Instructor, Cardiovascular Institute
Doctor of Philosophy, University of California Los Angeles, Physiology (2014)
Master of Philosophy, University of Hong Kong, Chemistry (2010)
Bachelor of Arts, University of California Berkeley, Molecular Biology (2006)
Systems-Wide Approaches in Induced Pluripotent Stem Cell Models.
Annual review of pathology
Human induced pluripotent stem cells (iPSCs) provide a renewable supply of patient-specific and tissue-specific cells for cellular and molecular studies of disease mechanisms. Combined with advances in various omics technologies, iPSC models can be used to profile the expression of genes, transcripts, proteins, and metabolites in relevant tissues. In the past 2 years, large panels of iPSC lines have been derived from hundreds of genetically heterogeneous individuals, further enabling genome-wide mapping to identify coexpression networks and elucidate gene regulatory networks. Here, we review recent developments in omics profiling of various molecular phenotypes and the emergence of human iPSCs as a systems biology model of human diseases. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease Volume 14 is January 24, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
View details for PubMedID 30379619
Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.
Journal of proteome research
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.
View details for PubMedID 30256117
Omics, Big Data, and Precision Medicine in Cardiovascular Sciences.
2018; 122 (9): 1165–68
View details for PubMedID 29700063
Integrated omics dissection of proteome dynamics during cardiac remodeling
2018; 9: 120
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.
View details for PubMedID 29317621
Communication and outreach
2018; 359 (6371): 26
View details for PubMedID 29301998
Diversity in science
2018; 359 (6371): 28
View details for Web of Science ID 000419324700050
Prolonged survival of transplanted stem cells after ischaemic injury via the slow release of pro-survival peptides from a collagen matrix
Nature Biomedical Engineering
2018; 2 (2): 104–13
Stem-cell-based therapies hold considerable promise for regenerative medicine. However, acute donor-cell death within several weeks after cell delivery remains a critical hurdle for clinical translation. Co-transplantation of stem cells with pro-survival factors can improve cell engraftment, but this strategy has been hampered by the typically short half-lives of the factors and by the use of Matrigel and other scaffolds that are not chemically defined. Here, we report a collagen-dendrimer biomaterial crosslinked with pro-survival peptide analogues that adheres to the extracellular matrix and slowly releases the peptides, significantly prolonging stem cell survival in mouse models of ischaemic injury. The biomaterial can serve as a generic delivery system to improve functional outcomes in cell-replacement therapy.
View details for DOI 10.1038/s41551-018-0191-4
View details for PubMedCentralID PMC5927627
- Connecting the Dots: From Big Data to Healthy Heart. Circulation 2016; 134 (5): 362–64
A large dataset of protein dynamics in the mammalian heart proteome.
2016; 3: 160015-?
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.
View details for DOI 10.1038/sdata.2016.15
View details for PubMedID 26977904
Spatial and temporal dynamics of the cardiac mitochondrial proteome
EXPERT REVIEW OF PROTEOMICS
2015; 12 (2): 133-146
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.
View details for DOI 10.1586/14789450.2015.1024227
View details for Web of Science ID 000351771000004
View details for PubMedID 25752359
View details for PubMedCentralID PMC4721584
Substrate- and Isoform-Specific Proteome Stability in Normal and Stressed Cardiac Mitochondria
2012; 110 (9): 1174-?
Mitochondrial protein homeostasis is an essential component of the functions and oxidative stress responses of the heart.To determine the specificity and efficiency of proteome turnover of the cardiac mitochondria by endogenous and exogenous proteolytic mechanisms.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.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.
View details for DOI 10.1161/CIRCRESAHA.112.268359
View details for Web of Science ID 000303406200012
View details for PubMedID 22456183