Genetic ablation of Lmp2 increases the susceptibility for impaired cardiac function.

Proteasome degradation is an integral part of cellular growth and function. Proteasomal intervention may mitigate adverse myocardial remodeling, but is associated with the onset of heart failure. Previously, we have demonstrated that increasing abundance of cardiac Lmp2 and its incorporation into proteasome complexes is an endogenous mechanism for proteasome regulation during hypertrophic remodeling of the heart induced by chronic ß-adrenoreceptor stimulation. Here, we investigated whether Lmp2 is required for myocardial remodeling not driven by inflammation and show that Lmp2 is a tipping element for growth and function in the heart but not for proteasome insufficiency. While it has no apparent impact under unchallenged conditions, myocardial remodeling without Lmp2 exacerbates hypertrophy and restricts cardiac function. Under chronic ß-adrenoreceptor stimulation, as seen in the development of cardiovascular disease and the manifestation of heart failure, genetic ablation of Lmp2 in mice caused augmented concentric hypertrophy of the left ventricle. While the heart rate was similarly elevated as in wildtype, myocardial contractility was not maintained without Lmp2, and apparently uncoupled of the ß-adrenergic response. Normalized to the exacerbated myocardial mass, contractility was reduced by 41% of the pretreatment level, but would appear preserved at absolute level. The lack of Lmp2 interfered with elevated 26S proteasome activities during early cardiac remodeling reported previously, but did not cause bulk proteasome insufficiency, suggesting the Lmp2 containing proteasome subpopulation is required for a selected group of proteins to be degraded. In the myocardial interstitium, augmented collagen deposition suggested matrix stiffening in the absence of Lmp2. Indeed, echocardiography of left ventricular peak relaxation velocity (circumferential strain rate) was reduced in this treatment group. Overall, targeting Lmp2 in a condition mimicking chronic ß-adrenoreceptor stimulation exhibited the onset of heart failure. Anticancer therapy inhibiting proteasome activity, including Lmp2, is associated with adverse cardiac events, in particular heart failure. Sparing Lmp2 may be an avenue to reduce adverse cardiac events when chronic sympathetic nervous system activation cannot be excluded.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated With Heart Failure With Improved Ejection Fraction.

Heart failure with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced EF (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using International Classification of Diseases codes, echocardiographic data, and natriuretic peptide levels. The main end points were HFimpEF (defined as EF >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. The study included 1,307 patients with HFrEF with a median follow-up of 16.3 months (interquartile range 8.0 to 30.6). The median age was 65 years; 68% were male whereas 57% were White. On follow-up, 38.7% (n = 506) developed HFimpEF, whereas 61.3% (n = 801) had persistent HFrEF. A multivariate Cox regression model identified gender, race, co-morbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF (p <0.05). The HFimpEF group had better survival compared with the persistent HFrEF group (p <0.001). Echocardiographic and laboratory trajectories differed between groups. In this HFrEF cohort, 38.7% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, gender, co-morbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated with Heart Failure with Improved Ejection Fraction.

Background: Heart failure (HF) with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced ejection fraction (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. Methods: This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using ICD codes, echocardiographic data, and natriuretic peptide levels. The main endpoints were HFimpEF (defined as ejection fraction >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. Results: The study included 1307 HFrEF patients with a median follow-up of 16.3 months (IQR 8.0-30.6). The median age was 65 years; 68% were male while 57% were white. On follow-up, 39% (n=506) developed HFimpEF, while 61% (n=801) had persistent HFrEF. A multivariate Cox regression model identified sex, race comorbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF ( p <0.05). The HFimpEF group had better survival compared to the persistent HFrEF group ( p <0.001). Echocardiographic and laboratory trajectories differed between groups. Conclusion: In this HFrEF cohort, 39% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, sex, comorbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.

Background and Objective: Heart failure (HF) in the pediatric population is a multi-factorial process with a wide spectrum of etiologies and clinical manifestations, that are distinct from the adult HF population, with congenital heart disease (CHD) as the most common cause. CHD has high morbidity/mortality with nearly 60% developing HF during the first 12 months of life. Hence, early discovery and diagnosis of CHD in neonates is pivotal. Plasma B-type natriuretic peptide (BNP) is an increasingly popular clinical marker in pediatric HF, however, in contrast to adult HF, it is not yet included in pediatric HF guidelines and there is no standardized reference cut-off value. We explore the current trends and prospects of biomarkers in pediatric HF, including CHD that can aid in diagnosis and management. Methods: As a narrative review, we will analyze biomarkers with respect to diagnosis and monitoring in specific anatomical types of CHD in the pediatric population considering all English PubMed publications till June 2022. Key Content and Findings: We present a concise description of our own experience in applying plasma BNP as a clinical biomarker in pediatric HF and CHD (tetralogy of fallot vs. ventricular septal defect) in the context of surgical correction, as well as untargeted metabolomics analyses. In the current age of Information Technology and large data sets we also explored new biomarker discovery using Text Mining of 33M manuscripts currently on PubMed. Conclusions: (Multi) Omics studies from patient samples as well as Data Mining can be considered for the discovery of potential pediatric HF biomarkers useful in clinical care. Future research should focus on validation and defining evidence-based value limits and reference ranges for specific indications using the most up-to-date assays in parallel to commonly used studies.

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

A reference set of curated biomedical data and metadata from clinical case reports.

Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

Quantitative temporal analysis of protein dynamics in cardiac remodeling.

Cardiac remodeling (CR) is a complex dynamic process common to many heart diseases. CR is characterized as a temporal progression of global adaptive and maladaptive perturbations. The complex nature of this process clouds a comprehensive understanding of CR, but greater insight into the processes and mechanisms has potential to identify new therapeutic targets. To provide a deeper understanding of this important cardiac process, we applied a new proteomic technique, PALM (Pulse Azidohomoalanine in Mammals), to quantitate the newly-synthesized protein (NSP) changes during the progression of isoproterenol (ISO)-induced CR in the mouse left ventricle. This analysis revealed a complex combination of adaptive and maladaptive alterations at acute and prolonged time points including the identification of proteins not previously associated with CR. We also combined the PALM dataset with our published protein turnover rate dataset to identify putative biochemical mechanisms underlying CR. The novel integration of analyzing NSPs together with their protein turnover rates demonstrated that alterations in specific biological pathways (e.g., inflammation and oxidative stress) are produced by differential regulation of protein synthesis and degradation.

Clinical phenomapping and outcomes after heart transplantation.

BACKGROUND: Survival after heart transplantation (HTx) is limited by complications related to alloreactivity, immune suppression, and adverse effects of pharmacologic therapies. We hypothesize that time-dependent phenomapping of clinical and molecular data sets is a valuable approach to clinical assessments and guiding medical management to improve outcomes. METHODS: We analyzed clinical, therapeutic, biomarker, and outcome data from 94 adult HTx patients and 1,557 clinical encounters performed between January 2010 and April 2013. Multivariate analyses were used to evaluate the association between immunosuppression therapy, biomarkers, and the combined clinical end point of death, allograft loss, retransplantation, and rejection. Data were analyzed by K-means clustering (K = 2) to identify patterns of similar combined immunosuppression management, and percentile slopes were computed to examine the changes in dosages over time. Findings were correlated with clinical parameters, human leucocyte antigen antibody titers, and peripheral blood mononuclear cell gene expression of the AlloMap (CareDx, Inc., Brisbane, CA) test genes. An intragraft, heart tissue gene coexpression network analysis was performed. RESULTS: Unsupervised cluster analysis of immunosuppressive therapies identified 2 groups, 1 characterized by a steeper immunosuppression minimization, associated with a higher likelihood for the combined end point, and the other by a less pronounced change. A time-dependent phenomap suggested that patients in the group with higher event rates had increased human leukocyte antigen class I and II antibody titers, higher expression of the FLT3 AlloMap gene, and lower expression of the MARCH8 and WDR40A AlloMap genes. Intramyocardial biomarker-related coexpression network analysis of the FLT3 gene showed an immune system-related network underlying this biomarker. CONCLUSIONS: Time-dependent precision phenotyping is a mechanistically insightful, data-driven approach to characterize patterns of clinical care and identify ways to improve clinical management and outcomes.

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.

Integrated omics dissection of proteome dynamics during cardiac remodeling.

Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.

A large dataset of protein dynamics in the mammalian heart proteome.

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems.

The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling.

BACKGROUND: To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level. RESULTS: Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction. CONCLUSIONS: Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.

Spatial and temporal dynamics of the cardiac mitochondrial proteome.

Mitochondrial proteins alter in their composition and quantity drastically through time and space in correspondence to changing energy demands and cellular signaling events. The integrity and permutations of this dynamism are increasingly recognized to impact the functions of the cardiac proteome in health and disease. This article provides an overview on recent advances in defining the spatial and temporal dynamics of mitochondrial proteins in the heart. Proteomics techniques to characterize dynamics on a proteome scale are reviewed and the physiological consequences of altered mitochondrial protein dynamics are discussed. Lastly, we offer our perspectives on the unmet challenges in translating mitochondrial dynamics markers into the clinic.

Characterization of human plasma proteome dynamics using deuterium oxide.

PURPOSE: High-throughput quantification of human protein turnover via in vivo administration of deuterium oxide ((2) H2 O) is a powerful new approach to examine potential disease mechanisms. Its immediate clinical translation is contingent upon characterizations of the safety and hemodynamic effects of in vivo administration of (2) H2 O to human subjects. EXPERIMENTAL DESIGN: We recruited ten healthy human subjects with a broad demographic variety to evaluate the safety, feasibility, efficacy, and reproducibility of (2) H2 O intake for studying protein dynamics. We designed a protocol where each subject orally consumed weight-adjusted doses of 70% (2) H2 O daily for 14 days to enrich body water and proteins with deuterium. Plasma proteome dynamics was measured using a high-resolution MS method we recently developed. RESULTS: This protocol was successfully applied in ten human subjects to characterize the endogenous turnover rates of 542 human plasma proteins, the largest such human dataset to-date. Throughout the study, we did not detect physiological effects or signs of discomfort from (2) H2 O consumption. CONCLUSIONS AND CLINICAL RELEVANCE: Our investigation supports the utility of a (2) H2 O intake protocol that is safe, accessible, and effective for clinical investigations of large-scale human protein turnover dynamics. This workflow shows promising clinical translational value for examining plasma protein dynamics in human diseases.

Protein kinetic signatures of the remodeling heart following isoproterenol stimulation.

Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Methods for quantitative assessments of protein kinetics are lacking, and despite knowledge gained from single-protein studies, integrative views of the coordinated behavior of multiple proteins in cardiac remodeling are scarce. Here, we developed a workflow that integrates deuterium oxide (2H2O) labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. Using this workflow, we characterized the in vivo turnover kinetics of 2,964 proteins in a mouse model of ß-adrenergic-induced cardiac remodeling. The data provided a quantitative and longitudinal view of cardiac remodeling at the molecular level, revealing widespread kinetic regulations in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. We translated the workflow to human studies, creating a reference dataset of 496 plasma protein turnover rates from 4 healthy adults. The approach is applicable to short, minimal label enrichment and can be performed on as little as a single biopsy, thereby overcoming critical obstacles to clinical investigations. The protein turnover quantitation experiments and computational workflow described here should be widely applicable to large-scale biomolecular investigations of human disease mechanisms with a temporal perspective.

Molecular basis of recovering on mechanical circulatory support.

Our insights into different system levels of mechanisms by left ventricular assist device support are increasing and suggest a complex regulatory system of overlapping biological processes. To develop novel decision-making strategies and patient selection criteria, heart failure and reverse cardiac remodeling should be conceptualized and explored by a multifaceted research strategy of transcriptomics, metabolomics, proteomics, molecular biology, and bioinformatics. Knowledge of the molecular mechanisms of reverse cardiac remodeling is in its early stages, and comprehensive reconstruction of the underlying networks is necessary.

Characterization, design, and function of the mitochondrial proteome: from organs to organisms.

Mitochondria are a common energy source for organs and organisms; their diverse functions are specialized according to the unique phenotypes of their hosting environment. Perturbation of mitochondrial homeostasis accompanies significant pathological phenotypes. However, the connections between mitochondrial proteome properties and function remain to be experimentally established on a systematic level. This uncertainty impedes the contextualization and translation of proteomic data to the molecular derivations of mitochondrial diseases. We present a collection of mitochondrial features and functions from four model systems, including two cardiac mitochondrial proteomes from distinct genomes (human and mouse), two unique organ mitochondrial proteomes from identical genetic codons (mouse heart and mouse liver), as well as a relevant metazoan out-group (drosophila). The data, composed of mitochondrial protein abundance and their biochemical activities, capture the core functionalities of these mitochondria. This investigation allowed us to redefine the core mitochondrial proteome from organs and organisms, as well as the relevant contributions from genetic information and hosting milieu. Our study has identified significant enrichment of disease-associated genes and their products. Furthermore, correlational analyses suggest that mitochondrial proteome design is primarily driven by cellular environment. Taken together, these results connect proteome feature with mitochondrial function, providing a prospective resource for mitochondrial pathophysiology and developing novel therapeutic targets in medicine.