질량분석기 데이터 상의 번역 후 변형 식별 향상을 위한 진단 이온을 활용한 체계적인 접근법 연구

Post-translational modifications (PTMs) play indispensable roles in a wide array of cellular regulatory events. More than 300 types of PTMs have been reported to occur in vivo, each with potentially different sets of substrate proteins, dynamics, and biological consequences. Due to the enormous complexity of PTMs, systems wide study of PTMs is an active area of research in the field of proteomics. For a more comprehensive understanding of the human PTM proteome, a taxonomy of the types of PTMs and their exact substrate proteins/sites ought to be carried out above all else. This, in turn, requires a large-scale and confident identification of PTMs. Mass spectrometry (MS)-based proteomics has enabled a systems-wide identification of proteins and their amino acid residues that are affected by various PTMs. However, several important limitations and challenges in sample preparation, MS analysis, and bioinformatics have impeded a deeper and wider characterization of PTMs. To tackle some of the major challenges in bioinformatic analysis of PTMs including the high false positive rate of PTMs and the heavy computational burden of database search, we developed methods using diagnostic ions for PTMs. First, we developed a statistical prediction model for the confident identification of citrullination. We systematically identified diagnostic ions for citrullination, and used these diagnostic ions to build a prediction model for assessing the validity of citrullinated PSMs identified by database searching. Application of our model to real biological data showed significantly alleviated false positive rate. We further extended our approach to find false negative citrullination and successfully identified additional citrullinated peptides that the database searching failed to identify. Second, we proposed a database search strategy for the large-scale identification of PTMs using a conventional standard search tool. We introduced a post-acquisition spectra filtering approach to effectively reduce the size of input MS data by retaining only the spectra that contain diagnostic ions of certain PTMs, thus rendering the use of standard search approach for identifying hundreds of PTMs practical. In summary, we proposed methods utilizing PTM diagnostic ions for the large-scale and confident identification of PTMs. Constant improvement of the suggested frameworks will enable achieving a more comprehensive and accurate identification of PTMs in the human proteome.|본 논문은 질량분석 데이터 상의 번역 후 변형 식별에 활용 가능한 진단 이온 기반 예측 모델 및 데이터 필터링 프로토콜에 대해 다룬다. 번역 후 변형은 세포내 여러 조절 작용에 관여하는 것으로 알려져 있다. 300 여 종의 번역 후 변형이 보고되어있고, 각각은 서로 다른 작용 단백질과 다이나믹스, 그리고 생물학적인 효과를 가진다. 이러한 복잡성 때문에, 사람의 번역 후 변형 단백체에 관한 연구는 여전히 초기 단계에 있다. 이를 실현시키기 위해서는 먼저 세포내 번역 후 변형의 종류와 그것들의 작용 단백질 및 아미노산 위치를 광범위하고 정확하게 파악하는 것이 중요하다. 질량분석기 기반 단백체 연구는 시스템적인 번역 후 변형 연구를 가능하게 만들었다. 하지만 샘플 준비 과정, 질량분석 과정, 그리고 생물정보학 분석 과정에서의 여러가지 문제점과 한계점 때문에 번역 후 변형에 대한 시스템적인 연구는 여전히 몇몇 잘 알려진 번역 후 변형에 국한되어왔다. 그 중에서도 생물정보학 분석 과정에서의 여러 문제점들을 해결하기 위해, 우리는 번역 후 변형의 진단 이온을 활용한 방법론을 개발하였다. 첫째, 우리는 질량분석 데이터 상의 번역 후 변형의 일종인 시트룰린화의 정확한 식별을 위해 통계적인 예측 모델을 개발하였다. 먼저 시트룰린화의 진단 이온을 체계적으로 찾아내었고, 그것들을 기반으로 예측 모델을 만들어 데이터베이스 서치가 찾아낸 시트룰린화 결과를 판단하는데 사용하였다. 또한, 실제 생물학 샘플에서 유래된 질량분석 데이터들에 우리가 개발한 예측 모델을 적용하여 거짓 양성과 거짓 음성 문제를 성공적으로 완화시켰다. 둘째, 우리는 통상적으로 사용되는 스탠다드 데이터베이스 서치 툴을 이용한 광범위한 번역 후 변형 식별을 가능케하는 서치 방법을 고안하였다. 질량분석 데이터에서 특정 번역 후 변형 진단 이온을 포함하는 데이터만 필터링하여 이것들을 데이터베이스 서치에 사용하는 것으로, 수백 종의 번역 후 변형에 대한 서치를 가능케하였다. 종합하면, 우리는 번역 후 변형 진단 이온을 활용하여 질량분석 데이터상의 번역 후 변형의 광범위하고 정확한 식별을 가능케하는 방법들을 개발하였다. 여기서 소개된 방법들은 지속적인 향상이 필요하며, 이는 사람의 번역 후 변형 단백체를 이해하는데 유용하게 활용될 것으로 예상한다.YAbstract i List of Contents ii List of Tables and Figures iii Chapter 1. Introduction 1 1.1 Post-search PSM evaluation for the confident identification of authentic modification 4 1.2 Pre-search PTM screening for the large-scale identification of PTMs 5 Chapter 2. Systematic search for diagnostic ions for citrullination 6 2.1 Introduction 6 2.2 Results 8 2.3 Discussion 14 2.4 Methods 15 Chapter 3. Development and application of a statistical model for the confident identification of citrullination 27 3.1 Introduction 27 3.2 Results 27 3.3 Discussion 33 3.4 Methods 33 Chapter 4. Development of a search strategy for the large-scale identification of >200 types of PTMs 44 4.1 Introduction 44 4.2 Results 46 4.3 Discussion 49 4.4 Methods 49 Chapter 5. Conclusion 58 REFERENCES 60 요 약 문 63 CURRICULUM VITAE 64 ACKNOWLEDGMENT 66DoctordCollectio

A User‐Friendly Visualization Tool for Multi‐Omics Data

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated large multi-omic datasets for various cancers. Each dataset consists of common and differential data types, including genomics, epigenomics, transcriptomics, proteomics, and post-translational modifications data. They together make up a rich resource for researchers and clinicians interested in understanding cancer biology to draw from. Nevertheless, the complexity of these multi-omic datasets and a lack of an easily accessible analytical and visualization tool for exploring them continue to be a hurdle for those who are not trained in bioinformatics. In this issue, Calinawan et al. describe a user-friendly, web-based visualization platform named ProTrack for exploring the CPTAC clear cell renal cell carcinoma (ccRCC) dataset. Compared to other available visualization tools, ProTrack offers an easy yet powerful customization interface, solely dedicated to the CPTAC ccRCC dataset. Their tool enables ready inspection of potential associations between different data types within a single gene or across multiple genes without any need to code. Specific mutation types or phosphosites can also be easily looked up for any gene of interest. Calinawan et al. aim to extend their work into other CPTAC datasets, which will greatly contribute to the CPTAC as well as cancer biology community in general. © 2020 Wiley-VCH GmbH1

Statistical Modeling for Enhancing the Discovery Power of Citrullination from Tandem Mass Spectrometry Data

Citrullination is a post-translational modification implicated in various human diseases including rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, and cancers. Due to a relatively low concentration of citrullinated proteins in the total proteome, confident identification of citrullinated proteome is challenging in mass spectrometry (MS)-based proteomic analysis. From these MS-based analyses, MS features that characterize citrullination, such as immonium ions (IMs) and neutral losses (NLs), called diagnostic ions, have been reported. However, there has been a lack of systematic approaches to comprehensively search for diagnostic ions and no statistical methods for the identification of citrullinated proteome based on these diagnostic ions. Here, we present a systematic approach to identify diagnostic IMs, internal ions (INTs), and NLs for citrullination from tandem mass (MS/MS) spectra. Diagnostic INTs mainly consisted of internal fragment ions for di- and tripeptides that contained two and three amino acids with at least one citrullinated arginine, respectively. A statistical logistic regression model was built for a confident assessment of citrullinated peptides that database searches identified (true positives) and prediction of citrullinated peptides that database searches failed to identify (false negatives) using the diagnostic IMs, INTs, and NLs. Applications of our model to complex global proteome data sets demonstrated the increased accuracy in the identification of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes. Copyright © 2020 American Chemical Society.1

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.N

2-Undecanone derived from Pseudomonas aeruginosa modulates the neutrophil activity

Pseudomonas aeruginosa (P. aeruginosa) is a well-known Gramnegative opportunistic pathogen. Neutrophils play key roles in mediating host defense against P. aeruginosa infection. In this study, we identified a metabolite derived from P. aeruginosa that regulates neutrophil activities. Using gas chromatography-mass spectrometry, a markedly increased level of 2-undecanone was identified in the peritoneal fluid of P. aeruginosa-infected mice. 2-Undecanone elicited the activation of neutrophils in a Gαi-phospholipase C pathway. However, 2-undecanone strongly inhibited responses to lipopolysaccharide and bactericidal activity of neutrophils against P. aeruginosa by inducing apoptosis. Our results demonstrate that 2-undecanone from P. aeruginosa limits the innate defense activity of neutrophils, suggesting that the production of inhibitory metabolites is a strategy of P. aeruginosa for escaping the host immune system. [BMB Reports 2022; 55(8): 395-400] © 2022 by the The Korean Society for Biochemistry and Molecular Biology1

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor beta signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).N