Using mass spectrometry-based proteomics to improve the understanding of multiple sclerosis treatments

In this work, the effects of multiple sclerosis (MScl) treatments were investigated by quantitative proteomics. First, the effects of the anti-inflammatory drug Fingolimod was studied to see if the drug affected central nervous system (CNS) repair in a non-inflammatory MScl mouse model. Next, the cerebrospinal fluid (CSF) proteome was investigated to learn more about the treatment response and mechanism of action in the CNS of MScl patients treated with the anti-inflammatory drug Natalizumab. Proteomic analysis identified over 6000 proteins in the frontal right hemisphere of the mouse model. Abundance changes of these proteins were measured during de- and remyalination and confirmed the global proteome effects of the disease model known from previous studies. The analysis showed no benefit of Fingolimod on myelination, which was also supported by histological analyses of brain sections in the corpus callosum. However, the proteomic approach did detect a novel reduction in one of the drug receptors known to be expressed in several cells in the brain. CSF samples from MScl patients in Czech cohort with a relapsing-remitting (RRMS) disease course was sampled at the beginning of, and after approximately two years of treatment. Proteomic changes during treatment were then related to disease processes in RRMS by comparison to online datasets in CSF-PR. The findings confirmed the known anti-inflammatory effect of Natalizumab, but also revealed previously unknown effects of the treatment on neurological proteins and metabolism. Finally, targeted proteomics assays were created based on biomarker candidates from existing literature, with the long-term goal of defining a biomarker panel for clinical use. Proteins linked to known disease processes were selected based on, e.g., peptide uniqueness, inter- and intra-day stability and optimal digestion time, in order to design robust assays that can be compared over time.Doktorgradsavhandlin

Enhanced label-free discovery proteomics through improved data analysis and knowledge enrichment

Mass spectrometry (MS)-based proteomics has evolved into an important tool applied in fundamental biological research as well as biomedicine and medical research. The rapid developments of technology have required the establishment of data processing algorithms, protocols and workflows. The successful application of such software tools allows for the maturation of instrumental raw data into biological and medical knowledge. However, as the choice of algorithms is vast, the selection of suitable processing tools for various data types and research questions is not trivial. In this thesis, MS data processing related to the label-free technology is systematically considered. Essential questions, such as normalization, choice of preprocessing software, missing values and imputation, are reviewed in-depth. Considerations related to preprocessing of the raw data are complemented with exploration of methods for analyzing the processed data into practical knowledge. In particular, longitudinal differential expression is reviewed in detail, and a novel approach well-suited for noisy longitudinal high-througput data with missing values is suggested. Knowledge enrichment through integrated functional enrichment and network analysis is introduced for intuitive and information-rich delivery of the results. Effective visualization of such integrated networks enables the fast screening of results for the most promising candidates (e.g. clusters of co-expressing proteins with disease-related functions) for further validation and research. Finally, conclusions related to the prepreprocessing of the raw data are combined with considerations regarding longitudinal differential expression and integrated knowledge enrichment into guidelines for a potential label-free discovery proteomics workflow. Such proposed data processing workflow with practical suggestions for each distinct step, can act as a basis for transforming the label-free raw MS data into applicable knowledge.Massaspektrometriaan (MS) pohjautuva proteomiikka on kehittynyt tehokkaaksi työkaluksi, jota hyödynnetään niin biologisessa kuin lääketieteellisessäkin tutkimuksessa. Alan nopea kehitys on synnyttänyt erikoistuneita algoritmeja, protokollia ja ohjelmistoja datan käsittelyä varten. Näiden ohjelmistotyökalujen oikeaoppinen käyttö lopulta mahdollistaa datan tehokkaan esikäsittelyn, analysoinnin ja jatkojalostuksen biologiseksi tai lääketieteelliseksi ymmärrykseksi. Mahdollisten vaihtoehtojen suuresta määrästä johtuen sopivan ohjelmistotyökalun valinta ei usein kuitenkaan ole yksiselitteistä ja ongelmatonta. Tässä väitöskirjassa tarkastellaan leimaamattomaan proteomiikkaan liittyviä laskennallisia työkaluja. Väitöskirjassa käydään läpi keskeisiä kysymyksiä datan normalisoinnista sopivan esikäsittelyohjelmiston valintaan ja puuttuvien arvojen käsittelyyn. Datan esikäsittelyn lisäksi tarkastellaan datan tilastollista jatkoanalysointia sekä erityisesti erilaisen ekspression havaitsemista pitkittäistutkimuksissa. Väitöskirjassa esitellään uusi, kohinaiselle ja puuttuvia arvoja sisältävälle suurikapasiteetti-pitkittäismittausdatalle soveltuva menetelmä erilaisen ekspression havaitsemiseksi. Uuden tilastollisen menetelmän lisäksi väitöskirjassa tarkastellaan havaittujen tilastollisten löydösten rikastusta käytännön ymmärrykseksi integroitujen rikastumis- ja verkkoanalyysien kautta. Tällaisten funktionaalisten verkkojen tehokas visualisointi mahdollistaa keskeisten tulosten nopean tulkinnan ja kiinnostavimpien löydösten valinnan jatkotutkimuksia varten. Lopuksi datan esikäsittelyyn ja pitkittäistutkimusten tilastollisen jatkokäsittelyyn liittyvät johtopäätökset yhdistetään tiedollisen rikastamisen kanssa. Näihin pohdintoihin perustuen esitellään mahdollinen työnkulku leimaamattoman MS proteomiikkadatan käsittelylle raakadatasta hyödynnettäviksi löydöksiksi sekä edelleen käytännön biologiseksi ja lääketieteelliseksi ymmärrykseksi

Detection of co-eluted peptides using database search methods

<p>Abstract</p> <p>Background</p> <p>Current experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS²) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost.</p> <p>Results</p> <p>We have computationally investigated the possibility of enhancing the information retrieval during a given LC/MS experiment by selecting the two or three most intense parent ions for simultaneous fragmentation. A set of spectra is created via superimposing a number of MS²spectra, each can be identified by all search methods tested with high confidence, to mimick the spectra of co-eluted peptides. The generated convoluted spectra were used to evaluate the capability of several database search methods – SEQUEST, Mascot, X!Tandem, OMSSA, and RAId_DbS – in identifying true peptides from superimposed spectra of co-eluted peptides. We show that using these simulated spectra, all the database search methods will gain eventually in the number of true peptides identified by using the compound spectra of co-eluted peptides.</p> <p>Open peer review</p> <p>Reviewed by Vlad Petyuk (nominated by Arcady Mushegian), King Jordan and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section.</p

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator)

A MASS SPECTROMETRY-BASED STUDY OF SERUM BUTYRYLCHOLINESTERASE INHIBITION FROM PESTICIDE EXPOSURE AND ORGANOPHOSPHATE PESTICIDE-INDUCED PROTEOME ALTERATION

Pesticides including organophosphates (OPs) and carbamates (CBs) are widelyused to control undesirable pests. These compounds are neurotoxic and inhibithydrolysis of the neurotransmitter acetylcholine by acetylcholinesterase. Public healthconcerns have increased with the escalating usage of pesticides. Reliable monitoringprograms are required to detect and quantify pesticide exposure, as well as to promotean understanding of their neurotoxic properties. In this dissertation, both theanticholinergic (Part I) toxicity and neurotoxicity in neuroblastoma cells (Part II) ofpesticides were explored using mass spectrometry (MS). The high sensitivity andhigh-throughput of this technique renders it well-suited for proteomics analysis.Part I describes the study of butyrylcholinesterase (BChE) inhibition resultingfrom OP and CB exposure. The main hypothesis of Part I is that the specialmodification of BChE can provide the origin and extent of pesticide exposure. A novelmethod for detection and quantification of pesticide exposure was designed using aproteomics approach and equine BChE (eBChE) as a model system. The methodologyfeatured detection and analysis of phosphorylated or carbamylated peptides at theactive site serine residue. The developed technique was successfully applied towardsthe study of human BChE (hBChE) inhibition in vitro and in serum samples. Aspecially designed affinity column enabled an isolation of BChE from serum. EnrichedBChE was subjected to enzymatic digestion by a novel on-bead double digestionprotocol. LC/MS/MS was employed to produce a calibration system for the analysis ofhBChE inhibition, which was then applied towards quantification of the enzyme.Part II describes a proteomic study of the neurotoxicity in neuroblastoma cellscaused from chlorpyrifos (CPF), an organophosphate pesticide. The concerns of CPFexposure to pregnant women, infants and children are increasing due todevelopmentally neurotoxic effects of this chemical. The main hypothesis of Part II isthat CPF can cause protein alterations and these altered proteins can be detected usingproteomics. Systematic studies at subcellular levels evaluated proteome changes inSH-SY5Y cells exposed to CPF. Two-dimensional gel electrophoresis (2DE) wasapplied with MALDI-TOF-MS to analyze differential protein expression. Thirty sevencommon unique altered proteins were identified, which play important roles inmetabolic pathway

Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

프로테오지노믹스 기법을 이용한 폐암 바이오마커 연구

학위논문 (박사)-- 서울대학교 대학원 : 수의과대학 수의학과, 2019. 2. 조제열.정밀의료 패러다임의 등장 이후, 질환의 진단 및 치료를 위해서 바이오마커에 대한 수요는 높아지고있다. 가설기반연구는 전통적으로 당연하게 사용되오던 연구수행체계이지만, 바이오마커 발굴에서 필연적으로 마주치게되는 광범위한 스크리닝 작업에서는 효율성의 한계를 드러낸다. 오믹스기술의 등장과 함께 질환연구의 패러다임은 증거기반 대규모 타겟발굴방식으로 변화하고 있다. 단백질은 생체 기능조절에 직접적으로 관여하는 물질이기 때문에 바이오마커로 활용할 수 있는 가장 이상적인 물질로 여겨진다. 질량분석기를 이용한 단백체분석은 단백질을 직접 정성 및 정량할 수 있을 뿐만 아니라 매우 생산성이 높아 질환 바이오마커 발굴에 유용하다. 이 논문에서는 질량분석기를 이용하여 폐암 바이오마커의 발굴을 위한 고도화된 분석기법인 프로테오지노믹스 기법의 적용과, 스크리닝된 바이오마커후보 단백질의 정량검증 및 폐암 감별진단 조합마커의 생성연구에 대하여 알아본다. CHAPTER I에서는 인간염색체기반 단백체프로젝트 (C-HPP)의 일환으로 수행된 염색체9번에 대한 단백체연구가 포함되어있다. 미확인 단백질과 유전단백체에서 발견되지 않았던 시그니처를 밝혀내기 위해 LC-MS/MS분석과 RNA-seq 차세대염기분석기법을 적용하여 샘암종 폐암환자 5명의 정상-종양조직을 분석하였다. 염색체중심-인간단백체프로젝트의 2013년 리포트에서는 neXtProt 인간단백체 데이터베이스를 기준으로 염색체 9번에서 170개의 미확인 단백질이 있는 것으로 알려졌으며, 본 논문의 연구가 진행된 2015년에는 133개가 계속 미확인상태로 남아있었다. 본 논문의 단백체분석에서는 19개의 미확인 단백질을 동정할 수 있었으며, 그 중에서 염색체9번에 해당하는 단백질은 SPATA31A4 한 개 였다. RNA-seq분석으로는 샘종폐암조직 5개에서 공통적으로 검출되면서 정상조직에서는 검출되지 않는nonsynonymous SNP 5개 (CDH17, HIST1H1T, SAPCD2, ZNF695) 그리고 synonymous SNP 3개를 발굴할 수 있었다. 프로테오지노믹 분석을 위해서 각 시료별 RNA-seq데이터를 가공하여 맞춤형 데이터베이스를 구축하였다. 이렇게 생성된 시료맞춤형 데이터베이스를 단백체 질량분석데이터 검색에 활용하여 5개 유전자(LTF, HDLBP, TF, HBD, HLA-DRB5)에 해당하는 7개의 돌연변이를 검출하였다. 두 개의 돌연변이는 정상조직에서는 검출되지 않고 암조직에서만 검출되었다. 또한, 이 결과에서는 정상-암조직 모두에서 위유전자 (EEF1A1P5) 펩티드를 검출할 수 있었다. CHAPTER II에서는 다중반응검지법 (MRM) 을 이용한 단백질 바이오마커 검증과 조합마커 구성에 대한 연구를 서술하였다. 폐암과 다른 폐질환은 감별이 어렵기 때문에 폐암은 오진단 위험이 큰 질병이다. 따라서 혈청기반의 폐암감별진단 바이오마커개발의 필요성은 널리 인정되고있다. 이 단원에서는 폐암환자와 대조군폐질환 환자 198명의 혈청시료를 활용하여 일곱개의 폐암바이오마커 후보단백질을 나노유속 액체크로마토그래피-다중반응검지법으로 정량하였다. 후보단백질을 개별로 분석하였을 때에는 SERPINA4만이 통계적으로 유의성있게 혈중농도가 감소하는 것으로 나타났다. 다중반응검지법 전체데이터를 임상정보와 함께 로지스틱회귀모델에 적용하여 하나의 조합마커로 만들 수 있었다. 이 과정에서 개별마커로는 통계적인 유의성이 두드러지지 않지만 간섭효과를 만들어낼 수 있는 변수를 고려하여 모델링을 진행하였다. 최종적으로 SERPINA4, PON1, 나이를 조합하였을 때 가장 최적의 조합마커가 생성되었다. 이 조합마커는 AUC 0.915 의 감별진단 성능을 보여주었으며, 모델을 만드는데 사용되었던 시료와는 별개의 검증군에서도 성능은 유지되었다. 이와 같이 통계모델을 이용하여 생성한 조합마커는 개별 분자마커를 이용했을 경우보다 개선된 폐암 감별진단능력을 보여줄 수 있음을 제시한다.Biomarkers have been in high demand for disease diagnosis and therapeutics. Traditional hypothesis-based research has been challenging due to massive screening studies. Together with the emergence of omics technologies, currently, the paradigm for disease research has been moving toward evidence-based large-scale discovery studies. Proteins, as key effector molecules, can serve as ideal biomarkers for various diseases because they catalyze every biological function. Proteomics, which is represented by mass spectrometry (MS) technologies, stands as a solution for disease diagnosis and drug target discovery. CHAPTER I includes a portion of a report from of the human proteome project (HPP) related to chromosome 9 (Chr 9). To identify missing proteins (MPs) and their potential features in regard to proteogenomic view, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based tools were used for the clinical samples including adjacent non-tumor tissues. When the Chr 9 working group of the Chromosome-Centric Human Proteome Project (C-HPP) began this project, there were 170 remaining MPs encoded by Chr 9 (neXtProt 2013.09.26 rel.)currently, 133 MPs remain unidentified at present (neXtProt 2015.04.28 rel.). Proteome analysis in this study identified 19 missing proteins across all chromosomes and one MP (SPATA31A4) from Chr 9. RNA-seq analysis enable detection of RNA expression of 4 nonsynonymous (NS) SNPs (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and 3 synonymous SNPs (in CDH17, CST1, and HNF1A) in all 5 tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data, and by searching the proteomics data from the same sample, 7 missense mutations in 5 genes (LTF, HDLBP, TF, HBD, and HLA-DRB5) were identified. Two of these mutations were found in tumor tissues but not in the paired normal tissues. Additionally, this study discovered peptides that were derived from the expression of a pseudogene (EEF1A1P5) in both tumor and normal tissues. In summary, this proteogenomic study of human primary lung tumor tissues enabled detection of additional missing proteins and revealed novel missense mutations and synonymous SNP signatures, some of which are predicted to be specific to lung cancer. CHAPTER II describes a study of the combination marker model using multiple reaction monitoring (MRM) quantitative data. Misdiagnosis of lung cancer remains a serious problem due to the difficulty of distinguishing lung cancer from other respiratory lung diseases. As a result, the development of serum-based differential diagnostic biomarkers is in high demand. In this study, 198 serum samples from non-cancer lung disease and lung cancer patients were analyzed using nLC-QqQ-MS to examine the diagnostic efficacy of seven lung cancer biomarker candidates. When the candidates were assessed individually, only SERPINEA4 showed statistically significant changes in the sera of cancer patient compared to those of control samples. The MRM results and clinical information were analyzed using logistic regression analysis to a select model for the best meta-marker, or combination of biomarkers for the differential diagnosis. Additionally, under consideration of statistical interaction, variables having a low significance as a single factor but statistically influencing the meta-marker model were selected. Using this probabilistic classification, the best meta-marker was determined to comprise two proteins SERPINA4 and PON1, with an age factor. This meta-marker showed an enhanced differential diagnostic capability (AUC=0.915) to distinguish the lung cancer from lung disease patient groups. These results suggest that a statistical model can determine optimal meta-markers, which may have better specificity and sensitivity than a single biomarker and may thus improve the differential diagnosis of lung cancer and lung disease patients.ABSTRACT_i CONTENTS_v LIST OF FIGURES_vii LIST OF TABLES_x ABBREVIATIONS_xii BACKGROUND_1 1. LUNG CANCER_1 2. BIOMARKER_7 3. MASS SPECTROMETRY BASED PROTEOMICS_12 4. PROTEOGENOMICS_24 5. TARGETED PROTEOMICS_33 CHAPTER I Proteogenomic Study: Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues_41 1. INTRODUCTION_42 2. MATERIALS AND METHODS_45 3. RESULTS AND DISCUSSION_53 4. CONCLUSION_81 CHAPTER II Multi-Panel Biomarker Development for the Efficient Discrimination of Lung Cancer for Other Lung Diseases_84 1. INTRODUCTION_85 2. MATERIALS AND METHODS_88 3. RESULTS_93 4. DISCUSSION_120 5. CONCLUSION_127 GENERAL CONCLUSION_129 REFERENCES_131 ABSTRACT IN KOREAN_154Docto

Group A Streptococcal S Protein Utilizes Red Blood Cells as Immune Camouflage and Is a Critical Determinant for Immune Evasion.

Group A Streptococcus (GAS) is a human-specific pathogen that evades the host immune response through the elaboration of multiple virulence factors. Although many of these factors have been studied, numerous proteins encoded by the GAS genome are of unknown function. Herein, we characterize a biomimetic red blood cell (RBC)-captured protein of unknown function-annotated subsequently as S protein-in GAS pathophysiology. S protein maintains the hydrophobic properties of GAS, and its absence reduces survival in human blood. S protein facilitates GAS coating with lysed RBCs to promote molecular mimicry, which increases virulence in&nbsp;vitro and in&nbsp;vivo. Proteomic profiling reveals that the removal of S protein from GAS alters cellular and extracellular protein landscapes and is accompanied by a decrease in the abundance of several key GAS virulence determinants. In&nbsp;vivo, the absence of S protein results in a striking attenuation of virulence and promotes a robust immune response and immunological memory