Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

Capillary blood self-collection for high-throughput proteomics

In this study, we sought to compare protein concentrations obtained from a high-throughput proteomics platform (Olink) on samples collected using capillary blood self-collection (with the Tasso+ device) versus standard venipuncture (control). Blood collection was performed on 20 volunteers, including one sample obtained via venipuncture and two via capillary blood using the Tasso+ device. Tasso+ samples were stored at 2°C-8°C for 24-hs (Tasso-24) or 48-h (Tasso-48) prior to processing to simulate shipping times from a study participant's home. Proteomics were analyzed using Olink (384 Inflammatory Panel). Tasso+ blood collection was successful in 37/40 attempts. Of 230 proteins included in our analysis, Pearson correlations (r) and mean coefficient of variation (CV) between Tasso-24 or Tasso-48 versus venipuncture were variable. In the Tasso-24 analysis, 34 proteins (14.8%) had both a correlation r > 0.5 and CV < 0.20. In the Tasso-48 analysis, 68 proteins (29.6%) had a correlation r > 0.5 and CV < 0.20. Combining the Tasso-24 and Tasso-48 analyses, 26 (11.3%) proteins met these thresholds. We concluded that protein concentrations from Tasso+ samples processed 24-48 h after collection demonstrated wide technical variability and variable correlation with a venipuncture gold-standard. Use of home capillary blood self-collection for large-scale proteomics should be limited to select proteins with good agreement with venipuncture.

Identification of plasma protein markers of allergic disease risk: a mendelian randomization approach to proteomic analysis

BACKGROUND

While numerous allergy-related biomarkers and targeted treatment strategies have been developed and employed, there are still signifcant limitations and challenges in the early diagnosis and targeted treatment for allegic diseases. Our study aims to identify circulating proteins causally associated with allergic disease-related traits through Mendelian randomization (MR)-based analytical framework.

METHODS

Large-scale cis-MR was employed to estimate the effects of thousands of plasma proteins on five main allergic diseases. Additional analyses including MR Steiger analyzing and Bayesian colocalisation, were performed to test the robustness of the associations; These findings were further validated utilizing meta-analytical methods in the replication analysis. Both proteome- and transcriptome-wide association studies approach was applied, and then, a protein-protein interaction was conducted to examine the interplay between the identified proteins and the targets of existing medications.

RESULTS

Eleven plasma proteins were identified with links to atopic asthma (AA), atopic dermatitis (AD), and allergic rhinitis (AR). Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. MR analysis and extra validation revealed STAT6 and TNFRSF6B to be Tier 1 and IL1RL2 and IL6R to be Tier 2 proteins with the potential for AA treatment. Two Tier 1 proteins, CRAT and TNFRSF6B, and five Tier 2 proteins, ERBB3, IL6R, MMP12, ICAM1, and IL1RL2, were linked to AD, and three Tier 2 proteins, MANF, STAT6, and TNFSF8, to AR.

CONCLUSION

Eleven Tier 1 and 2 protein targets that are promising drug target candidates were identified for AA, AD, and AR, which influence the development of allergic diseases and expose new diagnostic and therapeutic targets.

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.

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.

Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.

Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.

Prediction of coronary artery disease using urinary proteomics

AIMS

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.

METHODS AND RESULTS

Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

CONCLUSION

A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.

Myeloperoxidase is a critical mediator of anthracycline-induced cardiomyopathy

Cardiotoxicity is a major complication of anthracycline therapy that negatively impacts prognosis. Effective pharmacotherapies for prevention of anthracycline-induced cardiomyopathy (AICM) are currently lacking. Increased plasma levels of the neutrophil-derived enzyme myeloperoxidase (MPO) predict occurrence of AICM in humans. We hypothesized that MPO release causally contributes to AICM. Mice intravenously injected with the anthracycline doxorubicin (DOX) exhibited higher neutrophil counts and MPO levels in the circulation and cardiac tissue compared to saline (NaCl)-treated controls. Neutrophil-like HL-60 cells exhibited increased MPO release upon exposition to DOX. DOX induced extensive nitrosative stress in cardiac tissue alongside with increased carbonylation of sarcomeric proteins in wildtype but not in Mpo-/- mice. Accordingly, co-treatment of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) with DOX and MPO aggravated loss of hiPSC-CM-contractility compared to DOX treatment alone. DOX-treated animals exhibited pronounced cardiac apoptosis and inflammation, which was attenuated in MPO-deficient animals. Finally, genetic MPO deficiency and pharmacological MPO inhibition protected mice from the development of AICM. The anticancer efficacy of DOX was unaffected by MPO deficiency. Herein we identify MPO as a critical mediator of AICM. We demonstrate that DOX induces cardiac neutrophil infiltration and release of MPO, which directly impairs cardiac contractility through promoting oxidation of sarcomeric proteins, cardiac inflammation and cardiomyocyte apoptosis. MPO thus emerges as a promising pharmacological target for prevention of AICM.

Proteome-Wide Mendelian Randomization Analysis Identified Potential Drug Targets for Atrial Fibrillation

Background Finding effective and safe therapeutic drugs for atrial fibrillation (AF) is an important concern for clinicians. Proteome-wide Mendelian randomization analysis provides new ideas for finding potential drug targets. Methods and Results Using a proteome-wide Mendelian randomization approach, we assessed the genetic predictive causality between thousands of proteins and AF risk and found that genetically predicted plasma levels of phosphomevalonate kinase, tumor necrosis factor ligand superfamily member 12, sulfhydryl oxidase 2, interleukin-6 receptor subunit alpha, and low-affinity immunoglobulin gamma Fc region receptor II-b might decrease AF risk, while genetically predicted plasma levels of beta-mannosidase, collagen alpha-1(XV) chain, ANXA4 (annexin A4), COF2 (cofilin-2), and RAB1A (Ras-related protein Rab-1A) might increase AF risk (P<3.4×10-5). By using different Mendelian randomization methods and instrumental variable selection thresholds, we performed sensitivity analyses in 30 scenarios to test the robustness of positive findings. Replication analyses were also performed in independent samples to further avoid false-positive findings. Drugs targeting tumor necrosis factor ligand superfamily member 12, interleukin-6 receptor subunit alpha, low-affinity immunoglobulin gamma Fc region receptor II-b, and annexin A4 are approved or in development. The results of the phenome-wide Mendelian randomization analysis showed that changing the plasma levels of phosphomevalonate kinase, cofilin-2, annexin A4, Ras-related protein Rab-1A, sulfhydryl oxidase 2, and collagen alpha-1(XV) chain did not increase the risk of other diseases while decreasing the risk of AF. Conclusions We found a significant causal association between genetically predicted levels of 10 plasma proteins and AF risk. Four of these proteins have drugs targeting them that are approved or in development, and our results suggest the potential for these drugs to treat AF or cause AF. Sulfhydryl oxidase 2, low-affinity immunoglobulin gamma Fc region receptor II-b, and beta-mannosidase have not been suggested by previous laboratory or epidemiological studies to be associated with AF and may reveal new pathophysiological pathways as well as therapeutic targets for AF.

Serum proteome profiles in patients treated with targeted temperature management after out-of-hospital cardiac arrest

BACKGROUND

Definition of temporal serum proteome profiles after out-of-hospital cardiac arrest may identify biological processes associated with severe hypoxia-ischaemia and reperfusion. It may further explore intervention effects for new mechanistic insights, identify candidate prognostic protein biomarkers and potential therapeutic targets. This pilot study aimed to investigate serum proteome profiles from unconscious patients admitted to hospital after out-of-hospital cardiac arrest according to temperature treatment and neurological outcome.

METHODS

Serum samples at 24, 48, and 72 h after cardiac arrest at three centres included in the Target Temperature Management after out-of-hospital cardiac arrest trial underwent data-independent acquisition mass spectrometry analysis (DIA-MS) to find changes in serum protein concentrations associated with neurological outcome at 6-month follow-up and targeted temperature management (TTM) at 33 °C as compared to 36 °C. Neurological outcome was defined according to Cerebral Performance Category (CPC) scale as "good" (CPC 1-2, good cerebral performance or moderate disability) or "poor" (CPC 3-5, severe disability, unresponsive wakefulness syndrome, or death).

RESULTS

Of 78 included patients [mean age 66 ± 12 years, 62 (80.0%) male], 37 (47.4%) were randomised to TTM at 36 °C. Six-month outcome was poor in 47 (60.3%) patients. The DIA-MS analysis identified and quantified 403 unique human proteins. Differential protein abundance testing comparing poor to good outcome showed 19 elevated proteins in patients with poor outcome (log₂-fold change (FC) range 0.28-1.17) and 16 reduced proteins (log₂(FC) between - 0.22 and - 0.68), involved in inflammatory/immune responses and apoptotic signalling pathways for poor outcome and proteolysis for good outcome. Analysis according to level of TTM showed a significant protein abundance difference for six proteins [five elevated proteins in TTM 36 °C (log₂(FC) between 0.33 and 0.88), one reduced protein (log₂(FC) - 0.6)] mainly involved in inflammatory/immune responses only at 48 h after cardiac arrest.

CONCLUSIONS

Serum proteome profiling revealed an increase in inflammatory/immune responses and apoptosis in patients with poor outcome. In patients with good outcome, an increase in proteolysis was observed, whereas TTM-level only had a modest effect on the proteome profiles. Further validation of the differentially abundant proteins in response to neurological outcome is necessary to validate novel biomarker candidates that may predict prognosis after cardiac arrest.

Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.

Proteomic profiling identifies novel independent relationships between inflammatory proteins and myocardial infarction

BACKGROUND

Inflammation has been implicated in the pathogenesis of coronary heart disease, but the relevance and independence of individual inflammatory proteins is uncertain.

OBJECTIVE

To examine the relationships between a spectrum of inflammatory proteins and myocardial infarction (MI).

METHODS AND RESULTS

A panel of 92 inflammatory proteins was assessed using an OLINK multiplex immunoassay among 432 MI cases (diagnosed < 66 years) and 323 controls. Logistic regression was used to estimate associations between individual proteins and MI, after adjustment for established cardiovascular risk factors and medication use, and stepwise regression to identify proteins with independent effects. Machine learning techniques (Boruta analysis and LASSO regression) and bioinformatic resources were used to examine the concordance of results with those obtained by conventional methods and explore the underlying biological processes to inform the validity of the associations. Among the 92 proteins studied, 62 (67%) had plasma concentrations above the lower limit of detection in at least 50% of samples. Of these, 15 individual proteins were significantly associated with MI after covariate adjustment and correction for multiple testing. Five of these 15 proteins (CDCP1, CD6, IL1-8R1, IL-6, and CXCL1) were independently associated with MI, with up to three-fold higher risks of MI per doubling in plasma concentrations. Findings were further validated using machine learning techniques and biologically focused analyses.

CONCLUSIONS

This study, demonstrating independent relationships between five inflammatory proteins and MI, provides important novel insights into the inflammatory hypothesis of MI and the potential utility of proteomic analyses in precision medicine.

AI Machine Learning Technique Characterizes Potential Markers of Depression in Two Animal Models of Depression

(1) Background: there is an urgent clinical need for rapid and effective antidepressants. (2) Methods: We employed proteomics to profile proteins in two animal models (n = 48) of Chronic Unpredictable Stress and Chronic Social Defeat Stress. Additionally, partial least squares projection to latent structure discriminant analysis and machine learning were used to distinguish the models and the healthy control, extract and select protein features and build biomarker panels for the identification of different mouse models of depression. (3) Results: The two depression models were significantly different from the healthy control, and there were common changes in proteins in the depression-related brain regions of the two models; i.e., SRCN1 was down-regulated in the dorsal raphe nucleus in both models of depression. Additionally, SYIM was up-regulated in the medial prefrontal cortex in the two depression models. Bioinformatics analysis suggested that perturbed proteins are involved in energy metabolism, nerve projection, etc. Further examination confirmed that the trends of feature proteins were consistent with mRNA expression levels. (4) Conclusions: To the best of our knowledge, this is the first study to probe new targets of depression in multiple brain regions of two typical models of depression, which could be targets worthy of study.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Association between proteomic biomarkers and myocardial fibrosis measured by MRI: the multi-ethnic study of atherosclerosis

BACKGROUND

Cardiac magnetic resonance imaging (CMR) determines the extent of interstitial fibrosis, measured by increased extracellular volume (ECV), and replacement fibrosis with late gadolinium myocardial enhancement (LGE). Despite advances in detection, the pathophysiology of subclinical myocardial fibrosis is incompletely understood. Targeted proteomic discovery technologies enable quantification of low abundance circulating proteins to elucidate cardiac fibrosis mechanisms.

METHODS

Using a cross-sectional design, we selected 92 LGE+ cases and 92 LGE- demographically matched controls from the Multi-Ethnic Study of Atherosclerosis. Similarly, we selected 156 cases from the highest ECV quartile and matched with 156 cases from the lowest quartile. The plasma serum proteome was analyzed using proximity extension assays to determine differential regulation of 92 proteins previously implicated with cardiovascular disease. Results were analyzed using volcano plots of statistical significance vs. magnitude of change and Bayesian additive regression tree (BART) models to determine importance.

FINDINGS

After adjusting for false discovery, higher ECV was significantly associated with 17 proteins. Using BART, Plasminogen activator inhibitor 1, Insulin-like growth factor-binding protein 1, and N-terminal pro-B-type natriuretic peptide were associated with higher ECV after accounting for other proteins and traditional cardiovascular risk factors. In contrast, no circulating proteins were associated with replacement fibrosis.

INTERPRETATIONS

Our results suggest unique circulating proteomic signatures associated with interstitial fibrosis emphasizing its systemic influences. With future validation, protein panels may identify patients who may develop interstitial fibrosis with progression to heart failure.

FUNDING

This research was supported by contracts and grants from NHLBI, NCATS and the Inova Heart and Vascular Institute.

Estimating the causal effects of genetically predicted plasma proteome on heart failure

Background

Heart Failure (HF) is the end-stage cardiovascular syndrome with poor prognosis. Proteomics holds great promise in the discovery of novel biomarkers and therapeutic targets for HF. The aim of this study is to investigate the causal effects of genetically predicted plasma proteome on HF using the Mendelian randomization (MR) approach.

Methods

Summary-level data for the plasma proteome (3,301 healthy individuals) and HF (47,309 cases; 930,014 controls) were extracted from genome-wide association studies (GWASs) of European descent. MR associations were obtained using the inverse variance-weighted (IVW) method, sensitivity analyses, and multivariable MR analyses.

Results

Using single-nucleotide polymorphisms as instrumental variables, 1-SD increase in MET level was associated with an approximately 10% decreased risk of HF (odds ratio [OR]: 0.92; 95% confidence interval [CI]: 0.89 to 0.95; p = 1.42 × 10^-6), whereas increases in the levels of CD209 (OR: 1.04; 95% CI: 1.02-1.06; p = 6.67 × 10^-6) and USP25 (OR: 1.06; 95% CI: 1.03-1.08; p = 7.83 × 10^-6) were associated with an increased risk of HF. The causal associations were robust in sensitivity analyses, and no evidence of pleiotropy was observed.

Conclusion

The study findings suggest that the hepatocyte growth factor/c-MET signaling pathway, dendritic cells-mediated immune processes, and ubiquitin-proteasome system pathway are involved in the pathogenesis of HF. Moreover, the identified proteins have potential to uncover novel therapies for cardiovascular diseases.

Sericin-mediated improvement of dysmorphic cardiac mitochondria from hypercholesterolaemia is associated with maintaining mitochondrial dynamics, energy production, and mitochondrial structure

CONTEXT

Sericin is a component protein in the silkworm cocoon [Bombyx mori Linnaeus (Bombycidae)] that improves dysmorphic cardiac mitochondria under hypercholesterolemic conditions. This is the first study to explore cardiac mitochondrial proteins associated with sericin treatment.

OBJECTIVE

To investigate the mechanism of action of sericin in cardiac mitochondria under hypercholesterolaemia.

MATERIALS AND METHODS

Hypercholesterolaemia was induced in Wistar rats by feeding them 6% cholesterol-containing chow for 6 weeks. The hypercholesterolemic rats were separated into 2 groups (n = 6 for each): the sericin-treated (1,000 mg/kg daily) and nontreated groups. The treatment conditions were maintained for 4 weeks prior to cardiac mitochondria isolation. The mitochondrial structure was evaluated by immunolabeling electron microscopy, and differential mitochondrial protein expression was determined and quantitated by two-dimensional gel electrophoresis coupled with mass spectrometry.

RESULTS

A 32.22 ± 2.9% increase in the percent striated area of cardiac muscle was observed in sericin-treated hypercholesterolemic rats compared to the nontreatment group (4.18 ± 1.11%). Alterations in mitochondrial proteins, including upregulation of optic atrophy 1 (OPA1) and reduction of NADH-ubiquinone oxidoreductase 75 kDa subunit (NDUFS1) expression, are correlated with a reduction in mitochondrial apoptosis under sericin treatment. Differential proteomic observation also revealed that sericin may improve mitochondrial energy production by upregulating acetyl-CoA acetyltransferase (ACAT1) and NADH dehydrogenase 1α subcomplex subunit 10 (NDUFA10) expression.

DISCUSSION AND CONCLUSIONS

Sericin treatment could improve the dysmorphic mitochondrial structure, metabolism, and energy production of cardiac mitochondria under hypercholesterolaemia. These results suggest that sericin may be an alternative treatment molecule that is related to cardiac mitochondrial abnormalities.

Development of Single-Cell Transcriptomics and Its Application in COVID-19

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

Biomarkers in HFpEF for Diagnosis, Prognosis, and Biological Phenotyping

PURPOSE OF REVIEW

The heterogeneity of heart failure with preserved ejection fraction (HFpEF) is responsible for the limited success of broad management strategies. The role of biomarkers has been evolving helping to provide insight into the diversity of pathophysiology, prognosis, and potential targets for treatments. We will review the role of traditional and novel biomarkers in diagnosing, prognosticating, and evolving the management of patients with HFpEF. As circulating biomarker discovery rapidly evolves, we will explore technology for new biomarker discovery with examples of successful implementation.

RECENT FINDINGS

Besides cardiac-specific biomarkers (natriuretic peptides and troponin), other novel nonspecific biomarkers increasingly identify the diversity of pathophysiological mechanisms of HFpEF including inflammation, fibrosis, and renal dysfunction. Newer approaches have provided increasing granularity providing opportunities to integrate large amounts of information from proteomics and genomics as biomarkers of interest in HFpEF. HFpEF has been marked with failure of many medications to show benefit, whether measuring single targeted biomarkers or broader targeted discovery proteomics or genomic circulating biomarkers are providing increasing opportunities to better understand and manage HFpEF patients.

Ischemic preconditioning/ischemic postconditioning alleviates anoxia/reoxygenation injury via the Notch1/Hes1/VDAC1 axis

Ischemic preconditioning (IPC), and ischemic postconditioning (IPost) have a significant protective effect on myocardial ischemia/reperfusion (MI/R) injury by alleviating oxidative stress and mitochondrial disturbances, although the underlying molecular mechanisms are unclear. The study was to demonstrate that cardioprotection against anoxia/reoxygenation (A/R) injury is transduced via the Notch1/Hes1/VDAC1 signaling pathway. Using mass spectrometry and tandem affinity purification (TAP), to screen for differentially expressed proteins associated with Hes1, followed by standard bioinformatics analysis. The co-immunoprecipitation (Co-IP) assay confirmed an interaction between Hes1 and VDAC1 proteins. H9c2 cells were transfected with Hes1 adenoviral N-terminal TAP vector (AD-NTAP/Hes1) and Hes1-short hairpin RNA adenoviral vector (AD-Hes1-shRNA) to establish A/R injury, IPC, and IPost models, respectively. The expression of Hes1 and VDAC1 proteins were measured by western blot analysis, while the levels of reactive oxygen species (ROS), mitochondrial membrane potential (ΔΨm), and apoptosis were evaluated by flow cytometry. AD-NTAP/Hes1 can activate the exogenous protein expression of Hes1, thus decreasing creatine phosphokinase (CPK) and lactate dehydrogenase (LDH) activity and promoting cell viability. The study found that VDAC1 was a potential target protein for Hes1 and the overexpression of Hes1 protein expression downregulated protein expression levels of VDAC1, reduced ROS production, stabilized ΔΨm, and inhibited apoptosis in H9c2 cells. Additionally, downregulation of Hes1 protein expression also upregulated VDAC1 protein expression, increased ROS production, imbalanced ΔΨm, promoted cell apoptosis, and attenuated the cardioprotection afforded by IPC and IPost. The Notch1/Hes1 signaling pathway activated by IPC/IPost can directly downregulate the protein expression of VDAC1 and consequently relieve A/R injury.

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.

METHODS AND RESULTS

We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.

CONCLUSIONS

This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.

Mendelian randomization analyses reveal novel drug targets for anorexia nervosa

Among all psychiatric disorders, anorexia nervosa (AN) has the highest mortality rate. However, there is still no pharmacological therapy for AN. The human plasma proteome may be a great cornerstone for the development of new drugs against AN. Here we performed a Mendelian randomization (MR) analysis to identify causal risk proteins for AN. Exposure data were extracted from a large genome-wide association study (GWAS) of 2994 plasma proteins in 3301 subjects of European descent, while outcome data were obtained from another GWAS of AN (16,992 cases and 55,525 controls of European descent). MR analyses were performed using the inverse-variance weighted (IVW) method and other sensitivity analysis methods. Using single nucleotide polymorphisms as instruments, this study suggested that high TXNDC12 levels were associated with a higher risk of AN (IVW Odd's ratio [OR]: 1.12; 95% confidence interval [CI]: 1.08-1.16; P = 2.35 × 10^-10), while another protein ADH1B showed the opposite effect (IVW OR: 0.89; 95% CI: 0.85-0.93; P = 2.99 × 10^-7). The causal associations were robust in multivariable models, genome-wide significant models, and with additional MR methods. No pleiotropy was observed. Our findings suggest that TXNDC12 was associated with a high risk of AN, while AHD1B was associated with a low risk of AN. They might both have implications in AN by regulating the brain dopamine reward system. In combination with existing knowledge on AN, these proteins may be novel drug targets for AN treatment.

Functional and Molecular Characterisation of Heart Failure Progression in Mice and the Role of Myosin Regulatory Light Chains in the Recovery of Cardiac Muscle Function

Heart failure (HF) as a result of myocardial infarction (MI) is a major cause of fatality worldwide. However, the cause of cardiac dysfunction succeeding MI has not been elucidated at a sarcomeric level. Thus, studying the alterations within the sarcomere is necessary to gain insights on the fundamental mechansims leading to HF and potentially uncover appropriate therapeutic targets. Since existing research portrays regulatory light chains (RLC) to be mediators of cardiac muscle contraction in both human and animal models, its role was further explored In this study, a detailed characterisation of the physiological changes (i.e., isometric force, calcium sensitivity and sarcomeric protein phosphorylation) was assessed in an MI mouse model, between 2D (2 days) and 28D post-MI, and the changes were related to the phosphorylation status of RLCs. MI mouse models were created via complete ligation of left anterior descending (LAD) coronary artery. Left ventricular (LV) papillary muscles were isolated and permeabilised for isometric force and Ca²⁺ sensitivity measurement, while the LV myocardium was used to assay sarcomeric proteins’ (RLC, troponin I (TnI) and myosin binding protein-C (MyBP-C)) phosphorylation levels and enzyme (myosin light chain kinase (MLCK), zipper interacting protein kinase (ZIPK) and myosin phosphatase target subunit 2 (MYPT2)) expression levels. Finally, the potential for improving the contractility of diseased cardiac papillary fibres via the enhancement of RLC phosphorylation levels was investigated by employing RLC exchange methods, in vitro. RLC phosphorylation and isometric force potentiation were enhanced in the compensatory phase and decreased in the decompensatory phase of HF failure progression, respectively. There was no significant time-lag between the changes in RLC phosphorylation and isometric force during HF progression, suggesting that changes in RLC phosphorylation immediately affect force generation. Additionally, the in vitro increase in RLC phosphorylation levels in 14D post-MI muscle segments (decompensatory stage) enhanced its force of isometric contraction, substantiating its potential in HF treatment. Longitudinal observation unveils potential mechanisms involving MyBP-C and key enzymes regulating RLC phosphorylation, such as MLCK and MYPT2 (subunit of MLCP), during HF progression. This study primarily demonstrates that RLC phosphorylation is a key sarcomeric protein modification modulating cardiac function. This substantiates the possibility of using RLCs and their associated enzymes to treat HF.

Nandrolone combined with strenuous resistance training impairs myocardial proteome profile of rats

We aimed to investigate the effects of high doses of nandrolone decanoate and resistance training (RT) on the proteomic profile of the left ventricle (LV) of rats, using a label-free quantitative approach. Male rats were randomized into four groups: untrained vehicle (UTV), trained vehicle (TV), untrained nandrolone (UTN), and trained nandrolone (TN). Rats were familiarized with the exercise training protocol (jump exercise) for one week. Jump-exercise was performed five days a week for 6 weeks, with 30 s of inter-set rest intervals. Nandrolone was administrated for 6 weeks (5 mg/kg, twice a week, via intramuscular). Systolic and diastolic arterial pressure and heart rate were measured 48 h post-training. LV was isolated and collagen content was measured. The expression of cardiac proteins was analyzed by ultra-efficiency liquid chromatography with mass spectrometry high / low collision energy (UPLC/MS^E). Nandrolone and RT led to cardiac hypertrophy, even though high doses of nandrolone counteracted the RT-induced arterial pressures lowering. Nandrolone also affected the proteome profile negatively in LV of rats, including critical proteins related to biological processes (metabolism, oxidative stress, inflammation), structural function and membrane transporters. Our findings show physiological relevance since high doses of nandrolone induced detrimental effects on the proteome profile of heart tissue and hemodynamic parameters of rats. Furthermore, as nandrolone abuse has become increasingly common among recreational athletes and casual fitness enthusiasts, we consider that our findings have clinical relevance as well.

Dysregulated lncRNA and mRNA may promote the progression of ischemic stroke via immune and inflammatory pathways: results from RNA sequencing and bioinformatics analysis

BACKGROUND

Long non-coding RNAs (lncRNAs) are widely involved in gene transcription regulation and which act as epigenetic modifiers in many diseases.

OBJECTIVE

To determine whether lncRNAs are involved in ischemic stroke (IS), we analyzed the expression profile of lncRNAs and mRNAs in IS.

METHODS

RNA sequencing was performed on the blood of three pairs of IS patients and healthy controls. Differential expression analysis was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Based on the co-expression relationships between lncRNA and mRNA, a series of bioinformatics analysis including GO and KEGG enrichment analysis and PPI analysis, were conducted to predict the function of lncRNA.

RESULTS

RNA sequencing produced a total of 5 DElncRNAs and 144 DEmRNAs. Influenza A pathway and Herpes simplex infection pathway were the most significant pathways. EP300 and NFKB1 were the most important target proteins, and Human leucocyte antigen (HLA) family were the key genes in IS.

CONCLUSIONS

Analysis of this study revealed that dysregulated lncRNAs in IS may lead to IS by affecting the immune and inflammation system.

Cardiovascular mechanobiology-a Special Issue to look at the state of the art and the newest insights into the role of mechanical forces in cardiovascular development, physiology and disease

There has been much progress recently in the area of cardiovascular mechanobiology and this Special Issue aims at taking stock. This editorial gives context of the main motivation for this special issue as well as a brief summary of its content.

Proteins as Mediators of the Association Between Diet Quality and Incident Cardiovascular Disease and All-Cause Mortality: The Framingham Heart Study

Background

Biological mechanisms underlying the association of a healthy diet with chronic diseases remain unclear. Targeted proteomics may facilitate the understanding of mechanisms linking diet to chronic diseases.

Methods and Results

We examined 6360 participants (mean age 50 years; 54% women) in the Framingham Heart Study. The associations between diet and 71 cardiovascular disease (CVD)‐related proteins were examined using 3 diet quality scores: the Alternate Healthy Eating Index, the modified Mediterranean‐style Diet Score, and the modified Dietary Approaches to Stop Hypertension diet score. A mediation analysis was conducted to examine which proteins mediated the associations of diet with incident CVD and all‐cause mortality. Thirty of the 71 proteins were associated with at least 1 diet quality score (P<0.0007) after adjustment for multiple covariates in all study participants and confirmed by an internal validation analysis. Gene ontology analysis identified inflammation‐related pathways such as regulation of cell killing and neuroinflammatory response (Bonferroni corrected P<0.05). During a median follow‐up of 13 years, we documented 512 deaths and 488 incident CVD events. Higher diet quality scores were associated with lower risk of CVD (P≤0.03) and mortality (P≤0.004). After adjusting for multiple potential confounders, 4 proteins (B2M [beta‐2‐microglobulin], GDF15 [growth differentiation factor 15], sICAM1 [soluble intercellular adhesion molecule 1], and UCMGP [uncarboxylated matrix Gla‐protein]) mediated the association between at least 1 diet quality score and all‐cause mortality (median proportion of mediation ranged from 8.6% to 25.9%). We also observed that GDF15 mediated the association of the Alternate Healthy Eating Index with CVD (median proportion of mediation: 8.6%).

Conclusions

Diet quality is associated with new‐onset CVD and mortality and with circulating CVD‐related proteins. Several proteins appear to mediate the association of diet with these outcomes.

Circulating PGLYRP1 Levels as a Potential Biomarker for Coronary Artery Disease and Heart Failure

ABSTRACT

Coronary artery disease (CAD) and associated comorbidities such as heart failure (HF) remain the leading cause of morbidity and mortality worldwide attributed to, at least partially, the lack of biomarkers for efficient disease diagnosis. Here, we evaluated the diagnostic potential of serum peptidoglycan recognition protein 1 (PGLYRP1), an important component of the innate immunity and inflammation system, for both CAD and HF. A machine-learning method (random forest) was used to evaluate the clinical utility of circulating PGLYRP1 for diagnosis of CAD and HF in a total of 370 individuals. Causal links of chronic serum PGLYRP1 elevation to both diseases were further explored in ApoE-/- mice. The serum levels of PGLYRP1 were significantly higher in individuals with either chronic CAD or acute coronary syndrome than those in those without coronary artery stenosis (the control group) and even more pronounced in CAD individuals with concomitant HF. Our random forest classifier revealed that this protein performed better than other recommended clinical indicators in distinguishing the CAD from the control individuals. In addition, this protein associates more with the biomarkers of HF including left ventricular ejection fraction than inflammation. Notably, our mice experiment indicated that long-term treatment with recombinant PGLYRP1 could significantly impair the cardiovascular system as reflected from both increased atherogenic lesions and reduced fractional shortening of the left ventricle. Our findings, therefore, supported the circulating levels of PGLYRP1 as a valuable biomarker for both CAD and HF.

Differential Plasma Protein Regulation and Statin Effects in Human Immunodeficiency Virus (HIV)-Infected and Non-HIV-Infected Patients Utilizing a Proteomics Approach

BACKGROUND

People with human immunodeficiency virus (PWH) demonstrate increased atherosclerotic cardiovascular disease (ASCVD). Statins are being studied to prevent ASCVD in human immunodeficiency virus (HIV), but little is known regarding the effects of statins on a broad range of inflammatory and cardiovascular proteins in this population.

METHODS

We used a highly specific discovery proteomic approach (Protein Extension Assay), to determine statin effects on over 350 plasma proteins in relevant ASCVD pathways among HIV and non-HIV groups. Responses to pitavastatin calcium were assessed in 89 PWH in the INTREPID trial and 46 non-HIV participants with features of central adiposity and insulin resistance. History of cardiovascular disease was exclusionary for both studies.

RESULTS

Among participants with HIV, PCOLCE (enzymatic cleavage of type I procollagen) significantly increased after pitavastatin therapy and PLA2G7 (systemic marker of arterial inflammation) decreased. Among participants without HIV, integrin subunit alpha M (integrin adhesive function) and defensin alpha-1 (neutrophil function) increased after pitavastatin therapy and PLA2G7 decreased. At baseline, comparing participants with and without HIV, differentially expressed proteins included proteins involved in platelet and endothelial function and immune activation.

CONCLUSIONS

Pitavastatin affected proteins important to platelet and endothelial function and immune activation, and effects differed to a degree within PWH and participants without HIV.

Biomarker Discovery of Acute Coronary Syndrome Using Proteomic Approach

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.

The Human Explanted Heart Program: A translational bridge for cardiovascular medicine

The progression of cardiovascular research is often impeded by the lack of reliable disease models that fully recapitulate the pathogenesis in humans. These limitations apply to both in vitro models such as cell-based cultures and in vivo animal models which invariably are limited to simulate the complexity of cardiovascular disease in humans. Implementing human heart tissue in cardiovascular research complements our research strategy using preclinical models. We established the Human Explanted Heart Program (HELP) which integrates clinical, tissue and molecular phenotyping thereby providing a comprehensive evaluation into human heart disease. Our collection and storage of biospecimens allow them to retain key pathogenic findings while providing novel insights into human heart failure. The use of human non-failing control explanted hearts provides a valuable comparison group for the diseased explanted hearts. Using HELP we have been able to create a tissue repository which have been used for genetic, molecular, cellular, and histological studies. This review describes the process of collection and use of explanted human heart specimens encompassing a spectrum of pediatric and adult heart diseases, while highlighting the role of these invaluable specimens in translational research. Furthermore, we highlight the efficient procurement and bio-preservation approaches ensuring analytical quality of heart specimens acquired in the context of heart donation and transplantation.

Omics and Cardiometabolic Disease Risk Prediction

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.

Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives

The increasing application of single-cell RNA sequencing (scRNA-seq) technology in life science and biomedical research has significantly increased our understanding of the cellular heterogeneities in immunology, oncology and developmental biology. This review will summarize the development of various scRNA-seq technologies; primarily discussing the application of scRNA-seq on infectious diseases, and exploring the current development, challenges, and potential applications of scRNA-seq technology in the future.

Big Data Approaches in Heart Failure Research

PURPOSE OF REVIEW

The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential.

RECENT FINDINGS

Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Serum sCD14, PGLYRP2 and FGA as potential biomarkers for multidrug-resistant tuberculosis based on data-independent acquisition and targeted proteomics

Multidrug‐resistant tuberculosis (MDR‐TB), defined as tuberculosis (TB) resistant to at least isoniazid and rifampicin, is a major concern of TB control worldwide. However, the diagnosis of MDR‐TB remains a huge challenge to its prevention and control. To identify new diagnostic methods for MDR‐TB, a mass spectrometry strategy of data‐independent acquisition and parallel reaction monitoring was used to detect and validate differential serum proteins. The bioinformatic analysis showed that the functions of differential serum proteins between the MDR‐TB group and the drug‐sensitive tuberculosis group were significantly correlated to the complement coagulation cascade, surface adhesion and extracellular matrix receptor interaction, suggesting a disorder of coagulation in TB. Here, we identified three potential candidate biomarkers such as sCD14, PGLYRP2 and FGA, and established a diagnostic model using these three candidate biomarkers with a sensitivity of 81.2%, a specificity of 90% and the area under the curve value of 0.934 in receiver operation characteristics curve to diagnose MDR‐TB. Our study has paved the way for a novel method to diagnose MDR‐TB and may contribute to elucidate the mechanisms underlying MDR‐TB.

Proteomics-Enriched Prediction Model for Poor Neurologic Outcome in Cardiac Arrest Survivors

OBJECTIVES

Neurologic outcome prediction in out-of-hospital cardiac arrest survivors is highly limited due to the lack of consistent predictors of clinically relevant brain damage. The present study aimed to identify novel biomarkers of neurologic recovery to improve early prediction of neurologic outcome.

DESIGN

Prospective, single-center study, SETTING:: University-affiliated tertiary care center.

PATIENTS

We prospectively enrolled 96 out-of-hospital cardiac arrest survivors into our study.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Neurologic outcome was assessed by the Cerebral Performance Categories score. To identify plasma biomarkers for poor neurologic outcome (Cerebral Performance Categories score ≥ 3), we performed a three-step proteomics strategy of preselection by shotgun analyses, crosschecking in brain tissue samples, and verification by targeted proteomic analyses using a multistep statistical modeling approach. Sixty-three patients (66%) had a poor neurologic outcome. Out of a total of 299 proteins, we identified α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein as novel biomarkers for poor neurologic outcome. The implementation of these biomarkers into a clinical multimarker model, consisting of previously identified covariates associated to outcome, resulted in a significant improvement of neurologic outcome prediction (C-index, 0.70; explained variation, 11.9%; p for added value, 0.019).

CONCLUSIONS

This study identified four novel biomarkers for the prediction of poor neurologic outcome in out-of-hospital cardiac arrest survivors. The implementation of α-enolase, 14-3-3 protein ζ/δ, cofilin-1, and heat shock cognate 71 kDa protein into a multimarker predictive model along with previously identified risk factors significantly improved neurologic outcome prediction. Each of the proteomically identified biomarkers did not only outperform current risk stratification models but may also reflect important pathophysiologic pathways undergoing during cerebral ischemia.

Molecular signatures of atherosclerotic plaques: An up-dated panel of protein related markers

Atherosclerosis remains the leading cause of ischemic syndromes such as myocardial infarction or brain stroke, mainly promoted by plaque rupture and subsequent arterial blockade. Identification of vulnerable or high-risk plaques constitutes a major challenge, being necessary to identify patients at risk of occlusive events in order to provide them with appropriate therapies. Clinical imaging tools have allowed the identification of certain structural indicators of prone-rupture plaques, including a necrotic lipidic core, intimal and adventitial inflammation, extracellular matrix dysregulation, and smooth muscle cell depletion and micro-calcification. Additionally, alternative approaches focused on identifying molecular biomarkers of atherosclerosis have also been applied. Among them, proteomics has provided numerous protein markers currently investigated in clinical practice. In this regard, it is quite uncertain that a single molecule can describe plaque rupture, due to the complexity of the process itself. Therefore, it should be more accurate to consider a set of markers to define plaques at risk. Herein, we propose a selection of 76 proteins, from classical inflammatory to recently related markers, all of them identified in at least two proteomic studies analyzing unstable atherosclerotic plaques. Such panel could be used as a prognostic signature of plaque instability.

Cardiopoietic stem cell therapy restores infarction-altered cardiac proteome

Cardiopoietic stem cells have reached advanced clinical testing for ischemic heart failure. To profile their molecular influence on recipient hearts, systems proteomics was here applied in a chronic model of infarction randomized with and without human cardiopoietic stem cell treatment. Multidimensional label-free tandem mass spectrometry resolved and quantified 3987 proteins constituting the cardiac proteome. Infarction altered 450 proteins, reduced to 283 by stem cell treatment. Notably, cell therapy non-stochastically reversed a majority of infarction-provoked changes, remediating 85% of disease-affected protein clusters. Pathway and network analysis decoded functional reorganization, distinguished by prioritization of vasculogenesis, cardiac development, organ regeneration, and differentiation. Subproteome restoration nullified adverse ischemic effects, validated by echo-/electro-cardiographic documentation of improved cardiac chamber size, reduced QT prolongation and augmented ejection fraction post-cell therapy. Collectively, cardiopoietic stem cell intervention transitioned infarcted hearts from a cardiomyopathic trajectory towards pre-disease. Systems proteomics thus offers utility to delineate and interpret complex molecular regenerative outcomes.

Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and Proteomics

BACKGROUND

Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition.

OBJECTIVES

The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics.

METHODS

Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts.

RESULTS

In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10^-5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both).

CONCLUSIONS

A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.

A group of serum proteins as potential diagnostic biomarkers for Yin-deficiency-heat syndrome

Yin-deficiency-heat (YDH) syndrome is a very common subhealth status in Traditional Chinese Medicine. However, currently, there is no unified standard for diagnosing YDH syndrome. We applied the iTRAQ-2D LC-MS/MS method to explore the potential of serum protein profiles as biomarker for YDH syndrome. A total of 120 differentially expressed proteins (79 downregulated and 41 upregulated) were identified by the proteomic profiling. The results of KEGG pathway analysis showed that the functions of the differentially expressed proteins were mainly involved in complement and coagulation cascades. The clinical data showed that YDH syndrome was closely related to inflammation and coagulation, compared with the healthy controls. The ELISA validation results indicated that the expression levels of ALB, CFI, and KLKB1 were downregulated in the YDH syndrome group (p < .05). Moreover, we established a decision tree model based on the combination of these three proteins and achieved a sensitivity of 87.5%, a specificity of 84.4%, and AUC of 0.891. The results indicated that the combination of ALB, CFI, and KLKB1 may serve as potential biomarkers for diagnosing YDH syndrome. Our study can provide a new method for YDH syndrome diagnosis, and may also provide an experimental basis to understand the molecular mechanism of YDH syndrome.

Is proteomics of value in cardiovascular risk assessment?

PURPOSE OF REVIEW

To briefly summarize recently published evidence in the field of cardiovascular proteomics, focusing on its ability to improve cardiovascular risk stratification and critically discussing still open and burning issues and future perspectives of proteomics research.

RECENT FINDINGS

Several epidemiological studies have demonstrated an improvement in cardiovascular risk prediction beyond traditional risk factors by adding novel biomarkers, identified by both discovery and targeted proteomics. However, only a moderate improvement in risk discrimination over clinical variables was observed. Moreover, despite different outcomes there was also a strong overlap of identified candidates, with several of them being already well established cardiovascular risk markers such as growth differentiation factor 15, natriuretic peptides, C-reactive protein, interleukins, and metalloproteases.

SUMMARY

Although proteomics plays a crucial role in biomarker discovery, the modest discriminative ability of this technique raises the possibility that there are still hidden mechanisms in protein regulatory networks, which urgently need to be evaluated to improve a cardiovascular risk assessment to a clinically significant extent.

Hypoplastic left heart syndrome: From bedside to bench and back

Hypoplastic Left Heart Syndrome (HLHS) is a complex Congenital Heart Disease (CHD) that was almost universally fatal until the advent of the Norwood operation in 1981. Children with HLHS who largely succumbed to the disease within the first year of life, are now surviving to adulthood. However, this survival is associated with multiple comorbidities and HLHS infants have a higher mortality rate as compared to other non-HLHS single ventricle patients. In this review we (a) discuss current clinical challenges associated in the care of HLHS patients, (b) explore the use of systems biology in understanding the molecular framework of this disease, (c) evaluate induced pluripotent stem cells as a translational model to understand molecular mechanisms and manipulate them to improve outcomes, and (d) investigate cell therapy, gene therapy, and tissue engineering as a potential tool to regenerate hypoplastic cardiac structures and improve outcomes.

Somewhere over the sex differences rainbow of myocardial infarction remodeling: hormones, chromosomes, inflammasome, oh my

Introduction: Cardiovascular disease is a major cause of death in both men and women. While women are protected until the onset of menopause, after menopause women have increased risk of adverse cardiovascular disease events. Animal models of myocardial infarction recapitulate many of the sex differences observed in humans, and proteomics evaluations offer mechanistic insights to explain sex differences.Areas covered: In this review, we will discuss how proteomics has helped us understand the hormonal, chromosomal, and immune mechanisms behind sex differences in response to ischemic injury and the development of heart failure.Expert opinion: There are a number of ways in which proteomics has and will continue to facilitate our understanding of sex differences in cardiac remodeling after myocardial infarction.

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six categories of cardiovascular disease (CVD), namely, ischemic heart disease, cardiomyopathies, cerebrovascular accident, congenital heart disease, arrhythmias, and valve disease, anticipating novel ECM protein-disease and protein-protein relationships hidden within vast quantities of textual data. We conducted a phrase-mining analysis, delineating the relationships of 709 ECM proteins with the 6 groups of CVDs reported in 1,099,254 abstracts. The technology pipeline known as Context-Aware Semantic Online Analytical Processing was applied to semantically rank the association of proteins to each CVD and all six CVDs, performing analyses to quantify each protein-disease relationship. We performed principal component analysis and hierarchical clustering of the data, where each protein was visualized as a six-dimensional vector. We found that ECM proteins display variable degrees of association with the six CVDs; certain CVDs share groups of associated proteins, whereas others have divergent protein associations. We identified 82 ECM proteins sharing associations with all 6 CVDs. Our bioinformatics analysis ascribed distinct ECM pathways (via Reactome) from this subset of proteins, namely, insulin-like growth factor regulation and interleukin-4 and interleukin-13 signaling, suggesting their contribution to the pathogenesis of all six CVDs. Finally, we performed hierarchical clustering analysis and identified protein clusters predominantly associated with a targeted CVD; analyses of these proteins revealed unexpected insights underlying the key ECM-related molecular pathogenesis of each CVD, including virus assembly and release in arrhythmias. NEW & NOTEWORTHY The present study is the first application of a text-mining algorithm to characterize the relationships of 709 extracellular matrix-related proteins with 6 categories of cardiovascular disease described in 1,099,254 abstracts. Our analysis informed unexpected extracellular matrix functions, pathways, and molecular relationships implicated in the six cardiovascular diseases.