Identifying Low-Abundance Biomarkers: Aptamer-Based Proteomics Potentially Enables More Sensitive Detection in Cardiovascular Diseases

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Proteomic aptamer analysis reveals serum biomarkers associated with disease mechanisms and phenotypes of systemic sclerosis

Background

Systemic sclerosis (SSc) is an autoimmune connective tissue disease that affects multiple organs, leading to elevated morbidity and mortality with limited treatment options. The early detection of organ involvement is challenging as there is currently no serum marker available to predict the progression of SSc. The aptamer technology proteomic analysis holds the potential to correlate SSc manifestations with serum proteins up to femtomolar concentrations.

Methods

This is a two-tier study of serum samples from women with SSc (including patients with interstitial lung disease - ILD - at high-resolution CT scan) and age-matched healthy controls (HC) that were first analyzed with aptamer-based proteomic analysis for over 1300 proteins. Proposed associated proteins were validated by ELISA first in an independent cohort of patients with SSc and HC, and selected proteins subject to further validation in two additional cohorts.

Results

The preliminary aptamer-based proteomic analysis identified 33 proteins with significantly different concentrations in SSc compared to HC sera and 9 associated with SSc-ILD, including proteins involved in extracellular matrix formation and cell-cell adhesion, angiogenesis, leukocyte recruitment, activation, and signaling. Further validations in independent cohorts ultimately confirmed the association of specific proteins with early SSc onset, specific organ involvement, and serum autoantibodies.

Conclusions

Our multi-tier proteomic analysis identified serum proteins discriminating patients with SSc and HC or associated with different SSc subsets, disease duration, and manifestations, including ILD, skin involvement, esophageal disease, and autoantibodies.

Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling

BACKGROUND

Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.

METHODS

In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.

RESULTS

The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population

Introduction

In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population.

Materials and Methods

We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity.

Results

Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75–0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54–0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61–0.83) and the specificity was 0.78 (95% CI, 0.66–0.90). The sensitivity was 0.71 (95% CI, 0.49–0.92) and the specificity was 0.74 (95% CI, 0.54–0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6–948.5; p = 0.03).

Conclusions

In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.

Detection of cancer biomarkers CA125 and CA199 via terahertz metasurface immunosensor

The cancer biomarkers including AFP, CEA, CA199 and CA125, are of great importance in the diagnosis, prognostic prediction and recurrence monitoring of malignancies. However, in clinical practical applications, most tumor cancer biomarkers are lack of sensitivity and specificity. In this study, we propose a terahertz (THz) metasurface (MS) immunosensor coupled with gold nanoparticles (AuNPs), which have good biocompatibility and high specific surface area for biomarkers. Firstly, we added AuNPs to the surface of the sensor. And then, the surface is modified with Anti-CA125 or Anti-CA199 to improve the sensitivity and specificity to the target antigen. The biosensor was fabricated using a surface micromachining process and characterized by a THz-time-domain spectroscopy (TDS) system. The sensitivity of the resonance frequency of the sensor to the refractive index was 65 GHz/RIU (refractive index unit). The detection performance of the THz immunosensor was also verified with different concentrations of CA125 and CA199. The experimental results showed that the frequency shift of the resonance peak was linearly related to the concentration of CA125 and CA199. The detection limits for both CA125 and CA199 are 0.01 U/ml, which is better than that of other common methods. Finally, serum samples were collected and detected to explore whether this method is suitable for clinical detection. The results are consistent with the results of antigen recognition. This study proves that the practicability of the THz immunosensor, which potentially provides important techniques and equipment for improving the sensitivity and specificity of cancer biomarkers.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive.

RESULTS

The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.

Omics phenotyping in heart failure: the next frontier

This state-of-the-art review aims to provide an up-to-date look at breakthrough omic technologies that are helping to unravel heart failure (HF) disease mechanisms and heterogeneity. Genomics, transcriptomics, proteomics, and metabolomics in HF are reviewed in depth. In addition, there is a thorough, expert discussion regarding the value of omics in identifying novel disease pathways, advancing understanding of disease mechanisms, differentiating HF phenotypes, yielding biomarkers for diagnosis or prognosis, or identifying new therapeutic targets in HF. The combination of multiple omics technologies may create a more comprehensive picture of the factors and physiology involved in HF than achieved by either one alone and provides a rich resource for predictive phenotype modelling. However, the successful translation of omics tools as solutions to clinical HF requires that the observations are robust and reproducible and can be validated across multiple independent populations to ensure confidence in clinical decision-making.

Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention

In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody- and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Aptamer-based proteomics of serum and plasma in acquired aplastic anemia

Single-stranded oligonucleotides containing deoxyuridine are aptamers (SOMAmers) that can bind proteins with high specificity and affinity and slow dissociation rates. SOMAscan, an aptamer-based proteomic technology, allows measurement of more than 1,300 proteins simultaneously for the identification of new disease biomarkers. The aim of the present study was to identify new serum and plasma protein markers for diagnosis of acquired aplastic anemia (AA) and response to immunosuppressive therapies (IST). SOMAscan was used to screen 1,141 serum proteins in 28 AA patients before and after therapy and 1,317 plasma proteins in seven SAA patients treated with standard IST and a thrombopoietin receptor agonist. From our analysis, 19 serum and 28 plasma proteins were identified as possible candidate diagnostic and prognostic markers. A custom immunobead-based multiplex assay with five selected serum proteins (BMP-10, CCL17, DKK1, HGF, and SELL) was used for validation in a verification set (n = 65) of samples obtained before and after IST and in a blinded validation cohort at baseline (n = 16). After technical validation, four biomarkers were employed to predict diagnosis (accuracy, 88%) and long-term response to IST (accuracy, 79%). In conclusion, SOMAscan is a powerful tool for the identification of new biomarkers. We propose further larger studies to validate new candidate serum and plasma diagnostic and prognostic markers of AA.

Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib

Supplemental Digital Content is available in the text.

Background:

Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.

Methods:

A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.

Results:

Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.

Conclusions:

Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.

Clinical Trial Registration:

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.

Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

Proteomics is a systems physiology discipline to address the large-scale characterization of protein species within a biological system, be it a cell, a tissue, a body biofluid, an organism, or a cohort population. Building on advances from chemical analytical platforms (e.g., mass spectrometry and other technologies), proteomics approaches have contributed powerful applications in cardiovascular biomedicine, most notably in: 1) the discovery of circulating protein biomarkers of heart diseases from plasma samples; and 2) the identification of disease mechanisms and potential therapeutic targets in cardiovascular tissues, in both preclinical models and translational studies. Contemporary proteomics investigations offer powerful means to simultaneously examine tens of thousands of proteins in various samples, and understand their molecular phenotypes in health and disease. This concise review introduces study design considerations, example applications and use cases, as well as interpretation and analysis of proteomics data in cardiovascular biomedicine.