1
|
Kesmen E, Nezih Kök A, Ateş O, Şenol O. Investigating the pathogenesis of vitreous in postmortem COVID patients via untargeted metabolomics based bioinformatics model. Leg Med (Tokyo) 2024; 70:102461. [PMID: 38815416 DOI: 10.1016/j.legalmed.2024.102461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/01/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024]
Abstract
SARS-CoV-2 virus has become a worldwide pandemic causing millions of death. This severe disaster lead to a immense panic and stress all over the world. Several studies were dedicated to understand its mechanism, pathogenesis and spreading characteristics. By this way, scientists try to develop different therapy and diagnose strategies. For these reasons, several metabolomics, proteomics and genomics studies were also carried out to improve knowledge in this newly identified virus. In this study, we are aimed to explain the pathogenesis of SARS-CoV-2 exposure on postmortem COVID (+) patients via untargeted metabolomics analysis. To carry out this study, a Data Independent Acquisition SWATH method is optimized and performed. Vitreous samples were analyzed in both MS1 and MS2 ESI(+) mode. An orthogonal Partial Least Square Discriminant Analysis were performed for classification. It was observed that lipid metabolism, several amino acids and oxidative stress biomarkers were strongly affected due to high inflammation and possible cytokine storm.
Collapse
Affiliation(s)
- Elif Kesmen
- Erzurum Branch Office, The Ministry of Justice Council of Forensic Medicine, Erzurum, Turkey
| | - Ahmet Nezih Kök
- Atatürk University, Faculty of Medicine, Department of Forensic Science, 25240 Erzurum, Turkey
| | - Orhan Ateş
- Atatürk University, Faculty of Medicine, Department of Ophtalmology, 25240 Erzurum, Turkey
| | - Onur Şenol
- Atatürk University, Faculty of Pharmacy, Department of Analytical Chemistry, 25240 Erzurum, Turkey.
| |
Collapse
|
2
|
Reuschenbach M, Drees F, Schmidt TC, Renner G. qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry. Anal Chem 2023; 95:13804-13812. [PMID: 37658322 DOI: 10.1021/acs.analchem.3c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.
Collapse
Affiliation(s)
- Max Reuschenbach
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
| | - Felix Drees
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
| | - Torsten C Schmidt
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
- IWW Water Center, Moritzstr. 26, 45476 Mülheim an der Ruhr, Germany
| | - Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
| |
Collapse
|
3
|
Rodrigues Neto JC, Salgado FF, Braga ÍDO, Carvalho da Silva TL, Belo Silva VN, Leão AP, Ribeiro JADA, Abdelnur PV, Valadares LF, de Sousa CAF, Souza Júnior MT. Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach. FRONTIERS IN PLANT SCIENCE 2023; 14:1187803. [PMID: 37384354 PMCID: PMC10296175 DOI: 10.3389/fpls.2023.1187803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
Introduction Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress. Methods The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented. Results and discussion Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na+, Cl-, and K+, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.
Collapse
Affiliation(s)
| | | | | | | | | | - André Pereira Leão
- The Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, Brazil
| | | | | | | | | | - Manoel Teixeira Souza Júnior
- The Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, Brazil
- Graduate Program of Plant Biotechnology, Federal University of Lavras, Lavras, MG, Brazil
| |
Collapse
|
4
|
Dos Santos EKP, Canuto GAB. Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study. Metabolomics 2023; 19:26. [PMID: 36976375 DOI: 10.1007/s11306-023-01992-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/05/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AND AIMS Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform. METHODS GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples. RESULTS The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh). CONCLUSION This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.
Collapse
|
5
|
Innovative Application of Metabolomics on Bioactive Ingredients of Foods. Foods 2022; 11:foods11192974. [PMID: 36230049 PMCID: PMC9562173 DOI: 10.3390/foods11192974] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/12/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.
Collapse
|
6
|
Olkowicz M, Rosales-Solano H, Ramadan K, Wang A, Cypel M, Pawliszyn J. The metabolic fate of oxaliplatin in the biological milieu investigated during in vivo lung perfusion using a unique miniaturized sampling approach based on solid-phase microextraction coupled with liquid chromatography-mass spectrometry. Front Cell Dev Biol 2022; 10:928152. [PMID: 36092704 PMCID: PMC9453651 DOI: 10.3389/fcell.2022.928152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Adjuvant chemotherapy after pulmonary metastasectomy for colorectal cancer may reduce recurrence and improve survival rates; however, the benefits of this treatment are limited by the significant side effects that accompany it. The development of a novel in vivo lung perfusion (IVLP) platform would permit the localized delivery of high doses of chemotherapeutic drugs to target residual micrometastatic disease. Nonetheless, it is critical to continuously monitor the levels of such drugs during IVLP administration, as lung injury can occur if tissue concentrations are not maintained within the therapeutic window. This paper presents a simple chemical-biopsy approach based on sampling with a small nitinol wire coated with a sorbent of biocompatible morphology and evaluates its applicability for the near-real-time in vivo determination of oxaliplatin (OxPt) in a 72-h porcine IVLP survival model. To this end, the pigs underwent a 3-h left lung IVLP with 3 doses of the tested drug (5, 7.5, and 40 mg/L), which were administered to the perfusion circuit reservoir as a bolus after a full perfusion flow had been established. Along with OxPt levels, the biocompatible solid-phase microextraction (SPME) probes were employed to profile other low-molecular-weight compounds to provide spatial and temporal information about the toxicity of chemotherapy or lung injury. The resultant measurements revealed a rather heterogeneous distribution of OxPt (over the course of IVLP) in the two sampled sections of the lung. In most cases, the OxPt concentration in the lung tissue peaked during the second hour of IVLP, with this trend being more evident in the upper section. In turn, OxPt in supernatant samples represented ∼25% of the entire drug after the first hour of perfusion, which may be attributable to the binding of OxPt to albumin, its sequestration into erythrocytes, or its rapid nonenzymatic biotransformation. Additionally, the Bio-SPME probes also facilitated the extraction of various endogenous molecules for the purpose of screening biochemical pathways affected during IVLP (i.e., lipid and amino acid metabolism, steroidogenesis, or purine metabolism). Overall, the results of this study demonstrate that the minimally invasive SPME-based sampling approach presented in this work can serve as (pre)clinical and precise bedside medical tool.
Collapse
Affiliation(s)
- Mariola Olkowicz
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
| | | | - Khaled Ramadan
- Latner Thoracic Surgery Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Aizhou Wang
- Latner Thoracic Surgery Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Marcelo Cypel
- Latner Thoracic Surgery Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Division of Thoracic Surgery, Department of Surgery, University Health Network, University of Toronto, Toronto Lung Transplant Program, Toronto, ON, Canada
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON, Canada
| |
Collapse
|
7
|
Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
Collapse
Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| |
Collapse
|
8
|
HPLC–(Q)-TOF-MS-Based Study of Plasma Metabolic Profile Differences Associated with Age in Pediatric Population Using an Animal Model. Metabolites 2022; 12:metabo12080739. [PMID: 36005611 PMCID: PMC9413543 DOI: 10.3390/metabo12080739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 11/17/2022] Open
Abstract
A deep knowledge about the biological development of children is essential for appropriate drug administration and dosage in pediatrics. In this sense, the best approximation to study organ maturation is the analysis of tissue samples, but it requires invasive methods. For this reason, surrogate matrices should be explored. Among them, plasma emerges as a potential alternative since it represents a snapshot of global organ metabolism. In this work, plasma metabolic profiles from piglets of different ages (newborns, infants, and children) obtained by HPLC–(Q)-TOF-MS at positive and negative ionization modes were studied. Improved clustering within groups was achieved using multiblock principal component analysis compared to classical principal component analysis. Furthermore, the separation observed among groups was better resolved by using partial least squares-discriminant analysis, which was validated by bootstrapping and permutation testing. Thanks to univariate analysis, 13 metabolites in positive and 21 in negative ionization modes were found to be significant to discriminate the three groups of piglets. From these features, an acylcarnitine and eight glycerophospholipids were annotated and identified as metabolites of interest. The findings indicate that there is a relevant change with age in lipid metabolism in which lysophosphatidylcholines and lysophoshatidylethanolamines play an important role.
Collapse
|
9
|
mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells. Nutrients 2022; 14:nu14153022. [PMID: 35893876 PMCID: PMC9332257 DOI: 10.3390/nu14153022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2. Objective: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions. Methods: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests. Results: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 ± 33.19), Torin-2 (72.37 ± 17.33) treated cells, compared to rapamycin (250.45 ± 9.41) and the vehicle control (274.23 ± 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 ± 3.52), Torin-2 (55.93 ± 8.76), or rapamycin (80.01 ± 4.36, p = 0.006). Conclusions: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.
Collapse
|
10
|
Butin N, Bergès C, Portais JC, Bellvert F. An optimization method for untargeted MS-based isotopic tracing investigations of metabolism. Metabolomics 2022; 18:41. [PMID: 35713733 PMCID: PMC9205802 DOI: 10.1007/s11306-022-01897-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 05/17/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking. OBJECTIVES To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism. METHODS The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism. RESULTS The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time. CONCLUSION The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.
Collapse
Affiliation(s)
- Noémie Butin
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Cécilia Bergès
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Jean-Charles Portais
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France.
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France.
| |
Collapse
|
11
|
Fakouri Baygi S, Kumar Y, Barupal DK. IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets. J Proteome Res 2022; 21:1485-1494. [PMID: 35579321 DOI: 10.1021/acs.jproteome.2c00120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12C and 13C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.
Collapse
Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Yashwant Kumar
- Non-communicable Diseases Division, Translational Health Science and Technology Institute, Faridabad, Haryana 121001, India
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| |
Collapse
|
12
|
Guo J, Shen S, Huan T. Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box". Anal Chem 2022; 94:4260-4268. [PMID: 35245044 DOI: 10.1021/acs.analchem.1c04758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).
Collapse
Affiliation(s)
- Jian Guo
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Sam Shen
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| |
Collapse
|
13
|
Dietrich C, Wick A, Ternes TA. Open-source feature detection for non-target LC-MS analytics. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2022; 36:e9206. [PMID: 34614536 DOI: 10.1002/rcm.9206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 06/13/2023]
Abstract
RATIONALE Non-target screening techniques using high-resolution mass spectrometers become more and more important for environmental sciences. Highly reliable and sophisticated software solutions are required to deal with the large amount of data obtained from such analyses. METHODS Processing of high-resolution LC-HRMS data was performed upon conversion into an open, XML-based data format followed by an automated assignment of chromatographic peaks using the open-source programming language R. Raw data from three different LC-HRMS systems were processed as a proof of principle. RESULTS We present a simple and straightforward algorithm to extract chromatographic peaks from previously m/z-centroided data based on the open-source programming language R and C++. The working principle and processing parameters are explained in detail. A ready-to-use script is provided in the supporting information. CONCLUSIONS The developed algorithm enables a comprehensible automated peak picking of non-target LC-MS data. Application to three completely different HRMS raw data files showed reasonable False Positives and False Negatives detection and moderate calculation times.
Collapse
Affiliation(s)
| | - Arne Wick
- Federal Institute of Hydrology, Koblenz, Germany
| | | |
Collapse
|
14
|
Olkowicz M, Rosales-Solano H, Kulasingam V, Pawliszyn J. SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study. Sci Rep 2021; 11:22428. [PMID: 34789766 PMCID: PMC8599860 DOI: 10.1038/s41598-021-00802-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/15/2021] [Indexed: 12/11/2022] Open
Abstract
Epithelial ovarian cancer (EOC) is the most common cause of death from gynecological cancer. The outcomes of EOC are complicated, as it is often diagnosed late and comprises several heterogenous subtypes. As such, upfront treatment can be highly challenging. Although many significant advances in EOC management have been made over the past several decades, further work must be done to develop early detection tools capable of distinguishing between the various EOC subtypes. In this paper, we present a sophisticated analytical pipeline based on solid-phase microextraction (SPME) and three orthogonal LC/MS acquisition modes that facilitates the comprehensive mapping of a wide range of analytes in serum samples from patients with EOC. PLS-DA multivariate analysis of the metabolomic data was able to provide clear discrimination between all four main EOC subtypes: serous, endometrioid, clear cell, and mucinous carcinomas. The prognostic performance of discriminative metabolites and lipids was confirmed via multivariate receiver operating characteristic (ROC) analysis (AUC value > 88% with 20 features). Further pathway analysis using the top 57 dysregulated metabolic features showed distinct differences in amino acid, lipid, and steroids metabolism among the four EOC subtypes. Thus, metabolomic profiling can serve as a powerful tool for complementing histology in classifying EOC subtypes.
Collapse
Affiliation(s)
- Mariola Olkowicz
- Department of Chemistry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | | | - Vathany Kulasingam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Division of Clinical Biochemistry, University Health Network, Toronto, ON, M5G 2C4, Canada.
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
| |
Collapse
|
15
|
Delabriere A, Warmer P, Brennsteiner V, Zamboni N. SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS. Anal Chem 2021; 93:15024-15032. [PMID: 34735114 DOI: 10.1021/acs.analchem.1c02687] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Metabolomics has been shown to be promising for diverse applications in basic, applied, and clinical research. These applications often require large-scale data, and while the technology to perform such experiments exists, downstream analysis remains challenging. Different tools exist in a variety of ecosystems, but they often do not scale to large data and are not integrated into a single coherent workflow. Moreover, the outcome of processing is very sensitive to a multitude of algorithmic parameters. Hence, parameter optimization is not only critical but also challenging. We present SLAW, a scalable and yet easy-to-use workflow for processing untargeted LC-MS data in metabolomics and lipidomics. The capabilities of SLAW include (1) state-of-the-art peak-picking algorithms, (2) a new automated parameter optimization routine, (3) an efficient sample alignment procedure, (4) gap filling by data recursion, and (5) the extraction of consolidated MS2 and an isotopic pattern across all samples. Importantly, both the workflow and the parameter optimization were designed for robust analysis of untargeted studies with thousands of individual LC-MSn runs. We compared SLAW to two state-of-the-art workflows based on openMS and XCMS. SLAW was able to detect and align more reproducible features in all data sets considered. SLAW scaled well, and its analysis of a data set with 2500 LC-MS files consumed 40% less memory and was 6 times faster than that using the XCMS-based workflow. SLAW also extracted 2-fold more isotopic patterns and MS2 spectra, which in 60% of the cases led to positive matches against a spectral library.
Collapse
Affiliation(s)
- Alexis Delabriere
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - Philipp Warmer
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | | | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| |
Collapse
|
16
|
The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines. Metabolites 2021; 11:metabo11110713. [PMID: 34822371 PMCID: PMC8624948 DOI: 10.3390/metabo11110713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/13/2021] [Accepted: 10/16/2021] [Indexed: 11/30/2022] Open
Abstract
Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.
Collapse
|
17
|
Lassen J, Nielsen KL, Johannsen M, Villesen P. Assessment of XCMS Optimization Methods with Machine-Learning Performance. Anal Chem 2021; 93:13459-13466. [PMID: 34585906 DOI: 10.1021/acs.analchem.1c02000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.
Collapse
Affiliation(s)
- Johan Lassen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark
| | - Kirstine Lykke Nielsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Mogens Johannsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Palle Villesen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
| |
Collapse
|
18
|
Neto JCR, Vieira LR, de Aquino Ribeiro JA, de Sousa CAF, Júnior MTS, Abdelnur PV. Metabolic effect of drought stress on the leaves of young oil palm (Elaeis guineensis) plants using UHPLC-MS and multivariate analysis. Sci Rep 2021; 11:18271. [PMID: 34521943 PMCID: PMC8440612 DOI: 10.1038/s41598-021-97835-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 08/30/2021] [Indexed: 01/14/2023] Open
Abstract
The expansion of the oil palm in marginal areas can face challenges, such as water deficit, leading to an impact on palm oil production. A better understanding of the biological consequences of abiotic stresses on this crop can result from joint metabolic profiling and multivariate analysis. Metabolic profiling of leaves was performed from control and stressed plants (7 and 14 days of stress). Samples were extracted and analyzed on a UHPLC-ESI-Q-TOF-HRMS system. Acquired data were processed using XCMS Online and MetaboAnalyst for multivariate and pathway activity analysis. Metabolism was affected by drought stress through clear segregation between control and stressed groups. More importantly, metabolism changed through time, gradually from 7 to 14 days. The pathways most affected by drought stress were: starch and sucrose metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. The analysis of the metabolic profile were efficient to correlate and differentiate groups of oil palm plants submitted to different levels of drought stress. Putative compounds and their affected pathways can be used in future multiomics analysis.
Collapse
Affiliation(s)
- Jorge Candido Rodrigues Neto
- Institute of Chemistry, Federal University of Goiás, Campus Samambaia, Goiânia, GO, 74690-900, Brazil.,Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, 70770-901, Brazil
| | - Letícia Rios Vieira
- Graduate Program of Plant Biotechnology, Federal University of Lavras, CP 3037, Lavras, MG, 37200-000, Brazil.,Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, 70770-901, Brazil
| | | | | | - Manoel Teixeira Souza Júnior
- Graduate Program of Plant Biotechnology, Federal University of Lavras, CP 3037, Lavras, MG, 37200-000, Brazil.,Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, 70770-901, Brazil
| | - Patrícia Verardi Abdelnur
- Institute of Chemistry, Federal University of Goiás, Campus Samambaia, Goiânia, GO, 74690-900, Brazil. .,Brazilian Agricultural Research Corporation, Embrapa Agroenergy, Brasília, DF, 70770-901, Brazil.
| |
Collapse
|
19
|
Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques PÉ, Li S, Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 2021; 49:W388-W396. [PMID: 34019663 PMCID: PMC8265181 DOI: 10.1093/nar/gkab382] [Citation(s) in RCA: 2144] [Impact Index Per Article: 714.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/17/2021] [Accepted: 04/27/2021] [Indexed: 12/31/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jasmine Chong
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | | | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Michel Barrette
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Carol Gauthier
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Pierre-Étienne Jacques
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Animal Science, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
20
|
Plyushchenko IV, Fedorova ES, Potoldykova NV, Polyakovskiy KA, Glukhov AI, Rodin IA. Omics Untargeted Key Script: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study. J Proteome Res 2021; 21:833-847. [PMID: 34161108 DOI: 10.1021/acs.jproteome.1c00392] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale untargeted LC-MS-based metabolomic profiling is a valuable source for systems biology and biomarker discovery. Data analysis and processing are major tasks due to the high complexity of generated signals and the presence of unwanted variations. In the present study, we introduce an R-based open-source collection of scripts called OUKS (Omics Untargeted Key Script), which provides comprehensive data processing. OUKS is developed by integrating various R packages and metabolomics software tools and can be easily set up and prepared to create a custom pipeline. Novel computational features are related to quality control samples-based signal processing and are implemented by gradient boosting, tree-based, and other nonlinear regression algorithms. Bladder cancer biomarkers discovery study which is based on untargeted LC-MS profiling of urine samples is performed to demonstrate exhaustive functionality of the developed software tool. Unique examination among dozens of metabolomics-specific data curation methods was carried out at each processing step. As a result, potential biomarkers were identified, statistically validated, and described by metabolism disorders. Our study demonstrates that OUKS helps to make untargeted LC-MS metabolomic profiling with the latest computational features readily accessible in a ready-to-use unified manner to a research community.
Collapse
Affiliation(s)
- Ivan V Plyushchenko
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Elizaveta S Fedorova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 119071 Moscow, Russia
| | - Natalia V Potoldykova
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Konstantin A Polyakovskiy
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Alexander I Glukhov
- Biology Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Biochemistry, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Igor A Rodin
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Epidemiology and Evidence-Based Medicine, Sechenov First Moscow State Medical University, 119435 Moscow, Russia
| |
Collapse
|
21
|
Barupal DK, Baygi SF, Wright RO, Arora M. Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics. Front Public Health 2021; 9:653599. [PMID: 34178917 PMCID: PMC8222544 DOI: 10.3389/fpubh.2021.653599] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 01/27/2023] Open
Abstract
Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.
Collapse
|
22
|
Walmsley SJ, Guo J, Murugan P, Weight CJ, Wang J, Villalta PW, Turesky RJ. Comprehensive Analysis of DNA Adducts Using Data-Independent wSIM/MS 2 Acquisition and wSIM-City. Anal Chem 2021; 93:6491-6500. [PMID: 33844920 PMCID: PMC8675643 DOI: 10.1021/acs.analchem.1c00362] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A novel software has been created to comprehensively characterize covalent modifications of DNA through mass spectral analysis of enzymatically hydrolyzed DNA using the neutral loss of 2'-deoxyribose, a nearly universal MS2 fragmentation process of protonated 2'-deoxyribonucleosides. These covalent modifications termed DNA adducts form through xenobiotic exposures or by reaction with endogenous electrophiles and can induce mutations during cell division and initiate carcinogenesis. DNA adducts are typically present at trace levels in the human genome, requiring a very sensitive and comprehensive data acquisition and analysis method. Our software, wSIM-City, was created to process mass spectral data acquired by a wide selected ion monitoring (wSIM) with gas-phase fractionation and coupled to wide MS2 fragmentation. This untargeted approach can detect DNA adducts at trace levels as low as 1.5 adducts per 109 nucleotides. This level of sensitivity is sufficient for comprehensive analysis and characterization of DNA modifications in human specimens.
Collapse
Affiliation(s)
- Scott J Walmsley
- Masonic Cancer Center, University of Minnesota, Minneapolis 55455, Minnesota, United States
- Institute of Health Informatics, University of Minnesota, Minneapolis 55455, Minnesota, United States
| | - Jingshu Guo
- Masonic Cancer Center, University of Minnesota, Minneapolis 55455, Minnesota, United States
- Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, Minneapolis 55455, Minnesota, United States
| | - Paari Murugan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis 55455, Minnesota, United States
| | - Christopher J Weight
- Glickman Urologic and Kidney Institute, Cleveland Clinic, Cleveland 44125, Ohio, United States
- Case Comprehensive Cancer Center, Cleveland 44106, Ohio, United States
| | - Jinhua Wang
- Masonic Cancer Center, University of Minnesota, Minneapolis 55455, Minnesota, United States
- Institute of Health Informatics, University of Minnesota, Minneapolis 55455, Minnesota, United States
| | - Peter W Villalta
- Masonic Cancer Center, University of Minnesota, Minneapolis 55455, Minnesota, United States
- Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, Minneapolis 55455, Minnesota, United States
| | - Robert J Turesky
- Masonic Cancer Center, University of Minnesota, Minneapolis 55455, Minnesota, United States
- Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, Minneapolis 55455, Minnesota, United States
| |
Collapse
|
23
|
Phytotoxic Tryptoquialanines Produced In Vivo by Penicillium digitatum Are Exported in Extracellular Vesicles. mBio 2021; 12:mBio.03393-20. [PMID: 33563828 PMCID: PMC7885104 DOI: 10.1128/mbio.03393-20] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
During the postharvest period, citrus fruits can be affected by phytopathogens such as Penicillium digitatum, which causes green mold disease and is responsible for up to 90% of total citrus losses. Chemical fungicides are widely used to prevent green mold disease, leading to concerns about environmental and health risks. Penicillium digitatum is the most aggressive pathogen of citrus fruits. Tryptoquialanines are major indole alkaloids produced by P. digitatum. It is unknown if tryptoquialanines are involved in the damage of citrus fruits caused by P. digitatum. To investigate the pathogenic roles of tryptoquialanines, we initially asked if tryptoquialanines could affect the germination of Citrus sinensis seeds. Exposure of the citrus seeds to tryptoquialanine A resulted in a complete inhibition of germination and an altered metabolic response. Since this phytotoxic effect requires the extracellular export of tryptoquialanine A, we investigated the mechanisms of extracellular delivery of this alkaloid in P. digitatum. We detected extracellular vesicles (EVs) released by P. digitatum both in culture and during infection of citrus fruits. Compositional analysis of EVs produced during infection revealed the presence of a complex cargo, which included tryptoquialanines and the mycotoxin fungisporin. The EVs also presented phytotoxicity activity in vitro and caused damage to the tissues of citrus seeds. Through molecular networking, it was observed that the metabolites present in the P. digitatum EVs are produced in all of its possible hosts. Our results reveal a novel phytopathogenic role of P. digitatum EVs and tryptoquialanine A, implying that this alkaloid is exported in EVs during plant infection.
Collapse
|
24
|
Chaker J, Gilles E, Léger T, Jégou B, David A. From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening. Anal Chem 2020; 93:1792-1800. [DOI: 10.1021/acs.analchem.0c04660] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Erwann Gilles
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Thibaut Léger
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Bernard Jégou
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR_S 1085, F-35000 Rennes, France
| |
Collapse
|
25
|
Stricker T, Bonner R, Lisacek F, Hopfgartner G. Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification. Anal Bioanal Chem 2020; 413:503-517. [PMID: 33123762 PMCID: PMC7806579 DOI: 10.1007/s00216-020-03019-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 09/21/2020] [Accepted: 10/19/2020] [Indexed: 12/31/2022]
Abstract
Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]+ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]+ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]+ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. Graphical abstract ![]()
Collapse
Affiliation(s)
- Thomas Stricker
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, 1211, Geneva 4, Switzerland
- Proteome Informatics Group (PIG), Swiss Institute of Bioinformatics and University of Geneva, 7, route de Drize, 1211, Geneva 4, Switzerland
| | - Ron Bonner
- Ron Bonner Consulting, Newmarket, ON, L3Y 3C7, Canada
| | - Frédérique Lisacek
- Proteome Informatics Group (PIG), Swiss Institute of Bioinformatics and University of Geneva, 7, route de Drize, 1211, Geneva 4, Switzerland
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, 1211, Geneva 4, Switzerland.
| |
Collapse
|
26
|
Ventura G, Calvano CD, Porcelli V, Palmieri L, De Giacomo A, Xu Y, Goodacre R, Palmisano F, Cataldi TRI. Phospholipidomics of peripheral blood mononuclear cells (PBMCs): the tricky case of children with autism spectrum disorder (ASD) and their healthy siblings. Anal Bioanal Chem 2020; 412:6859-6874. [DOI: 10.1007/s00216-020-02817-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/08/2020] [Accepted: 07/14/2020] [Indexed: 12/19/2022]
|
27
|
Guo Z, Zhu Z, Huang S, Wang J. Non-targeted screening of pesticides for food analysis using liquid chromatography high-resolution mass spectrometry-a review. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2020; 37:1180-1201. [DOI: 10.1080/19440049.2020.1753890] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Zeqin Guo
- College of Bioengineering, Chongqing University, Chongqing, P. R. China
| | - Zhiguo Zhu
- College of Pharmacy and Life Science, Jiujiang University, Jiujiang, P.R. China
| | - Sheng Huang
- College of Bioengineering, Chongqing University, Chongqing, P. R. China
| | - Jianhua Wang
- College of Bioengineering, Chongqing University, Chongqing, P. R. China
| |
Collapse
|
28
|
Pang Z, Chong J, Li S, Xia J. MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics. Metabolites 2020; 10:E186. [PMID: 32392884 PMCID: PMC7281575 DOI: 10.3390/metabo10050186] [Citation(s) in RCA: 315] [Impact Index Per Article: 78.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 04/30/2020] [Accepted: 05/03/2020] [Indexed: 12/26/2022] Open
Abstract
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
Collapse
Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (J.C.)
| | - Jasmine Chong
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (J.C.)
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, Canada;
| | - Jianguo Xia
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (J.C.)
- Department of Animal Science, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
| |
Collapse
|