Characterization of the membrane proteome and N-glycoproteome in BV-2 mouse microglia by liquid chromatography-tandem mass spectrometry

Background : Microglial cells are resident macrophages of the central nervous system and important cellular mediators of the immune response and neuroinflammatory processes. In particular, microglial activation and communication between microglia, astrocytes, and neurons are hallmarks of the pathogenesis of several neurodegenerative diseases. Membrane proteins and their N-linked glycosylation mediate this microglial activation and regulate many biological process including signal transduction, cell-cell communication, and the immune response. Although membrane proteins and N-glycosylation represent a valuable source of drug target and biomarker discovery, the knowledge of their expressed proteome in microglia is very limited. Results : To generate a large-scale repository, we constructed a membrane proteome and N-glycoproteome from BV-2 mouse microglia using a novel integrated approach, comprising of crude membrane fractionation, multienzyme-digestion FASP, N-glyco-FASP, and various mass spectrometry. We identified 6928 proteins including 2850 membrane proteins and 1450 distinct N-glycosylation sites on 760 N-glycoproteins, of which 556 were considered novel N-glycosylation sites. Especially, a total of 114 CD antigens are identified via MS-based analysis in normal conditions of microglia for the first time. Our bioinformatics analysis provides a rich proteomic resource for examining microglial function in, for example, cell-to-cell communication and immune responses. Conclusions : Herein, we introduce a novel integrated proteomic approach for improved identification of membrane protein and N-glycosylation sites. To our knowledge, this workflow helped us to obtain the first and the largest membrane proteomic and N-glycoproteomic datesets for mouse microglia. Collectively, our proteomics and bioinformatics analysis significantly expands the knowledge of the membrane proteome and N-glycoproteome expressed in microglia within the brain and constitutes a foundation for ongoing proteomic studies and drug development for various neurological diseases.This work was supported by the Proteogenomic Research Program through the National Research Foundation of Korea and a National Research Foundation of Korea [NRF] grant (No. 2011–0030740), funded by the Korea government [MSIP]. This work was also supported by the Industrial Strategic Technology Development Program (#10045352), funded by the Ministry of Knowledge Economy (MKE, Korea).Peer Reviewe

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks.

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC-MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit

N-말단단백체 및 다중반응검지 질량분석기술을 이용한 암 표지자 개발 연구

학위논문 (박사)-- 서울대학교 대학원 : 의과학과 의과학전공, 2015. 8. 김영수.Introduction: Cancer is the leading cause of death in the worldwide, and the major cause of cancer death is the difficulty for early diagnosis. To overcome this problem, the discovery of cancer biomarkers is useful for early diagnosis, outcome monitoring, or predicting recurrence. For biomarker discovery, proteomics technique is powerful tools with high-throughput and high sensitivity. Thus, proteomics study can help variable cancer biomarker discovery and understand of cancer mechanisms in body. Methods: In chapter I, to examine metastatic events in lung cancer, we performed a proteomics study by label-free quantitative analysis and N-terminal analysis in 2 human non-small-cell lung cancer cell lines with disparate metastatic potentials?NCI-H1703 (primary cell, stage I) and NCI-H1755 (metastatic cell, stage IV). In chapter II, we performed to identify new marker-candidate proteins from LiverAtlas database. And abundance of marker-candidate proteins were quantified in individual patients by multiple reaction monitoring assay. Results: In chapter I, we identified 2130 proteins, 1355 of which were common to both cell lines. In the label-free quantitative analysis, we used the NSAF normalization method, resulting in 242 differential expressed proteins. For the N-terminal proteome analysis, 325 N-terminal peptides, including 45 novel fragments, were identified in the 2 cell lines. Based on two proteomic analysis, 11 quantitatively expressed proteins and 8 N-terminal peptides were enriched for the focal adhesion pathway. Most proteins from the quantitative analysis were upregulated in metastatic cancer cells, whereas novel fragment of CRKL was detected only in primary cancer cells. In chapter II, we selected quantitative 104 marker candidate proteins with reference labeled peptides. Among them, we found that 17 proteins with AUC more than 0.60 were able to effectively discriminate poor responders from total patients underwent TACE. Also, we discovered powerful ensemble model panel with protein markers and clinical variables. Conclusions: In chapter I, our datasets of proteins and fragment peptides in lung cells might be valuable in discovering and validating lung cancer biomarkers and metastasis markers. This study increases our understanding of the NSCLC metastasis proteome. In chapter II, we discovered three new marker proteins that are associated with prognosis prediction after TACE in the first time. Our study can help to identify useful biomarkers for prediction of prognosis with multi-panel modeling.Abstract i Contents iii List of Tables vi List of Figures vii List of Abbreviations x General Introduction 1 Chapter I 3 Label-Free Quantitative Proteomics and N-terminal Analysis of Human Metastatic Lung Cancer Cells Introduction 4 Material and Methods 7 Results 16 Discussion 41 Chapter II 48 Targeted proteomics predicts complete response transarterial chemoembolization in hepatocellular carcinoma Introduction 49 Material and Methods 52 Results 62 Discussion 92 References 96 Abstract in Korean 109Docto

Online monitoring of immunoaffinity-based depletion of high-abundance blood proteins by UV spectrophotometry using enhanced green fluorescence protein and FITC-labeled human serum albumin

<p>Abstract</p> <p>Background</p> <p>The removal of high-abundance proteins from plasma is an efficient approach to investigating flow-through proteins for biomarker discovery studies. Most depletion methods are based on multiple immunoaffinity methods available commercially including LC columns and spin columns. Despite its usefulness, high-abundance depletion has an intrinsic problem, the sponge effect, which should be assessed during depletion experiments. Concurrently, the yield of depletion of high-abundance proteins must be monitored during the use of the depletion column. To date, there is no reasonable technique for measuring the recovery of flow-through proteins after depletion and assessing the capacity for capture of high-abundance proteins.</p> <p>Results</p> <p>In this study, we developed a method of measuring recovery yields of a multiple affinity removal system column easily and rapidly using enhanced green fluorescence protein as an indicator of flow-through proteins. Also, we monitored the capture efficiency through depletion of a high-abundance protein, albumin labeled with fluorescein isothiocyanate.</p> <p>Conclusion</p> <p>This simple method can be applied easily to common high-abundance protein depletion methods, effectively reducing experimental variations in biomarker discovery studies.</p

Untargeted Metabolomics Analysis Reveals Toxicity Based on the Sex and Sexual Maturity of Single Low-Dose DEHP Exposure

Di-(2-Ethylhexyl) phthalate (DEHP) is a prevalent environmental endocrine disruptor that affects homeostasis, reproduction, and developmental processes. The effects of DEHP have been shown to differ based on sex and sexual maturity. This study examines the metabolic profiles of mature adult rats from both sexes, aged 10 weeks, and adolescent female rats, aged 6 weeks, following a single 5 mg/kg of body weight DEHP oral administration. An untargeted metabolomic analysis was conducted on urine samples collected at multiple times to discern potential sex- and maturity-specific DEHP toxicities. Various multivariate statistical analyses were employed to identify the relevant metabolites. The findings revealed disruptions to the steroid hormone and primary bile acid biosynthesis. Notably, DEHP exposure increased hyocholic, muricholic, and ketodeoxycholic acids in male rats. Moreover, DEHP exposure was linked to heart, liver, and kidney damage, as indicated by increased plasma GOT1 levels when compared to the levels before DEHP exposure. This study provides detailed insights into the unique mechanisms triggered by DEHP exposure concerning sex and sexual maturity, emphasizing significant distinctions in lipid metabolic profiles across the different groups. This study results deepens our understanding of the health risks linked to DEHP, informing future risk assessments and policy decisions

Application of Skyline software for detecting prohibited substances in doping control analysis.

As the number of prohibited drugs has been progressively increasing and analytical methods for detecting such substances are renewed continuously for doping control, the need for more sensitive and accurate doping analysis has increased. To address the urgent need for high throughput and accurate analysis, liquid chromatography with tandem mass spectrometry is actively utilized in case of most of the newly designated prohibited substances. However, because all mass spectrometer vendors provide data processing software that is incapable of handling other instrumental data, it is difficult to cover all doping analysis procedures, from method development to result reporting, on one platform. Skyline is an open-source and vendor-neutral software program invented for the method development and data processing of targeted proteomics. Recently, the utilization of Skyline has been expanding for the quantitative analysis of small molecules and lipids. Herein, we demonstrated Skyline as a simple platform for unifying overall doping control, including the optimization of analytical methods, monitoring of data quality, discovery of suspected doping samples, and validation of analytical methods for detecting newly prohibited substances. For method optimization, we selected the optimal collision energies for 339 prohibited substances. Notably, 195 substances exhibited a signal intensity increase of >110% compared with the signal intensity of the original collision energy. All data related to method validation and quantitative analysis were efficiently visualized, extracted, or calculated using Skyline. Moreover, a comparison of the time consumed and the number of suspicious samples screened in the initial test procedure highlighted the advantages of using Skyline over the commercially available software TraceFinder in doping control

Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics

Diabetic retinopathy (DR) is a common microvascular complication caused by diabetes mellitus (DM) and is a leading cause of vision impairment and loss among adults. Here, we performed a comprehensive proteomic analysis to discover biomarkers for DR. First, to identify biomarker candidates that are specifically expressed in human vitreous, we performed data-mining on both previously published DR-related studies and our experimental data; 96 proteins were then selected. To confirm and validate the selected biomarker candidates, candidates were selected, confirmed, and validated using plasma from diabetic patients without DR (No DR) and diabetics with mild or moderate nonproliferative diabetic retinopathy (Mi or Mo NPDR) using semiquantitative multiple reaction monitoring (SQ-MRM) and stable-isotope dilution multiple reaction monitoring (SID-MRM). Additionally, we performed a multiplex assay using 15 biomarker candidates identified in the SID-MRM analysis, which resulted in merged AUC values of 0.99 (No DR versus Mo NPDR) and 0.93 (No DR versus Mi and Mo NPDR). Although further validation with a larger sample size is needed, the 4-protein marker panel (APO4, C7, CLU, and ITIH2) could represent a useful multibiomarker model for detecting the early stages of DR