Browsing by Author "Chen, Wenrong"

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    Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations
    (American Chemical Society, 2023) Chen, Wenrong; Ding, Zhengming; Zang, Yong; Liu, Xiaowen; BioHealth Informatics, School of Informatics and Computing
    Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledge bases, such as UniProt, provide valuable information for PTM characterization and verification. Here, we present a software pipeline PTM-TBA (PTM characterization by Top-down and Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as PTM annotations. We assessed PTM-TBA using a technical triplicate of bottom-up and top-down MS data of SW480 cells. On average, database search of the top-down MS data identified 2000 mass shifts, 814.5 (40.7%) of which were matched to 11 common PTMs and 423 of which were localized. Of the mass shifts identified by top-down MS, PTM-TBA verified 435 mass shifts using the bottom-up MS data and UniProt annotations.
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    Computational Methods for Proteoform Identification and Characterization Using Top-Down Mass Spectrometry
    (2023-12) Chen, Wenrong; Yan, Jingwen; Wang, Juexin; Wan, Jun; Zang, Yong; Luo, Xiao; Liu, Xiaowen
    Proteoforms, distinct molecular forms of proteins, arise due to numerous factors such as genetic mutations, differential gene expression, alternative splicing, and a range of biological processes. These proteoforms are often characterized by primary structural variances such as amino acid substitutions, terminal truncations, and post-translational modifications (PTMs). Proteoforms from the same proteins can manifest varied functional behaviors based on the specific alterations. The complexity inherent to proteoforms has elevated the significance of top-down mass spectrometry (MS) due to its proficiency in providing intricate sequence information for these intact proteoforms. During a typical top-down MS experiment, intact proteoforms are separated through platforms like liquid chromatography (LC) or capillary zone electrophoresis (CZE) prior to tandem mass spectrometry (MS/MS) analysis. Despite advancements in instruments and protocols for top-down MS, computational challenges persist, with software tool development still in its early stage. In this dissertation, our research revolves around three primary goals, all aimed at refining proteoform characterization. First, we bridge RNA-Seq with top-down MS for a better proteoform identification. We propose TopPG, an innovative proteogenomic tool which is tailored to generate proteoform sequence databases from genetic and splicing variations explicitly for top-down MS in contrast to traditional approaches. Second, to boost the accuracy of proteoform detection, we utilize machine learning methods to predict proteoform retention and migration times in top-down MS, an area previously overshadowed by bottom-up MS paradigms. critically evaluating models in a realm traditionally dominated by bottom-up MS methodologies. Lastly, recognizing the indispensable role of post-translational modifications (PTMs) on cellular functions, we introduce PTM-TBA. This tool integrates the complementary strengths of both top-down and bottom-up MS, augmented with annotations, building a comprehensive strategy for precise PTM identification and localization.
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    Deep top-down proteomics revealed significant proteoform-level differences between metastatic and nonmetastatic colorectal cancer cells
    (American Association for the Advancement of Science, 2022) McCool, Elijah N.; Xu, Tian; Chen, Wenrong; Beller, Nicole C.; Nolan, Scott M.; Hummon, Amanda B.; Liu, Xiaowen; Sun, Liangliang; BioHealth Informatics, School of Informatics and Computing
    Understanding cancer metastasis at the proteoform level is crucial for discovering previously unknown protein biomarkers for cancer diagnosis and drug development. We present the first top-down proteomics (TDP) study of a pair of isogenic human nonmetastatic and metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). We identified 23,622 proteoforms of 2332 proteins from the two cell lines, representing nearly fivefold improvement in the number of proteoform identifications (IDs) compared to previous TDP datasets of human cancer cells. We revealed substantial differences between the SW480 and SW620 cell lines regarding proteoform and single amino acid variant (SAAV) profiles. Quantitative TDP unveiled differentially expressed proteoforms between the two cell lines, and the corresponding genes had diversified functions and were closely related to cancer. Our study represents a pivotal advance in TDP toward the characterization of human proteome in a proteoform-specific manner, which will transform basic and translational biomedical research.
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    Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry
    (American Chemical Society, 2022) Chen, Wenrong; McCool, Elijah N.; Sun, Liangliang; Zang, Yong; Ning, Xia; Liu, Xiaowen; BioHealth Informatics, School of Informatics and Computing
    Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.
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    Pilot Evaluation of the Long-Term Reproducibility of Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics of a Complex Proteome Sample
    (American Chemical Society, 2024) Sadeghi, Seyed Amirhossein; Chen, Wenrong; Wang, Qianyi; Wang, Qianjie; Fang, Fei; Liu, Xiaowen; Sun, Liangliang; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Mass spectrometry (MS)-based top-down proteomics (TDP) has revolutionized biological research by measuring intact proteoforms in cells, tissues, and biofluids. Capillary zone electrophoresis-tandem MS (CZE-MS/MS) is a valuable technique for TDP, offering a high peak capacity and sensitivity for proteoform separation and detection. However, the long-term reproducibility of CZE-MS/MS in TDP remains unstudied, which is a crucial aspect for large-scale studies. This work investigated the long-term qualitative and quantitative reproducibility of CZE-MS/MS for TDP for the first time, focusing on a yeast cell lysate. Over 1000 proteoforms were identified per run across 62 runs using one linear polyacrylamide (LPA)-coated separation capillary, highlighting the robustness of the CZE-MS/MS technique. However, substantial decreases in proteoform intensity and identification were observed after some initial runs due to proteoform adsorption onto the capillary inner wall. To address this issue, we developed an efficient capillary cleanup procedure using diluted ammonium hydroxide, achieving high qualitative and quantitative reproducibility for the yeast sample across at least 23 runs. The data underscore the capability of CZE-MS/MS for large-scale quantitative TDP of complex samples, signaling its readiness for deployment in broad biological applications. The MS RAW files were deposited in ProteomeXchange Consortium with the data set identifier of PXD046651.
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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.