Tissue Usage Preference and Intrinsically Disordered Region Remodeling of Alternative Splicing Derived Proteoforms in the Heart.

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.

HDAC Inhibition Reverses Preexisting Diastolic Dysfunction and Blocks Covert Extracellular Matrix Remodeling.

BACKGROUND: Diastolic dysfunction (DD) is associated with the development of heart failure and contributes to the pathogenesis of other cardiac maladies, including atrial fibrillation. Inhibition of histone deacetylases (HDACs) has been shown to prevent DD by enhancing myofibril relaxation. We addressed the therapeutic potential of HDAC inhibition in a model of established DD with preserved ejection fraction. METHODS: Four weeks after uninephrectomy and implantation with deoxycorticosterone acetate pellets, when DD was clearly evident, 1 cohort of mice was administered the clinical-stage HDAC inhibitor ITF2357/Givinostat. Echocardiography, blood pressure measurements, and end point invasive hemodynamic analyses were performed. Myofibril mechanics and intact cardiomyocyte relaxation were assessed ex vivo. Cardiac fibrosis was evaluated by picrosirius red staining and second harmonic generation microscopy of left ventricle (LV) sections, RNA sequencing of LV mRNA, mass spectrometry-based evaluation of decellularized LV biopsies, and atomic force microscopy determination of LV stiffness. Mechanistic studies were performed with primary rat and human cardiac fibroblasts. RESULTS: HDAC inhibition normalized DD without lowering blood pressure in this model of systemic hypertension. In contrast to previous models, myofibril relaxation was unimpaired in uninephrectomy/deoxycorticosterone acetate mice. Furthermore, cardiac fibrosis was not evident in any mouse cohort on the basis of picrosirius red staining or second harmonic generation microscopy. However, mass spectrometry revealed induction in the expression of >100 extracellular matrix proteins in LVs of uninephrectomy/deoxycorticosterone acetate mice, which correlated with profound tissue stiffening based on atomic force microscopy. ITF2357/Givinostat treatment blocked extracellular matrix expansion and LV stiffening. The HDAC inhibitor was subsequently shown to suppress cardiac fibroblast activation, at least in part, by blunting recruitment of the profibrotic chromatin reader protein BRD4 (bromodomain-containing protein 4) to key gene regulatory elements. CONCLUSIONS: These findings demonstrate the potential of HDAC inhibition as a therapeutic intervention to reverse existing DD and establish blockade of extracellular matrix remodeling as a second mechanism by which HDAC inhibitors improve ventricular filling. Our data reveal the existence of pathophysiologically relevant covert or hidden cardiac fibrosis that is below the limit of detection of histochemical stains such as picrosirius red, highlighting the need to evaluate fibrosis of the heart using diverse methodologies.

Proteogenomics reveals sex-biased aging genes and coordinated splicing in cardiac aging.

The risks of heart diseases are significantly modulated by age and sex, but how these factors influence baseline cardiac gene expression remains incompletely understood. Here, we used RNA sequencing and mass spectrometry to compare gene expression in female and male young adult (4 mo) and early aging (20 mo) mouse hearts, identifying thousands of age- and sex-dependent gene expression signatures. Sexually dimorphic cardiac genes are broadly distributed, functioning in mitochondrial metabolism, translation, and other processes. In parallel, we found over 800 genes with differential aging response between male and female, including genes in cAMP and PKA signaling. Analysis of the sex-adjusted aging cardiac transcriptome revealed a widespread remodeling of exon usage patterns that is largely independent from differential gene expression, concomitant with upstream changes in RNA-binding protein and splice factor transcripts. To evaluate the impact of the splicing events on cardiac proteoform composition, we applied an RNA-guided proteomics computational pipeline to analyze the mass spectrometry data and detected hundreds of putative splice variant proteins that have the potential to rewire the cardiac proteome. Taken together, the results here suggest that cardiac aging is associated with 1) widespread sex-biased aging genes and 2) a rewiring of RNA splicing programs, including sex- and age-dependent changes in exon usages and splice patterns that have the potential to influence cardiac protein structure and function. These changes contribute to the emerging evidence for considerable sexual dimorphism in the cardiac aging process that should be considered in the search for disease mechanisms.NEW & NOTEWORTHY Han et al. used proteogenomics to compare male and female mouse hearts at 4 and 20 mo. Sex-biased cardiac genes function in mitochondrial metabolism, translation, autophagy, and other processes. Hundreds of cardiac genes show sex-by-age interactions, that is, sex-biased aging genes. Cardiac aging is accompanied with a remodeling of exon usage in functionally coordinated genes, concomitant with differential expression of RNA-binding proteins and splice factors. These features represent an underinvestigated aspect of cardiac aging that may be relevant to the search for disease mechanisms.

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF.

Approximately 50% of all heart failure (HF) diagnoses can be classified as HF with preserved ejection fraction (HFpEF). HFpEF is more prevalent in females compared with males, but the underlying mechanisms are unknown. We previously showed that pressure overload (PO) in male felines induces a cardiopulmonary phenotype with essential features of human HFpEF. The goal of this study was to determine if slow progressive PO induces distinct cardiopulmonary phenotypes in females and males in the absence of other pathological stressors. Female and male felines underwent aortic constriction (banding) or sham surgery after baseline echocardiography, pulmonary function testing, and blood sampling. These assessments were repeated at 2 and 4 mo postsurgery to document the effects of slow progressive pressure overload. At 4 mo, invasive hemodynamic studies were also performed. Left ventricle (LV) tissue was collected for histology, myofibril mechanics, extracellular matrix (ECM) mass spectrometry, and single-nucleus RNA sequencing (snRNAseq). The induced pressure overload (PO) was not different between sexes. PO also induced comparable changes in LV wall thickness and myocyte cross-sectional area in both sexes. Both sexes had preserved ejection fraction, but males had a slightly more robust phenotype in hemodynamic and pulmonary parameters. There was no difference in LV fibrosis and ECM composition between banded male and female animals. LV snRNAseq revealed changes in gene programs of individual cell types unique to males and females after PO. Based on these results, both sexes develop cardiopulmonary dysfunction but the phenotype is somewhat less advanced in females.NEW & NOTEWORTHY We performed a comprehensive assessment to evaluate the effects of slow progressive pressure overload on cardiopulmonary function in a large animal model of heart failure with preserved ejection fraction (HFpEF) in males and females. Functional and structural assessments were performed at the organ, tissue, cellular, protein, and transcriptional levels. This is the first study to compare snRNAseq and ECM mass spectrometry of HFpEF myocardium from males and females. The results broaden our understanding of the pathophysiological response of both sexes to pressure overload. Both sexes developed a robust cardiopulmonary phenotype, but the phenotype was equal or a bit less robust in females.

Computation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart.

Alternative splicing is prevalent in the heart and implicated in many cardiovascular diseases, but not every alternative transcript is translated and detecting non-canonical isoforms at the protein level remains challenging. Here we show the use of a computation-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart. We build on a recent strategy to integrate deep RNA-seq and large-scale mass spectrometry data to identify candidate translated isoform peptides. A machine learning approach is then applied to predict their fragmentation patterns and design protein isoform-specific parallel reaction monitoring detection (PRM) assays. As proof-of-principle, we built PRM assays for 29 non-canonical isoform peptides and detected 22 peptides in a human heart lysate. The predictions-aided PRM assays closely mirrored synthetic peptide standards for non-canonical sequences. This approach may be useful for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.

Dynamic Chromatin Targeting of BRD4 Stimulates Cardiac Fibroblast Activation.

RATIONALE: Small molecule inhibitors of the acetyl-histone binding protein BRD4 have been shown to block cardiac fibrosis in preclinical models of heart failure (HF). However, since the inhibitors target BRD4 ubiquitously, it is unclear whether this chromatin reader protein functions in cell type-specific manner to control pathological myocardial fibrosis. Furthermore, the molecular mechanisms by which BRD4 stimulates the transcriptional program for cardiac fibrosis remain unknown. OBJECTIVE: We sought to test the hypothesis that BRD4 functions in a cell-autonomous and signal-responsive manner to control activation of cardiac fibroblasts, which are the major extracellular matrix-producing cells of the heart. METHODS AND RESULTS: RNA-sequencing, mass spectrometry, and cell-based assays employing primary adult rat ventricular fibroblasts demonstrated that BRD4 functions as an effector of TGF-ß (transforming growth factor-ß) signaling to stimulate conversion of quiescent cardiac fibroblasts into Periostin (Postn)-positive cells that express high levels of extracellular matrix. These findings were confirmed in vivo through whole-transcriptome analysis of cardiac fibroblasts from mice subjected to transverse aortic constriction and treated with the small molecule BRD4 inhibitor, JQ1. Chromatin immunoprecipitation-sequencing revealed that BRD4 undergoes stimulus-dependent, genome-wide redistribution in cardiac fibroblasts, becoming enriched on a subset of enhancers and super-enhancers, and leading to RNA polymerase II activation and expression of downstream target genes. Employing the Sertad4 (SERTA domain-containing protein 4) locus as a prototype, we demonstrate that dynamic chromatin targeting of BRD4 is controlled, in part, by p38 MAPK (mitogen-activated protein kinase) and provide evidence of a critical function for Sertad4 in TGF-ß-mediated cardiac fibroblast activation. CONCLUSIONS: These findings define BRD4 as a central regulator of the pro-fibrotic cardiac fibroblast phenotype, establish a p38-dependent signaling circuit for epigenetic reprogramming in heart failure, and uncover a novel role for Sertad4. The work provides a mechanistic foundation for the development of BRD4 inhibitors as targeted anti-fibrotic therapies for the heart.

Mining the Proteome Associated with Rheumatic and Autoimmune Diseases.

A steady increase in the incidence of osteoarthritis and other rheumatic diseases has been observed in recent decades, including autoimmune conditions such as rheumatoid arthritis, spondyloarthropathies, systemic lupus erythematosus, systemic sclerosis, and Sjögren's syndrome. Rheumatic and autoimmune diseases (RADs) are characterized by the inflammation of joints, muscles, or other connective tissues. In addition to often experiencing debilitating mobility and pain, RAD patients are also at a higher risk of suffering comorbidities such as cardiovascular or infectious events. Given the socioeconomic impact of RADs, broad research efforts have been dedicated to these diseases worldwide. In the present work, we applied literature mining platforms to identify "popular" proteins closely related to RADs. The platform is based on publicly available literature. The results not only will enable the systematic prioritization of candidates to perform targeted proteomics studies but also may lead to a greater insight into the key pathogenic processes of these disorders.

Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.

Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic. We describe here a method to identify and rank protein-topic relationships by calculating the semantic similarity between a protein and a query term in the biomerical literature while adjusting for the impact and immediacy of associated research articles. We term the calculated metric the weighted copublication distance (WCD) and show that it compares well to related approaches in predicting benchmark protein lists in multiple biological processes. We used WCD to extract prioritized "popular proteins" across multiple cell types, subanatomical regions, and standardized vocabularies containing over 20 000 human disease terms. The collection of protein-disease associations across the resulting human "diseasome" supports data analytical workflows to perform reverse protein-to-disease queries and functional annotation of experimental protein lists. We envision that the described improvement to the popular proteins strategy will be useful for annotating protein lists and guiding method development efforts as well as generating new hypotheses on understudied disease proteins using bibliometric information.

Data-Driven Approach To Determine Popular Proteins for Targeted Proteomics Translation of Six Organ Systems.

Amidst the proteomes of human tissues lie subsets of proteins that are closely involved in conserved pathophysiological processes. Much of biomedical research concerns interrogating disease signature proteins and defining their roles in disease mechanisms. With advances in proteomics technologies, it is now feasible to develop targeted proteomics assays that can accurately quantify protein abundance as well as their post-translational modifications; however, with rapidly accumulating number of studies implicating proteins in diseases, current resources are insufficient to target every protein without judiciously prioritizing the proteins with high significance and impact for assay development. We describe here a data science method to prioritize and expedite assay development on high-impact proteins across research fields by leveraging the biomedical literature record to rank and normalize proteins that are popularly and preferentially published by biomedical researchers. We demonstrate this method by finding priority proteins across six major physiological systems (cardiovascular, cerebral, hepatic, renal, pulmonary, and intestinal). The described method is data-driven and builds upon the collective knowledge of previous publications referenced on PubMed to lend objectivity to target selection. The method and resulting popular protein lists may also be useful for exploring biological processes associated with various physiological systems and research topics, in addition to benefiting ongoing efforts to facilitate the broad translation of proteomics technologies.

Cardiovascular proteomics in the era of big data: experimental and computational advances.

Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted.

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.

Integration of cardiac proteome biology and medicine by a specialized knowledgebase.

RATIONALE: Omics sciences enable a systems-level perspective in characterizing cardiovascular biology. Integration of diverse proteomics data via a computational strategy will catalyze the assembly of contextualized knowledge, foster discoveries through multidisciplinary investigations, and minimize unnecessary redundancy in research efforts. OBJECTIVE: The goal of this project is to develop a consolidated cardiac proteome knowledgebase with novel bioinformatics pipeline and Web portals, thereby serving as a new resource to advance cardiovascular biology and medicine. METHODS AND RESULTS: We created Cardiac Organellar Protein Atlas Knowledgebase (COPaKB; www.HeartProteome.org), a centralized platform of high-quality cardiac proteomic data, bioinformatics tools, and relevant cardiovascular phenotypes. Currently, COPaKB features 8 organellar modules, comprising 4203 LC-MS/MS experiments from human, mouse, drosophila, and Caenorhabditis elegans, as well as expression images of 10,924 proteins in human myocardium. In addition, the Java-coded bioinformatics tools provided by COPaKB enable cardiovascular investigators in all disciplines to retrieve and analyze pertinent organellar protein properties of interest. CONCLUSIONS: COPaKB provides an innovative and interactive resource that connects research interests with the new biological discoveries in protein sciences. With an array of intuitive tools in this unified Web server, nonproteomics investigators can conveniently collaborate with proteomics specialists to dissect the molecular signatures of cardiovascular phenotypes.

Substrate- and isoform-specific proteome stability in normal and stressed cardiac mitochondria.

RATIONALE: Mitochondrial protein homeostasis is an essential component of the functions and oxidative stress responses of the heart. OBJECTIVE: To determine the specificity and efficiency of proteome turnover of the cardiac mitochondria by endogenous and exogenous proteolytic mechanisms. METHODS AND RESULTS: Proteolytic degradation of the murine cardiac mitochondria was assessed by 2-dimensional differential gel electrophoresis and liquid chromatography-tandem mass spectrometry. Mitochondrial proteases demonstrated a substrate preference for basic protein variants, which indicates a possible recognition mechanism based on protein modifications. Endogenous mitochondrial proteases and the cytosolic 20S proteasome exhibited different substrate specificities. CONCLUSIONS: The cardiac mitochondrial proteome contains low amounts of proteases and is remarkably stable in isolation. Oxidative damage lowers the proteolytic capacity of cardiac mitochondria and reduces substrate availability for mitochondrial proteases. The 20S proteasome preferentially degrades specific substrates in the mitochondria and may contribute to cardiac mitochondrial proteostasis.

Metabolic labeling reveals proteome dynamics of mouse mitochondria.

Mitochondrial dysfunction is associated with many human diseases. Mitochondrial damage is exacerbated by inadequate protein quality control and often further contributes to pathogenesis. The maintenance of mitochondrial functions requires a delicate balance of continuous protein synthesis and degradation, i.e. protein turnover. To understand mitochondrial protein dynamics in vivo, we designed a metabolic heavy water ((2)H(2)O) labeling strategy customized to examine individual protein turnover in the mitochondria in a systematic fashion. Mice were fed with (2)H(2)O at a minimal level (<5% body water) without physiological impacts. Mitochondrial proteins were analyzed from 9 mice at each of the 13 time points between 0 and 90 days (d) of labeling. A novel multiparameter fitting approach computationally determined the normalized peak areas of peptide mass isotopomers at initial and steady-state time points and permitted the protein half-life to be determined without plateau-level (2)H incorporation. We characterized the turnover rates of 458 proteins in mouse cardiac and hepatic mitochondria and found median turnover rates of 0.0402 d(-1) and 0.163 d(-1), respectively, corresponding to median half-lives of 17.2 d and 4.26 d. Mitochondria in the heart and those in the liver exhibited distinct turnover kinetics, with limited synchronization within functional clusters. We observed considerable interprotein differences in turnover rates in both organs, with half-lives spanning from hours to months (≈ 60 d). Our proteomics platform demonstrates the first large-scale analysis of mitochondrial protein turnover rates in vivo, with potential applications in translational research.

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection.

The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the isotopomer patterns of deuterium labeled peptides continue to be a subject of research. Here we describe a method, which comprises a reverse lookup of numerically approximated peptide isotope envelopes, coupled to the selection of optimal isotopomer pairs based on peptide sequence, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments. We validated this approach using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes. We then re-analyzed 17 proteome-wide turnover experiments from four mouse organs, and showed that the method increases the coverage of well-fitted peptides in protein turnover experiments by 25-82%. The method is implemented in the Riana software tool for protein turnover analysis, and may avail ongoing efforts to study the synthesis and degradation kinetics of proteins in animals on a proteome-wide scale. What's new: We describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25-82%.

A Ratiometric Catalog of Protein Isoform Shifts in the Cardiac Fetal Gene Program.

The fetal genetic program orchestrates cardiac development and the re-expression of fetal genes is thought to underlie cardiac disease and adaptation. Here, a proteomics ratio test using mass spectrometry is applied to find protein isoforms with statistically significant usage differences in the fetal vs. postnatal mouse heart. Changes in isoform usage ratios are pervasive at the protein level, with 104 significant events observed, including 88 paralog-derived isoform switching events and 16 splicing-derived isoform switching events between fetal and postnatal hearts. The ratiometric proteomic comparisons rediscovered hallmark fetal gene signatures including a postnatal switch from fetal ß (MYH7) toward ɑ (MYH6) myosin heavy chains and from slow skeletal muscle (TNNI1) toward cardiac (TNNI3) troponin I. Altered usages in metabolic proteins are prominent, including a platelet to muscle phosphofructokinase (PFKP - PFKM), enolase 1 to 3 (ENO1 - ENO3), and alternative splicing of pyruvate kinase M2 toward M1 (PKM2 - PKM1) isoforms in glycolysis. The data also revealed a parallel change in mitochondrial proteins in cardiac development, suggesting the shift toward aerobic respiration involves also a remodeling of the mitochondrial protein isoform proportion. Finally, a number of glycolytic protein isoforms revert toward their fetal forms in adult hearts under pathological cardiac hypertrophy, suggesting their functional roles in adaptive or maladaptive response, but this reversal is partial. In summary, this work presents a catalog of ratiometric protein markers of the fetal genetic program of the mouse heart, including previously unreported splice isoform markers.