Integrative Multi-Omics Analysis in Calcific Aortic Valve Disease Reveals a Link to the Formation of Amyloid-Like Deposits

Prevalence, Characteristics, and Impact on Prognosis of Aortic Stenosis in Patients With Cardiac Amyloidosis

BACKGROUND

Cardiac amyloidosis (CA) is frequently found in older patients with aortic stenosis (AS). However, the prevalence of AS among patients with CA is unknown. The objective was to study the prevalence and prognostic impact of AS among patients with CA.

METHODS AND RESULTS

We conducted a retrospective analysis of a prospective registry comprising 976 patients with native aortic valves who were confirmed with wild type transthyretin amyloid (ATTRwt), hereditary variant transthyretin amyloid (ATTRv), or immunoglobulin light-chain (AL) CA. CA patients' echocardiograms were re-analyzed focusing on the aortic valve. Multivariable Cox regression analysis was performed to assess the mortality risk associated with moderate or greater AS in ATTRwt CA. The crude prevalence of AS among patients with CA was 26% in ATTRwt, 8% in ATTRv, and 5% in AL. Compared with population-based controls, all types of CA had higher age- and sex-standardized rate ratios (SRRs) of having any degree of AS (AL: SRR, 2.62; 95% Confidence Interval (CI) [1.09-3.64]; ATTRv: SRR, 3.41; 95%CI [1.64-4.60]; ATTRwt: SRR, 10.8; 95%CI [5.25-14.53]). Compared with hospital controls, only ATTRwt had a higher SRR of having any degree of AS (AL: SRR, 0.97, 95%CI [0.56-1.14]; ATTRv: SRR, 1.27; 95%CI [0.85-1.44]; ATTRwt: SRR, 4.01; 95%CI [2.71-4.54]). Among patients with ATTRwt, moderate or greater AS was not associated with increased all-cause death after multivariable adjustment (hazard ratio, 0.71; 95%CI [0.42-1.19]; P=0.19).

CONCLUSIONS

Among patients with CA, ATTRwt but not ATTRv or AL is associated with a higher prevalence of patients with AS compared with hospital controls without CA, even after adjusting for age and sex. In our population, having moderate or greater AS was not associated with a worse outcome in patients with ATTRwt.

Association of aortic valve calcium with dementia and stroke: The Multi-Ethnic Study of Atherosclerosis

BACKGROUND AND AIMS

Calcific aortic valve disease is associated with increased thrombin formation, platelet activation, decreased fibrinolysis, and subclinical brain infarcts. We examined the long-term association of aortic valve calcification (AVC) with newly diagnosed dementia and incident stroke in the Multi-Ethnic Study of Atherosclerosis (MESA).

METHODS

AVC was measured using non-contrast cardiac CT at Visit 1. We examined AVC as a continuous (log-transformed) and categorical variable (0, 1-99, 100-299, ≥300). Newly diagnosed dementia was adjudicated using International Classification of Disease codes. Stroke was adjudicated from medical records. We calculated absolute event rates (per 1000 person-years) and multivariable adjusted Cox proportional hazards ratios (HR).

RESULTS

Overall, 6812 participants had AVC quantified with a mean age of 62.1 years old, 52.9 % were women, and the median 10-year estimated atherosclerotic cardiovascular disease (ASCVD) risk was 13.5 %. Participants with AVC >0 were older and less likely to be women compared to those with AVC=0. Over a median 16-year follow-up, there were 535 cases of dementia and 376 cases of stroke. The absolute risk of newly diagnosed dementia increased in a stepwise pattern with higher AVC scores, and stroke increased in a logarithmic pattern. In multivariable analyses, AVC was significantly associated with newly diagnosed dementia as a log-transformed continuous variable (HR 1.09; 95 % CI 1.04-1.14) and persons with AVC ≥300 had nearly a two-fold higher risk (HR 1.77; 95 % CI 1.14-2.76) compared to those with AVC=0. AVC was associated with an increased risk of stroke after adjustment for age, sex, and race/ethnicity, but not after adjustment for ASCVD risk factors.

CONCLUSIONS

After multivariable adjustment, AVC >0 was significantly associated with an increased risk of newly diagnosed dementia, but not incident stroke. This suggests that AVC may be an important risk factor for the long-term risk of dementia beyond traditional ASCVD risk factors.

Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification

This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.

Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis

BACKGROUND

Fewer than 50% of patients who develop aortic valve calcification have concomitant atherosclerosis, implying differential pathogenesis. Although circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs are associated with early mineralization, but their cargoes, functions, and contributions to disease remain unknown.

METHODS

Disease stage-specific proteomics was performed on human carotid endarterectomy specimens (n=16) and stenotic aortic valves (n=18). Tissue EVs were isolated from human carotid arteries (normal, n=6; diseased, n=4) and aortic valves (normal, n=6; diseased, n=4) by enzymatic digestion, (ultra)centrifugation, and a 15-fraction density gradient validated by proteomics, CD63-immunogold electron microscopy, and nanoparticle tracking analysis. Vesiculomics, comprising vesicular proteomics and small RNA-sequencing, was conducted on tissue EVs. TargetScan identified microRNA targets. Pathway network analyses prioritized genes for validation in primary human carotid artery smooth muscle cells and aortic valvular interstitial cells.

RESULTS

Disease progression drove significant convergence (P<0.0001) of carotid artery plaque and calcified aortic valve proteomes (2318 proteins). Each tissue also retained a unique subset of differentially enriched proteins (381 in plaques; 226 in valves; q<0.05). Vesicular gene ontology terms increased 2.9-fold (P<0.0001) among proteins modulated by disease in both tissues. Proteomics identified 22 EV markers in tissue digest fractions. Networks of proteins and microRNA targets changed by disease progression in both artery and valve EVs revealed shared involvement in intracellular signaling and cell cycle regulation. Vesiculomics identified 773 proteins and 80 microRNAs differentially enriched by disease exclusively in artery or valve EVs (q<0.05); multiomics integration found tissue-specific EV cargoes associated with procalcific Notch and Wnt signaling in carotid arteries and aortic valves, respectively. Knockdown of tissue-specific EV-derived molecules FGFR2, PPP2CA, and ADAM17 in human carotid artery smooth muscle cells and WNT5A, APP, and APC in human aortic valvular interstitial cells significantly modulated calcification.

CONCLUSIONS

The first comparative proteomics study of human carotid artery plaques and calcified aortic valves identifies unique drivers of atherosclerosis versus aortic valve stenosis and implicates EVs in advanced cardiovascular calcification. We delineate a vesiculomics strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Integration of vesicular proteomics and transcriptomics by network approaches revealed novel roles for tissue EVs in modulating cardiovascular disease.

Focusing on the Native Matrix Proteins in Calcific Aortic Valve Stenosis

Calcific aortic valve stenosis (CAVS) is a widespread valvular heart disease affecting people in aging societies, primarily characterized by fibrosis, inflammation, and progressive calcification, leading to valve orifice stenosis. Understanding the factors associated with CAVS onset and progression is crucial to develop effective future pharmaceutical therapies. In CAVS, native extracellular matrix proteins modifications, play a significant role in calcification in vitro and in vivo. This work aimed to review the evidence on the alterations of structural native extracellular matrix proteins involved in calcification development during CAVS and highlight its link to deregulated biomechanical function.

The contribution of amyloid deposition in the aortic valve to calcification and aortic stenosis

Calcific aortic valve disease (CAVD) and stenosis have a complex pathogenesis, and no therapies are available that can halt or slow their progression. Several studies have shown the presence of apolipoprotein-related amyloid deposits in close proximity to calcified areas in diseased aortic valves. In this Perspective, we explore a possible relationship between amyloid deposits, calcification and the development of aortic valve stenosis. These amyloid deposits might contribute to the amplification of the inflammatory cycle in the aortic valve, including extracellular matrix remodelling and myofibroblast and osteoblast-like cell proliferation. Further investigation in this area is needed to characterize the amyloid deposits associated with CAVD, which could allow the use of antisense oligonucleotides and/or isotype gene therapies for the prevention and/or treatment of CAVD.

Network-Guided Multiomic Mapping of Aortic Valve Calcification

Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.

Amyloid Deposits in a Functionally Unicuspid Stenotic Aortic Valve

Amyloidosis concomitant to aortic stenosis usually occurs with myocardial infiltration by the transthyretin protein. To our knowledge, this is the first report of localized amyloidosis of indeterminate type in a severely calcified and functionally unicuspid aortic valve. Isolated dystrophic valvular amyloidosis is believed to be related to fibrocalcific valve disease. In light of the literature on this topic, the present case raises new hypotheses on pathophysiology and further supports the contributory role of unusual non-tricuspid valve morphology in the development of dystrophic amyloid, likely secondary to altered hemodynamic stress.

Distinct role of mitochondrial function and protein kinase C in intimal and medial calcification in vitro

Introduction

Vascular calcification (VC) is a major risk factor for cardiovascular morbidity and mortality. Depending on the location of mineral deposition within the arterial wall, VC is classified as intimal and medial calcification. Using in vitro mineralization assays, we developed protocols triggering both types of calcification in vascular smooth muscle cells (SMCs) following diverging molecular pathways.

Materials and methods and results

Human coronary artery SMCs were cultured in osteogenic medium (OM) or high calcium phosphate medium (CaP) to induce a mineralized extracellular matrix. OM induces osteoblast-like differentiation of SMCs–a key process in intimal calcification during atherosclerotic plaque remodeling. CaP mimics hyperphosphatemia, associated with chronic kidney disease–a risk factor for medial calcification. Transcriptomic analysis revealed distinct gene expression profiles of OM and CaP-calcifying SMCs. OM and CaP-treated SMCs shared 107 differentially regulated genes related to SMC contraction and metabolism. Real-time extracellular efflux analysis demonstrated decreased mitochondrial respiration and glycolysis in CaP-treated SMCs compared to increased mitochondrial respiration without altered glycolysis in OM-treated SMCs. Subsequent kinome and in silico drug repurposing analysis (Connectivity Map) suggested a distinct role of protein kinase C (PKC). In vitro validation experiments demonstrated that the PKC activators prostratin and ingenol reduced calcification triggered by OM and promoted calcification triggered by CaP.

Conclusion

Our direct comparison results of two in vitro calcification models strengthen previous observations of distinct intracellular mechanisms that trigger OM and CaP-induced SMC calcification in vitro. We found a differential role of PKC in OM and CaP-calcified SMCs providing new potential cellular and molecular targets for pharmacological intervention in VC. Our data suggest that the field should limit the generalization of results found in in vitro studies using different calcification protocols.

Clinical Significance of miR-21-5p in Predicting Occurrence and Progression of Uremic Vascular Calcification in Patients with End-Stage Renal Disease

PURPOSE

Vascular calcification (VC) is a common complication of end-stage renal disease (ESRD). This study aimed to examine changes in the expression of miR-21-5p in ESRD patients with VC and to explore its clinical value in predicting the occurrence and progression of uremic VC.

MATERIALS AND METHODS

120 ESRD patients were divided into patients without VC group (n=38) and patients with VC group (n=82). All patients were followed up for 2 years to evaluate VC progression. qRT-PCR was used to detect serum miR-21-5p levels. Receiver operating characteristic curves were constructed to assess diagnostic value. Kaplan-Meier and log-rank methods were utilized to calculate associations between VC progression and risk factors.

RESULTS

Serum miR-21-5p levels were significantly higher in ESRD patients with VC than in those without VC and increased progressively with increasing disease severity. Serum miR-21-5p levels were able to distinguish patients with VC from those without VC, with an area under the curve value of 0.883, a sensitivity of 81.7%, and a specificity of 84.2%. After 2 years of follow-up, miR-21-5p expression had increased in patients with worse VC severity, compared with those with stable VC severity. Patients with high miR-21-5p levels were more likely to develop more severe VC, indicating an association between miR-21-5p and VC progression (log-rank p=0.002). Multivariable Cox regression analysis suggested that serum miR-21-5p is an independent predictive factor of VC progression in ESRD patients (hazard ratio=2.064, 95% confidence interval=1.225-3.478, p=0.006).

CONCLUSION

miR-21-5p is overexpressed in the serum of ESRD patients with VC. Our results suggest that overexpression of miR-21-5p is closely associated with VC progression.

AGTR1, PLTP, and SCG2 associated with immune genes and immune cell infiltration in calcific aortic valve stenosis: analysis from integrated bioinformatics and machine learning

Background: Calcific aortic valve stenosis (CAVS) is a crucial cardiovascular disease facing aging societies. Our research attempts to identify immune-related genes through bioinformatics and machine learning analysis. Two machine learning strategies include Least Absolute Shrinkage Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In addition, we deeply explore the role of immune cell infiltration in CAVS, aiming to study the potential therapeutic targets of CAVS and explore possible drugs. Methods: Download three data sets related to CAVS from the Gene Expression Omnibus. Gene set variation analysis (GSVA) looks for potential mechanisms, determines differentially expressed immune-related genes (DEIRGs) by combining the ImmPort database with CAVS differential genes, and explores the functions and pathways of enrichment. Two machine learning methods, LASSO and SVM-RFE, screen key immune signals and validate them in external data sets. Single-sample GSEA (ssGSEA) and CIBERSORT analyze the subtypes of immune infiltrating cells and integrate the analysis with DEIRGs and key immune signals. Finally, the possible targeted drugs are analyzed through the Connectivity Map (CMap). Results: GSVA analysis of the gene set suggests that it is highly correlated with multiple immune pathways. 266 differential genes (DEGs) integrate with immune genes to obtain 71 DEIRGs. Enrichment analysis found that DEIRGs are related to oxidative stress, synaptic membrane components, receptor activity, and a variety of cardiovascular diseases and immune pathways. Angiotensin II Receptor Type 1(AGTR1), Phospholipid Transfer Protein (PLTP), Secretogranin II (SCG2) are identified as key immune signals of CAVS by machine learning. Immune infiltration found that B cells naï ve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS. Simultaneously, AGTR1, PLTP, SCG2 are highly correlated with a variety of immune cell subtypes. CMap analysis found that isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are the top three targeted drugs related to CAVS immunity. Conclusion: The key immune signals, immune infiltration and potential drugs obtained from the research play a vital role in the pathophysiological progress of CAVS.

Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis

Background

Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with patients having normal valves, using a knowledge-slanted random forest (RF).

Results

This study implemented a knowledge-slanted random forest (RF) using information extracted from a protein-protein interactions network to rank genes in order to modify their selection probability to draw the candidate split-variables. A total of 15,191 genes were assessed in 19 valves with CAVS (BAV, n = 10; TAV, n = 9) and 8 normal valves. The performance of the model was evaluated using accuracy, sensitivity, and specificity to discriminate cases with CAVS. A comparison with conventional RF was also performed. The performance of this proposed approach reported improved accuracy in comparison with conventional RF to classify cases separately with BAV and TAV (Slanted RF: 59.3% versus 40.7%). When patients with BAV and TAV were grouped against patients with normal valves, the addition of prior biological information was not relevant with an accuracy of 92.6%.

Conclusion

The knowledge-slanted RF approach reflected prior biological knowledge, leading to better precision in distinguishing between cases with BAV, TAV, and normal valves. The results of this study suggest that the integration of biological knowledge can be useful during difficult classification tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00269-4.

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development.

Transthyretin: From Structural Stability to Osteoarticular and Cardiovascular Diseases

Transthyretin (TTR) is a tetrameric protein transporting hormones in the plasma and brain, which has many other activities that have not been fully acknowledged. TTR is a positive indicator of nutrition status and is negatively correlated with inflammation. TTR is a neuroprotective and oxidative-stress-suppressing factor. The TTR structure is destabilized by mutations, oxidative modifications, aging, proteolysis, and metal cations, including Ca2+. Destabilized TTR molecules form amyloid deposits, resulting in senile and familial amyloidopathies. This review links structural stability of TTR with the environmental factors, particularly oxidative stress and Ca2+, and the processes involved in the pathogenesis of TTR-related diseases. The roles of TTR in biomineralization, calcification, and osteoarticular and cardiovascular diseases are broadly discussed. The association of TTR-related diseases and vascular and ligament tissue calcification with TTR levels and TTR structure is presented. It is indicated that unaggregated TTR and TTR amyloid are bound by vicious cycles, and that TTR may have an as yet undetermined role(s) at the crossroads of calcification, blood coagulation, and immune response.

Panomics: New Databases for Advancing Cardiology

The multifactorial nature of cardiology makes it challenging to separate noisy signals from confounders and real markers or drivers of disease. Panomics, the combination of various omic methods, provides the deepest insights into the underlying biological mechanisms to develop tools for personalized medicine under a systems biology approach. Questions remain about current findings and anticipated developments of omics. Here, we search for omic databases, investigate the types of data they provide, and give some examples of panomic applications in health care. We identified 104 omic databases, of which 72 met the inclusion criteria: genomic and clinical measurements on a subset of the database population plus one or more omic datasets. Of those, 65 were methylomic, 59 transcriptomic, 41 proteomic, 42 metabolomic, and 22 microbiomic databases. Larger database sample sizes and longer follow-up are often better suited for panomic analyses due to statistical power calculations. They are often more complete, which is important when dealing with large biological variability. Thus, the UK BioBank rises as the most comprehensive panomic resource, at present, but certain study designs may benefit from other databases.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.