Browsing by Author "Ansong, Charles"

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    Characterization of proteoforms with unknown post-translational modi cations using the MIScore
    (ACS, 2016) Kou, Qiang; Zhu, Binhai; Wu, Si; Ansong, Charles; Tolić, Nikola; Paša-Tolić, Ljiljana; Liu, Xiaowen; Department of Biohealth Informatics, School of Informatics and Computing
    Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform–spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.
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    Regulation of β-cell death by ADP-ribosylhydrolase ARH3 via lipid signaling in insulitis
    (Springer Nature, 2024-02-21) Sarkar, Soumyadeep; Deiter, Cailin; Kyle, Jennifer E.; Guney, Michelle A.; Sarbaugh, Dylan; Yin, Ruichuan; Li, Xiangtang; Cui, Yi; Ramos‑Rodriguez, Mireia; Nicora, Carrie D.; Syed, Farooq; Juan‑Mateu, Jonas; Muralidharan, Charanya; Pasquali, Lorenzo; Evans‑Molina, Carmella; Eizirik, Decio L.; Webb‑Robertson, Bobbie‑Jo M.; Burnum‑Johnson, Kristin; Orr, Galya; Laskin, Julia; Metz, Thomas O.; Mirmira, Raghavendra G.; Sussel, Lori; Ansong, Charles; Nakayasu, Ernesto S.; Pediatrics, School of Medicine
    Background: Lipids are regulators of insulitis and β-cell death in type 1 diabetes development, but the underlying mechanisms are poorly understood. Here, we investigated how the islet lipid composition and downstream signaling regulate β-cell death. Methods: We performed lipidomics using three models of insulitis: human islets and EndoC-βH1 β cells treated with the pro-inflammatory cytokines interlukine-1β and interferon-γ, and islets from pre-diabetic non-obese mice. We also performed mass spectrometry and fluorescence imaging to determine the localization of lipids and enzyme in islets. RNAi, apoptotic assay, and qPCR were performed to determine the role of a specific factor in lipid-mediated cytokine signaling. Results: Across all three models, lipidomic analyses showed a consistent increase of lysophosphatidylcholine species and phosphatidylcholines with polyunsaturated fatty acids and a reduction of triacylglycerol species. Imaging assays showed that phosphatidylcholines with polyunsaturated fatty acids and their hydrolyzing enzyme phospholipase PLA2G6 are enriched in islets. In downstream signaling, omega-3 fatty acids reduce cytokine-induced β-cell death by improving the expression of ADP-ribosylhydrolase ARH3. The mechanism involves omega-3 fatty acid-mediated reduction of the histone methylation polycomb complex PRC2 component Suz12, upregulating the expression of Arh3, which in turn decreases cell apoptosis. Conclusions: Our data provide insights into the change of lipidomics landscape in β cells during insulitis and identify a protective mechanism by omega-3 fatty acids.
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    Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation
    (Springer Nature, 2021) Nakayasu, Ernesto S.; Gritsenko, Marina; Piehowski, Paul D.; Gao, Yuqian; Orton, Daniel J.; Schepmoes, Athena A.; Fillmore, Thomas L.; Frohnert, Brigitte I.; Rewers, Marian; Krischer, Jeffrey P.; Ansong, Charles; Suchy-Dicey, Astrid M.; Evans-Molina, Carmella; Qian, Wei-Jun; Webb-Robertson, Bobbie-Jo M.; Metz, Thomas O.; Pediatrics, School of Medicine
    Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.