MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca

MetaboAnalyst: a web server for metabolomic data analysis and interpretation

Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.c

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCore(TM), MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration

Metabolic characterization of directly reprogrammed renal tubular epithelial cells (iRECs)

Fibroblasts can be directly reprogrammed to induced renal tubular epithelial cells (iRECs) using four transcription factors. These engineered cells may be used for disease modeling, cell replacement therapy or drug and toxicity testing. Direct reprogramming induces drastic changes in the transcriptional landscape, protein expression, morphological and functional properties of cells. However, how the metabolome is changed by reprogramming and to what degree it resembles the target cell type remains unknown. Using untargeted gas chromatography-mass spectrometry (GC-MS) and targeted liquid chromatography-MS, we characterized the metabolome of mouse embryonic fibroblasts (MEFs), iRECs, mIMCD-3 cells, and whole kidneys. Metabolic fingerprinting can distinguish each cell type reliably, revealing iRECs are most similar to mIMCD-3 cells and clearly separate from MEFs used for reprogramming. Treatment with the cytotoxic drug cisplatin induced typical changes in the metabolic profile of iRECs commonly occurring in acute renal injury. Interestingly, metabolites in the medium of iRECs, but not of mIMCD-3 cells or fibroblast could distinguish treated and non-treated cells by cluster analysis. In conclusion, direct reprogramming of fibroblasts into renal tubular epithelial cells strongly influences the metabolome of engineered cells, suggesting that metabolic profiling may aid in establishing iRECs as in vitro models for nephrotoxicity testing in the future

PiMP my metabolome:An integrated, web-based tool for LC-MS metabolomics data

Summary: The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by ‘evidence cards’, which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information. Availability: PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50 GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL

꿀벌과 제브라피쉬에서 대사체학과 단백질체학을 이용한 살충제 abamectin의 독성학적 비교

학위논문 (박사) -- 서울대학교 대학원 : 농업생명과학대학 농생명공학부, 2021. 2. 김정한.Abamectin is an abamectin insecticide known to be isolated from fermentation of Streptomyces avermitilis, a naturally occurring soil Actinomycete. Due to its high toxicity in bees and fish, this study investigated the toxic mechanisms of abamectin in honeybee (Apis mellifera) and zebrafish (Danio rerio) using targeted metabolomics approach by gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-high resolution mass spectrometry (LC-orbitrap-HRMS). All homogenized samples were extracted with 80% methanol (honeybee) or 50% methanol (zebrafish) and derivatized with TMS for GC-MS/MS analysis. In the targeted metabolomics approach, multiple reaction monitoring (MRM) mode of a GC-MS/MS for 396 metabolites was used to detect 239 metabolites in honeybee and 243 metabolites in zebrafish. With the results of metabolites detected in each sample, statistical analysis such as partial least squares‐discriminant analysis (PLS-DA), variable importance in the projection (VIP) and analysis of variance (ANOVA) were performed to identify important biomarkers. Metabolic pathways associated with those biomarkers were constructed using MetaboAnalyst 5.0. In the exposure experiment of honeybee to abamectin as a targeted metabolomics using GC-MS/MS and non-targeted metaboloimcs using LC-orbitrap-HRMS, metabolic pathways such as tyrosine metabolism, phenylalanine/tyrosine/tryptophan biosynthesis, citrate cycle, ascorbate/aldarate metabolism, and alanine/aspartate/glutamate metabolism were identified as the significantly perturbed pathways. While, zebrafish showed several metabolic pathways such as aminoacyl tRNA biosynthesis, glyoxylate/dicarboxylate metabolism, citrate cycle, and tryptophan metabolism were identified by exposure of abamectin. Such significant disturbance of important metabolites within key biochemical pathways by abamectin could result in biologically hazardous effects in honeybee and zebrafish. In toxicoproteomics study, the toxicological effects of abamectin in honeybee and adult zebrafisha were investigated using a label-free quantitative proteomic approach on LC-HRMS. The proteins of honeybee and zebrafish samples were extracted with 0.1M phosphate buffer (pH 7.4) and 200 μg of proteins were digested with trypsin using the in-solution filter-aided sample preparation (FASP) method. After LC-HRMS analysis, a total of 670 proteins were identified and 32 proteins were selected as biomarkers through volcano analysis in honeybee. In zebrafish, 2189 proteins were identified and 1050 proteins were selected as biomarkers by statistically analysis.Abamectin은 자연적으로 발생하는 토양 방선균인 Streptomyces avermitilis의 발효에서 분리 된 것으로 알려진 살충제다. 꿀벌과 어류의 독성이 높기 때문에 본 연구는 가스크로마토그래피-탠덤 질량 분석법 (GC-MS/MS) 및 액체크로마토그래피-고분해능 질량 분석법(LC-orbitrap-HRMS)에 의한 비표적 및 표적 대사 체학 접근법을 사용하여 꿀벌(Apis mellifera) 및 제브라피시 (Danio rerio)에서 abamectin의 독성 메커니즘을 조사했다. 모든 균질화 된 샘플을 80 % 메탄올(꿀벌) 또는 50 % 메탄올(제브라피쉬)로 추출하고 GC-MS/MS 분석을 위해 추출액을 TMS로 유도체화반응을 적용하였다. 표적 대사체학 접근법에서는 396 개의 대사산물에 대한 GC-MS/MS의 다중 반응 모니터링(MRM) 모드를 사용하여 꿀벌에서 239개의 대사산물과 제브라피시에서 243개의 대사산물을 검출했다. 각 샘플에서 검출 된 대사 산물의 결과를 바탕으로 PLS-DA 패턴분석, VIP score 및 ANOVA 분산 분석과 같은 통계 분석을 수행하여 중요한 바이오마커를 식별했다. 이러한 바이오 마커와 관련된 대사경로는 MetaboAnalyst 5.0을 사용하여 확인 할 수 있었다. GC-MS/MS를 사용하는 표적 대사체와 LC-orbitrap-HRMS를 사용하는 비표적 대사체학을 통하여 꿀벌의 노출 실험에서 tyrosine metabolism, phenylalanine/tyrosine/tryptophan biosynthesis, citrate cycle, ascorbate/aldarate metabolism, and alanine/aspartate/glutamate metabolism가 상당히 교란 된 것으로 확인되었다. 반면, 제브라피쉬는 aminoacyl tRNA biosynthesis, glyoxylate/dicarboxylate metabolism, citrate cycle, and tryptophan metabolism이 상당히 변화함을 확인하였다. Abamectin에 의한 중요한 대사산물의 심각한 교란은 꿀벌과 제브라피쉬에 생물학적으로 위험한 영향을 미칠 수 있음을 확인 할 수 있었다. 한편, 독성 단백질체학 연구에서는 꿀벌과 제브라피쉬에서 LC-HRMS를 통하여 비표지 정량을 활용한 단백질체학적 접근방식을 사용하였다. 꿀벌과 제브라피시 샘플의 단백질은 0.1M phosphate buffer (pH 7.4)로 추출하고 200μg의 단백질은 in-solution filter-aided sample preparation (FASP) 방법을 사용하여 트립신으로 분해하였다. LC-HRMS 분석 후 총 670개의 단백질이 확인되었고, 화산 분석을 통해 32개의 단백질이 바이오 마커로 선정되었다. 제브라 피쉬에서는 2189개의 단백질이 확인되었고 통계 분석을 통해 1050개의 단백질이 바이오 마커로 선택되었다.Abstract.............................................................................................................i Table of Contents...................................................................iii List of Tables...........................................................................vi List of Figures.................................................................................................viii Preface.......................................................................................................................1 Chapter I. Toxicometabolomics of abamectin in honeybee (Apis mellifera) and zebrafish (Danio rerio).............................................................................................3 Introduction.........................................................................................................5 Methodology of metabolomics............................................................................8 Application of metabolomics.............................................................................21 Purose of study………………………………………………………………….23 Materials and Methods......................................................................................24 Chemicals and reagents........................................................................................24 Experimental animals…………………………………………………………24 Chemical exposure...............................................................................................24 Sample preparation..............................................................................................25 Targeted profiling and identification of metabolites by GC-MS/MS...................26 Non-targeted profiling and identification of metabolites by LC-Orbitrap-HRMS…………………………………………………………..………………26 Statistical analysis and biomarker selection.........................................................30 Metabolic pathway analysis.................................................................................34 Measurement of MDA contents………………………………………………...34 Results and Discussion.......................................................................................36 Chemical exposure and sample preparation…….................................................36 Targeted profiling and identification of metabolites by GC-MS/MS...................36 Non-targeted profiling and identification of metabolites by LC-Orbitrap-HRMS…………………………………………………………………………..52 Statistical analysis and biomarker selection…………………………………….58 Metabolic pathway analysis…………….............................................................74 Abamectin effects on MDA in zebrafish………………………………………..84 Conclusions........................................................................................................88 Chapter II. Toxicoproteomics of abamectin in honeybee (Apis mellifera) and zebrafish (Danio rerio)...........................................................................................91 Introduction.......................................................................................................93 Proteomics...........................................................................................................93 Methodology of proteomics.................................................................................94 Application of proteomics..................................................................................105 Purpose of study.................................................................................................180 Materials and Methods....................................................................................109 Chemicals and reagents......................................................................................109 Experimental animals and chemical exposure……………………………….109 Sample preparation............................................................................................109 Profiling and identification of proteomes by LC-Orbitrap-HRMS.....................111 Statistical analysis……………………….......................................................114 Enrichment analysis...........................................................................................114 Results and Discussion.....................................................................................115 Protein profiling using LC-Orbitrap-HRMS......................................................115 Proteomic alteration induced by abamectin exposure........................................120 Enrichment analysis...........................................................................................186 Conclusions......................................................................................................199 References.............................................................................................................200 초록...............................................................................................................212Docto