Browsing by Subject "Proteogenomics"

Now showing 1 - 5 of 5
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    Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer
    (American Society for Biochemistry and Molecular Biology, 2019-08-09) Zhan, Xiaohui; Cheng, Jun; Huang, Zhi; Han, Zhi; Helm, Bryan; Liu, Xiaowen; Zhang, Jie; Wang, Tian-Fu; Ni, Dong; Huang, Kun; Medicine, School of Medicine
    Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
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    Peptide ancestry informative markers in uterine neoplasms from women of European, African, and Asian ancestry
    (Elsevier, 2021-12-23) Bateman, Nicholas W.; Tarney, Christopher M.; Abulez, Tamara S.; Hood, Brian L.; Conrads, Kelly A.; Zhou, Ming; Soltis, Anthony R.; Teng, Pang-Ning; Jackson, Amanda; Tian, Chunqiao; Dalgard, Clifton L.; Wilkerson, Matthew D.; Kessler, Michael D.; Goecker, Zachary; Loffredo, Jeremy; Shriver, Craig D.; Hu, Hai; Cote, Michele; Parker, Glendon J.; Segars, James; Al-Hendy, Ayman; Risinger, John I.; Phippen, Neil T.; Casablanca, Yovanni; Darcy, Kathleen M.; Maxwell, G. Larry; Conrads, Thomas P.; O'Connor, Timothy D.; Medicine, School of Medicine
    Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R2 = 0.99). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues combined resulted in the quantitation of 62 pAIMs that correlate with patient race and genotype-confirmed ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. pAIMs will support generalized proteoancestry assessment as well as efforts investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.
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    Proteoform Identification by Combining RNA-Seq and Top-down Mass Spectrometry
    (American Chemical Society, 2021) Chen, Wenrong; Liu, Xiaowen; BioHealth Informatics, School of Informatics and Computing
    In proteogenomic studies, genomic and transcriptomic variants are incorporated into customized protein databases for the identification of proteoforms, especially proteoforms with sample-specific variants. Most proteogenomic research has been focused on combining genomic or transcriptomic data with bottom-up mass spectrometry data. In the last decade, top-down mass spectrometry has attracted increasing attention because of its capacity to identify various proteoforms with alterations. However, top-down proteogenomics, in which genomic or transcriptomic data are combined with top-down mass spectrometry data, has not been widely adopted, and there is still a lack of software tools for top-down proteogenomic data analysis. In this paper, we introduce TopPG, a proteogenomic tool for generating proteoform sequence databases with genetic alterations and alternative splicing events. Experiments on top-down proteogenomic data of DLD-1 colorectal cancer cells showed that TopPG coupled with database search confidently identified proteoforms with sample-specific alterations.
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    Proteome Landscape of Epithelial-to-Mesenchymal Transition (EMT) of Retinal Pigment Epithelium Shares Commonalities With Malignancy-Associated EMT
    (American Society for Biochemistry and Molecular Biology, 2021) Sripathi, Srinivasa R.; Hu, Ming-Wen; Turaga, Ravi Chakra; Mertz, Joseph; Liu, Melissa M.; Wan, Jun; Maruotti, Julien; Wahlin, Karl J.; Berlinicke, Cynthia A.; Qian, Jiang; Zack, Donald J.; Medical and Molecular Genetics, School of Medicine
    Stress and injury to the retinal pigment epithelium (RPE) often lead to dedifferentiation and epithelial-to-mesenchymal transition (EMT). These processes have been implicated in several retinal diseases, including proliferative vitreoretinopathy, diabetic retinopathy, and age-related macular degeneration. Despite the importance of RPE-EMT and the large body of data characterizing malignancy-related EMT, comprehensive proteomic studies to define the protein changes and pathways underlying RPE-EMT have not been reported. This study sought to investigate the temporal protein expression changes that occur in a human-induced pluripotent stem cell-based RPE-EMT model. We utilized multiplexed isobaric tandem mass tag labeling followed by high-resolution tandem MS for precise and in-depth quantification of the RPE-EMT proteome. We have identified and quantified 7937 protein groups in our tandem mass tag-based MS analysis. We observed a total of 532 proteins that are differentially regulated during RPE-EMT. Furthermore, we integrated our proteomic data with prior transcriptomic (RNA-Seq) data to provide additional insights into RPE-EMT mechanisms. To validate these results, we have performed a label-free single-shot data-independent acquisition MS study. Our integrated analysis indicates both the commonality and uniqueness of RPE-EMT compared with malignancy-associated EMT. Our comparative analysis also revealed that multiple age-related macular degeneration-associated risk factors are differentially regulated during RPE-EMT. Together, our integrated dataset provides a comprehensive RPE-EMT atlas and resource for understanding the molecular signaling events and associated biological pathways that underlie RPE-EMT onset. This resource has already facilitated the identification of chemical modulators that could inhibit RPE-EMT, and it will hopefully aid in ongoing efforts to develop EMT inhibition as an approach for the treatment of retinal disease.
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    SpectroGene: A Tool for Proteogenomic Annotations Using Top-Down Spectra
    (ACS Publications, 2016-01-04) Kolmogorov, Mikhail; Liu, Xiaowen; Pevzner, Pavel A.; BioHealth Informatics, School of Informatics and Computing
    In the past decade, proteogenomics has emerged as a valuable technique that contributes to the state-of-the-art in genome annotation; however, previous proteogenomic studies were limited to bottom-up mass spectrometry and did not take advantage of top-down approaches. We show that top-down proteogenomics allows one to address the problems that remained beyond the reach of traditional bottom-up proteogenomics. In particular, we show that top-down proteogenomics leads to the discovery of previously unannotated genes even in extensively studied bacterial genomes and present SpectroGene, a software tool for genome annotation using top-down tandem mass spectra. We further show that top-down proteogenomics searches (against the six-frame translation of a genome) identify nearly all proteoforms found in traditional top-down proteomics searches (against the annotated proteome). SpectroGene is freely available at http://github.com/fenderglass/SpectroGene .