (CPB) and aortic cross-clamping (AoX). Glutamine supplementation protects cardiac cells during cardiac ischemia. This study analysed the correlation between cardiac index (CI), plasma troponin I, myocardial histopathology, CPB and AoX duration in low ejection fraction patients receiving glutamine and no glutamine undergoing elective on-pump CABG.

Material and Methods

This was a secondary analysis of a double-blind, randomised controlled trial of 60 patients, split into control and intervention (glutamine) groups. Glutamine was administered at a dose of 0.5 g/kg/24 hours. There were 29 patients in each respective groups after a total of two patients dropped out.

Results

A negative correlation (p = 0.037) was observed between CPB duration and CI at 6 hours after CPB in the glutamine group. A positive correlation (p = 0.002) was also observed between AoX duration and plasma troponin I at 6 hours after CPB in the control group. However, no correlation was observed between myocardial histopathology and plasma troponin I level at 5 minutes after CPB.

Conclusion

Significant negative correlation between CPB duration and CI at 6 hours after CPB in the glutamine group, along with significant positive correlation between AoX duration and plasma troponin I level at 6 hours after CPB in the control group demonstrated the myocardial protection qualities of intravenous glutamine administration in patients with low ejection fraction undergoing elective on-pump CABG surgeries.

The Patent Ductus Arteriosus in Extremely Preterm Neonates Is More than a Hemodynamic Challenge: New Molecular Insights

Complications to preterm birth are numerous, including the presence of a patent ductus arteriosus (PDA). The biological understanding of the PDA is sparse and treatment remains controversial. Herein, we speculate whether the PDA is more than a cardiovascular imbalance, and may be a marker in response to immature core molecular and physiological processes driven by biological systems, such as inflammation. To achieve a new biological understanding of the PDA, we performed echocardiography and collected plasma samples on day 3 of life in 53 consecutively born neonates with a gestational age at birth below 28 completed weeks. The proteome of these samples was analyzed by mass spectrometry (nanoLC-MS/MS) and immunoassay of 17 cytokines and chemokines. We found differences in 21 proteins and 8 cytokines between neonates with a large PDA (>1.5 mm) compared to neonates without a PDA. Amongst others, we found increased levels of angiotensinogen, periostin, pro-inflammatory associations, including interleukin (IL)-1β and IL-8, and anti-inflammatory associations, including IL-1RA and IL-10. Levels of complement factors C8 and carboxypeptidases were decreased. Our findings associate the PDA with the renin-angiotensin-aldosterone system and immune- and complement systems, indicating that PDA goes beyond the persistence of a fetal circulatory connection of the great vessels.