Caitlin H. Choi

Bio: Caitlin H. Choi is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Proteome & Ovarian cancer. The author has an hindex of 9, co-authored 9 publications receiving 850 citations.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

Abstract: To provide a detailed analysis of the molecular com-ponents and underlying mechanisms associatedwith ovarian cancer, we performed a comprehensivemass-spectrometry-based proteomic characteriza-tion of 174 ovarian tumors previously analyzed byThe Cancer Genome Atlas (TCGA), of which 169were high-grade serous carcinomas (HGSCs). Inte-grating our proteomic measurements with thegenomic data yielded a number of insights into dis-ease, such as how different copy-number alterna-tionsinfluencetheproteome,theproteinsassociatedwith chromosomal instability, the sets of signalingpathways that diverse genome rearrangementsconverge on, and the ones most associated withshort overall survival. Specific protein acetylationsassociated with homologous recombination defi-ciency suggest a potential means for stratifying pa-tients for therapy. In addition to providing a valuableresource,thesefindingsprovideaviewofhowtheso-maticgenomedrivesthecancerproteomeandasso-ciations between protein and post-translationalmodification levels and clinical outcomes in HGSC.

Overexpression of α (1,6) fucosyltransferase associated with aggressive prostate cancer

Abstract: Aberrant protein glycosylation is known to be associated with the development of cancers. The aberrant glycans are produced by the combined actions of changed glycosylation enzymes, substrates and transporters in glycosylation synthesis pathways in cancer cells. To identify glycosylation enzymes associated with aggressive prostate cancer (PCa), we analyzed the difference in the expression of glycosyltransferase genes between aggressive and non-aggressive PCa. Three candidate genes encoding glycosyltransferases that were elevated in aggressive PCa were subsequently selected. The expression of the three candidates was then further evaluated in androgen-dependent (LNCaP) and androgen-independent (PC3) PCa cell lines. We found that the protein expression of one of the glycosyltransferases, α (1,6) fucosyltransferase (FUT8), was only detected in PC3 cells, but not in LNCaP cells. We further showed that FUT8 protein expression was elevated in metastatic PCa tissues compared to normal prostate tissues. In addition, using tissue microarrays, we found that FUT8 overexpression was statistically associated with PCa with a high Gleason score. Using PC3 and LNCaP cells as models, we found that FUT8 overexpression in LNCaP cells increased PCa cell migration, while loss of FUT8 in PC3 cells decreased cell motility. Our results suggest that FUT8 may be associated with aggressive PCa and thus is potentially useful for its prognosis.

MicroRNAs downregulate Bag of marbles to ensure proper terminal differentiation in the Drosophila male germline

Abstract: In many adult stem cell lineages, the continuous production of functional differentiated cells depends on the maintenance of progenitor cells in an undifferentiated and proliferative state, as well as the subsequent commitment to proper terminal differentiation. In the Drosophila male germline stem cell (GSC) lineage, a key differentiation factor, Bag of marbles (Bam), is required for the transition from proliferative spermatogonia to differentiating spermatocytes. We show that bam mRNA, but not Bam, is present in spermatocytes, suggesting that bam is regulated post-transcriptionally. Consistent with this, repression of Bam accumulation is achieved by microRNAs via the bam 3′UTR. When the bam 3′UTR was substituted with the 3′UTR of a constitutively expressed α-Tubulin, Bam became stabilized in spermatocytes. Moreover, such a persistent expression of Bam in spermatocytes was recapitulated by specifically mutating the putative miR-275/miR-306 recognition site at the bam 3′UTR. In addition, overexpression of miR-275 or miR-306 in spermatogonial cells resulted in a delay of the proliferation-to-differentiation transition and resembled the bam loss-of-function phenotype, suggesting that these microRNAs are sufficient to downregulate Bam. Finally, the failure of Bam downregulation in spermatocytes affected spermatid terminal differentiation and resulted in increased male sterility. Our results demonstrate that microRNAs control the stem cell differentiation pathway through regulating Bam, the downregulation of which is crucial for proper spermatid terminal differentiation.

Identification of sialylated glycoproteins from metabolically oligosaccharide engineered pancreatic cells

Abstract: In this study, we investigated the use of metabolic oligosaccharide engineering and bio-orthogonal ligation reactions combined with lectin microarray and mass spectrometry to analyze sialoglycoproteins in the SW1990 human pancreatic cancer line. Specifically, cells were treated with the azido N-acetylmannosamine analog, 1,3,4-Bu3ManNAz, to label sialoglycoproteins with azide-modified sialic acids. The metabolically labeled sialoglyproteins were then biotinylated via the Staudinger ligation, and sialoglycopeptides containing azido-sialic acid glycans were immobilized to a solid support. The peptides linked to metabolically labeled sialylated glycans were then released from sialoglycopeptides and analyzed by mass spectrometry; in parallel, the glycans from azido-sialoglycoproteins were characterized by lectin microarrays. This method identified 75 unique N-glycosite-containing peptides from 55 different metabolically labeled sialoglycoproteins of which 42 were previously linked to cancer in the literature. A comparison of two of these glycoproteins, LAMP1 and ORP150, in histological tumor samples showed overexpression of these proteins in the cancerous tissue demonstrating that our approach constitutes a viable strategy to identify and discover sialoglycoproteins associated with cancer, which can serve as biomarkers for cancer diagnosis or targets for therapy.

LinkedOmics: analyzing multi-omics data within and across 32 cancer types.

Abstract: The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org.

MicroRNAs: Target Recognition and Regulatory Functions

Abstract: MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

Integrative omics for health and disease.

Abstract: Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Multi-omics Data Integration, Interpretation, and Its Application.

Abstract: To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.