Cardiac Proteomics and Signalling Laboratory

Overview of Research Projects

Our overall research interest is to advance cardiovascular medicine through a better understanding on the regulatory principles of cardiac proteins on a global scale. A major current focus is to develop proteomics and data science methods to interrogate how changes in protein expression orchestrate higher physiological functions in normal and diseased hearts.

Focus Area I: Data Science and Bioinformatics Platforms

Our lab hosts the NIH National Center of Excellence for Big Data to Knowledge at UCLA. The mission of our Center is to develop new data science solutions and software to promote community participation and in biomedical data analysis and annotation of large-scale biomedical data. Current emphases include an integrated cardiovascular proteomics knowledgebase that connects raw proteomics data with curated biomedical knowledge to facilitate data analyses and annotation, as well as text-mining tools to create computable info-boxes from multidimensional data including electronic medical records.

O-PTM Biology Project: Oxidative post-translational modifications (O-PTMs) of proteins are highly prevalent cellular features that enable diverse and nuanced functions, and elicit critical effects on human health and disease. Thus far, at least 35 different types of O-PTMs have been reported in various model systems and in humans (link). However, very few studies have been able to elucidate a molecular fingerprint of O-PTMs, mainly due to the lack of sophistication of mass spec and data science technologies. To address this challenge, we collaborate with Drs. Alex Bui, Wei Wang, Karol Watson from UCLA, Dr. Jiawei Han from UIUC, Dr. Lan Huang from UCI, Dr. John Yates from Scripps, and Henning Hermjakob from EMBL-EBI and utilize our expertise spans across O-PTM biology, MS/MS based O-PTM technology, database & knowledgebase query, ML analytics, KG construction, and text mining. Our project aims to comprehensively characterize the molecular landscape, elucidate mechanistic insights, and define the translational value of O-PTMs in biomedical processes, cells, organelles, model systems, and humans.

Integrated proteomics knowledgebase and data access infrastructure: A current bottleneck for widespread adoption of proteomics technologies in cardiovascular medicine is the limited accessibility of bioinformatics tools and the quality and quantity of protein functional annotations. To address these shortcomings we created the Cardiac Organellar Protein Atlas Knowledgebase (COPaKB), a knowledgebase aimed at connecting proteomics data with cardiovascular biology knowledge. We are implementing in COPaKB a curated spectra library to enable researchers to analyze raw proteomics datasets without hefty computational power requirements. This library will also be equipped with an index to integrate orthogonal properties of cardiac proteins, which serves to simultaneously annotate the protein list with functional and phenotypic properties as the proteins are identified from raw data. In parallel, we are building a unified access point to locate and acquire transcript, protein, and metabolite datasets in collaboration with Henning Hermjakob from EMBL-EBI. A major focus is to develop a proxy to bridge COPaKB and other specialized repositories, which will serve as a unified portal to integrate gene, protein, and metabolite expression data for analysis and visualization in multi-scale interaction networks and pathway models. We are also exploring cloud-based computational infrastructures optimized for Big Data to support remote, high-performance data access and analysis that will transform the research sharing infrastructure and encourage interoperability in biomedical research. ultimate goal of this long-term project is to provide a comprehensive platform to bridge traditional data?driven proteomic studies and hypothesis?driven investigations widely employed by the cardiovascular community.

Crowdsourcing cardiovascular research knowledge and annotations: Current annotations on genes, proteins, metabolites, and their functions in health and diseases are fragmented and incomplete, causing valuable information to be scattered across multiple websites, databases, local files, and research articles. To address this challenge, we are building upon the growing trend of crowdsourcing to enable collaborative annotation on existing datasets. Working with Andrew Su from TSRI and Carlo Zaniolo from the UCLA Department of Computer Science, we are developing crowdsourcing and text-mining tools to efficiently define relationships between genes, proteins, diseases, and drugs from biomedical literature, with a particular emphasis on cardiovascular health. We are developing text-mining tools to build info-boxes for multi-dimensional data files (e.g., electronic medical records and imaging), digital files from (blood work, text for symptoms, ECG, etc.)  To prioritize our data annotation efforts and to gauge community interests, our laboratory created PubSieve, a publicly available software which tracks genes and proteins of interest to a particular research community via analysis of relevant PubMed records. These community and computational efforts are designed to allow end-users to obtain concise and accurate information on cardiovascular health and disease, and thus facilitating the translation of Big Data to biomedical knowledge.

Bioinformatics software tools for next-generation proteomics applications: Aside from protein expression, the proteome is defined by numerous dynamic parameters that currently remain underexplored. In collaboration with John Yates from TSRI, Wei Wang from the UCLA Department of Computer Science, and Zhilin Qu from the UCLA Department of Bioengineering, we aim to create advanced proteomics software tools and pattern recognition algorithms to support applications in protein-protein interactions, spatial dynamics, and to connect molecular data with functional information. Our laboratory recently created ProTurn, a bioinformatics software application designed to facilitate the analysis of heavy water-labeled mass spectrometry datasets for protein turnover measurements. Our next step is to explore machine learning and data mining algorithms to infer spatiotemporal models from protein data, and to integrate molecular profiles with biomedical variables. Our long-term goal is to implement, deliver, and execute these tools and algorithms on the cloud to provide easy access to cardiovascular researchers and clinicians, as well as the broader biomedical community.

Relevant publications:

Focus Area II: Cardiovascular Proteomics and Metabolomics

Our lab is interested in understanding the mechanisms of heart diseases and injuries through interrogating the large-scale alterations of protein expression and dynamics during disease development, and to infer novel disease proteins by combining multi-scale molecular parameters. As host to the NIH NHLBI Proteomics Center at UCLA, we develop advanced mass spectrometry techniques to understand the organization and functions of cardiac proteomes in healthy and diseased hearts. A major emphasis is to expand the number of proteome parameters one can observe on a large-scale, including quantification of proteome-wide post-translational modification, localizations, and temporal dynamics, in order to detect "hidden" disease signatures.

Large-scale analysis of protein turnover rates in mouse, human, and drosophila: Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Many potential disease associations in protein homeostasis may manifest in disrupted protein half-life but are masked in measurements of protein expression, yet method developments for quantitative assessments of protein kinetics lag behind that for the assessment of protein expression. For the past two years, our lab has been developing a workflow that integrates heavy water labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. A principal advantage of heavy water labeling is that it may be applied to diverse complex organisms from fly to human to discern in vivo protein turnover rates. With our custom methods, we are investigating how protein turnover alters and affects critical cellular processes in a mouse isoproterenol model of cardiac remodeling. The data are revealing a quantitative and longitudinal view of cardiac remodeling at the molecular level, where widespread kinetic regulations occur in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. In addition, we are developing a fly labeling protocol to investigate how protein turnover permutes in fly genetic strains with perturbed proteolysis in cardiac mitochondria. In parallel, we are interested in translating heavy water study to human subjects to identify new candidate biomarkers in human heart diseases.

Plasma metabolite profiles of heart failure patients: Mechanical circulatory support (MCS) is a promising bridge or destination therapy for heart failure patients, but post-surgery prognosis for the majority of the patients remains poor. We are utilizing multiple reaction monitoring (MRM)-based mass spectrometry methods to conduct metabolomics profiling of heart failure patients before surgery and at multiple time points after MCS implantation, with the goal of determining whether any differentially expressed metabolites in the plasma of these patients may be used to predict clinical outcomes. The technology platform allows upwards of 500 patient plasma metabolites belonging to multiple compound classes to be simultaneously quantified in high throughput, which can then be modeled to deduce a panel of potential prognostic marker proteins. We are examining potential pathogenic mechanisms from the metabolite profile, which may be integrated into therapeutic regiments to improve post-surgical survival rates in the patients.

Protein phosphorylation signaling in cardiac mitochondria: Protein phosphorylation is a key regulatory mechanism for modulating mitochondrial functions including respiratory rate and cell death gating, but our knowledge on phosphorylation signaling inside the mitochondria are woefully underdeveloped compared to the well-characterized kinase cascades in the cytosol, plasma membrane, and the nucleus. Our laboratory performed a comprehensive characterization of the cardiac mitochondrial phosphoproteome in the context of mitochondrial functional pathways. A platform employing the complementary technologies of collision?induced dissociation (CID) and electron transfer dissociation (ETD) demonstrated successful identification of over 236 phosphorylation sites in respiratory chain, TCA cycle, and other key metabolic proteins, with 210 of these sites being novel. To elucidate the biological significance of the cardiac mitochondrial phosphoproteome, we developed over 100 MRM assays that allows the occupancy of these sites to be quantified in a sensitive and site-specific manner under different perturbations.

Relevant publications:

Focus Area III: Mitochondrial Biology of Heart Diseases

Our laboratory has a longstanding emphasis in understanding the regulations of cardiac mitochondria in energetics and metabolism, and their roles in the development of heart diseases. We utilize cutting-edge proteomics and biochemistry methods to understand the signaling pathways and organization of cardiac mitochondrial proteome in the heart, with particular interest in the regulatory relationships between protein expression level, organelle functions, and cardiac phenotypes.

Proteome design and function of cardiac mitochondria: Mitochondria are double?membrane organelles which are essential for cell metabolism, transport, biosynthesis and signaling. A central question regarding the role of mitochondrial biology in disease is how altering protein expression mechanistically impacts higher organelle functions and disease phenotypes. Although omics technology now enables simultaneous quantification of thousands of proteins, new bottlenecks are arising on how to effectively translate data into biological insights. To address this challenge, we are developing large-scale approaches for in-depth interpretations of the datasets of mitochondrial protein expression, including one of the most extensive catalogs of mitochondrial proteins to-date: a dataset of >1,600 distinct mitochondrial proteins and relative expression levels in mouse liver, mouse heart, human heart, and fly with 94 total replicates we previously generated. We functionally re-annotate datasets with up-to-date information using ClueGO, UniProt, PubMed, plus other sources and repositories. To determine how protein expression may correlate with functions, we search for a core proteome that is conserved amongst mitochondria populations. Unsupervised hierarchical clustering on the expression of these core mitochondrial proteins revealed five principal clusters of proteins significantly enriched in electron transport, mitochondrial organization, mitochondrial transport, protein degradation, and protein transport functions, respectively. The analysis indicates that members of these five core sub-proteomes co-vary significantly in their expression over diverse mitochondria types, whereas each of the functional categories is differentially regulated and contributes to specializations of organelle phenotypes. We further explore the distinct organizations of protein interactions in each system, and discuss how the homology of such interactomes may be used to elucidate similarities and differences in functional specialization and inform on model selection.

Mitochondrial anatomy and functions following left ventricular assist device support: Left ventricular assist devices (LVAD) have assumed an increasingly important role in the care of patients with end?stage heart failure. Support with an LVAD leads to improvements in cardiac function, normalization of left ventricular morphology, and reversal of myocyte molecular and biochemical defects. Working with Mario Deng at UCLA Department of Medicine, we are examining whether common detriments in cardiac mitochondrial functions in the failing hearts are reversed after LVAD support. Our data reveal that respiratory activity from LVAD?supported hearts show a significant increase compared to end?stage HF samples, whereas mitochondria isolated from LVAD?supported hearts, were less susceptible to calcium challenges. Currently we are interested in elucidating the molecular mechanisms by which LVAD is able to rescue higher physiological functions in order to further our understanding on the recuperative effect of mechanical unloading at the cellular level and identify potential targets for targeted therapy.

Protein degradation signaling: Increased reactive oxygen species is a hallmark of multiple heart disease etiologies that can lead to increased protein damage in the mitochondria. The ubiquitin-proteasome system degrades the majority of cardiac proteins as one of the means to removed damaged proteins. Recent evidences have emerged that it is also involved in the proteome homeostasis of mitochondrial proteins. We employ functional proteomics and metabolomics approaches to investigate multiple aspects of the cardiac mitochondrial proteolysis in depth. The data are revealing not only tremendous diversity and heterogeneity of proteolytic machineries, but also their potential substrate preferences and degradation signal recognition in the mitochondria. Ongoing studies will examine how molecular physiology can dictate the degrees of disease and injury, and how mitochondrial proteolysis permutes under a model of oxidative insults that mimics those seen in cardiac ischemic injuries. It is hoped that these studies will identify crucial points of perturbation that will lead to therapeutic targets and further understanding of cardiac diseases.

Relevant publications:

Crowdsourcing knowledge in heart disease: Anonymous Patient Survey