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Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. bioRxiv 2023:2023.11.21.568121. [PMID: 38045370 PMCID: PMC10690215 DOI: 10.1101/2023.11.21.568121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.
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Rajczewski A, Ndreu L, Vryonidis E, Hurben AK, Jamshidi S, Griffin TJ, Törnqvist MÅ, Tretyakova NY, Karlsson I. Mass Spectrometry-Based Strategies for Assessing Human Exposure Using Hemoglobin Adductomics. Chem Res Toxicol 2023; 36:2019-2030. [PMID: 37963067 PMCID: PMC10731639 DOI: 10.1021/acs.chemrestox.3c00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023]
Abstract
Hemoglobin (Hb) adducts are widely used in human biomonitoring due to the high abundance of hemoglobin in human blood, its reactivity toward electrophiles, and adducted protein stability for up to 120 days. In the present paper, we compared three methods of analysis of hemoglobin adducts: mass spectrometry of derivatized N-terminal Val adducts, mass spectrometry of N-terminal adducted hemoglobin peptides, and limited proteolysis mass spectrometry . Blood from human donors was incubated with a selection of contact allergens and other electrophiles, after which hemoglobin was isolated and subjected to three analysis methods. We found that the FIRE method was able to detect and reliably quantify N-terminal adducts of acrylamide, acrylic acid, glycidic acid, and 2,3-epoxypropyl phenyl ether (PGE), but it was less efficient for 2-methyleneglutaronitrile (2-MGN) and failed to detect 1-chloro-2,4-dinitrobenzene (DNCB). By contrast, bottom-up proteomics was able to determine the presence of adducts from all six electrophiles at both the N-terminus and reactive hemoglobin side chains. Limited proteolysis mass spectrometry, studied for four contact allergens (three electrophiles and a metal salt), was able to determine the presence of covalent hemoglobin adducts with one of the three electrophiles (DNCB) and coordination complexation with the nickel salt. Together, these approaches represent complementary tools in the study of the hemoglobin adductome.
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Affiliation(s)
- Andrew
T. Rajczewski
- Department
of Biochemistry, University of Minnesota, Minneapolis, Minnesota55455, United States
| | - Lorena Ndreu
- Department
of Environmental Science, Stockholm University, SE-10691Stockholm, Sweden
| | - Efstathios Vryonidis
- Department
of Environmental Science, Stockholm University, SE-10691Stockholm, Sweden
| | - Alexander K. Hurben
- Department
of Medicinal Chemistry and the Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota55455, United States
| | - Sara Jamshidi
- Department
of Environmental Science, Stockholm University, SE-10691Stockholm, Sweden
| | - Timothy J. Griffin
- Department
of Biochemistry, University of Minnesota, Minneapolis, Minnesota55455, United States
| | | | - Natalia Y. Tretyakova
- Department
of Medicinal Chemistry and the Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota55455, United States
| | - Isabella Karlsson
- Department
of Environmental Science, Stockholm University, SE-10691Stockholm, Sweden
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Murray KJ, Villalta PW, Griffin TJ, Balbo S. Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS. Chem Res Toxicol 2023; 36:1666-1682. [PMID: 37862059 DOI: 10.1021/acs.chemrestox.3c00209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.
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Affiliation(s)
- Kevin J Murray
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Peter W Villalta
- Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota 55455, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Silvia Balbo
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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Bihani S, Gupta A, Mehta S, Rajczewski AT, Johnson J, Borishetty D, Griffin TJ, Srivastava S, Jagtap PD. Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients. J Proteome Res 2023; 22:2608-2619. [PMID: 37450889 DOI: 10.1021/acs.jproteome.3c00040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.
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Affiliation(s)
- Surbhi Bihani
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Aryan Gupta
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Dhanush Borishetty
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - Sanjeeva Srivastava
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
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Schiml VC, Delogu F, Kumar P, Kunath B, Batut B, Mehta S, Johnson JE, Grüning B, Pope PB, Jagtap PD, Griffin TJ, Arntzen MØ. Integrative meta-omics in Galaxy and beyond. Environ Microbiome 2023; 18:56. [PMID: 37420292 DOI: 10.1186/s40793-023-00514-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/05/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND 'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. RESULTS In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. CONCLUSION Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.
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Affiliation(s)
- Valerie C Schiml
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway
| | - Francesco Delogu
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway
| | - Praveen Kumar
- Department of Biochemistry, Biophysics and Molecular Biology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Benoit Kunath
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway
| | - Bérénice Batut
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Subina Mehta
- Department of Biochemistry, Biophysics and Molecular Biology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Phillip B Pope
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway
| | - Pratik D Jagtap
- Department of Biochemistry, Biophysics and Molecular Biology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Timothy J Griffin
- Department of Biochemistry, Biophysics and Molecular Biology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, 1432, Ås, Norway.
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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Weise DO, Kruk ME, Higgins L, Markowski TW, Jagtap PD, Mehta S, Mickelson A, Parker LL, Wendt CH, Griffin TJ. An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples. Clin Proteomics 2023; 20:14. [PMID: 37005570 PMCID: PMC10068177 DOI: 10.1186/s12014-023-09404-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/13/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Clinical bronchoalveolar lavage fluid (BALF) samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, mass spectrometry (MS)-based proteomic analysis of BALF is challenged by the dynamic range of protein abundance, and potential for interfering contaminants. A robust, MS-based proteomics compatible sample preparation workflow for BALF samples, including those of small and large volume, would be useful for many researchers. RESULTS We have developed a workflow that combines high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, that is compatible with either qualitative or quantitative MS-based proteomic analysis. The workflow includes a value-added collection of endogenous peptides for peptidomic analysis of BALF samples, if desired, as well as amenability to offline semi-preparative or microscale fractionation of complex peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We demonstrate the effectiveness of this workflow on BALF samples collected from COPD patients, including for smaller sample volumes of 1-5 mL that are commonly available from the clinic. We also demonstrate the repeatability of the workflow as an indicator of its utility for quantitative proteomic studies. CONCLUSIONS Overall, our described workflow consistently provided high quality proteins and tryptic peptides for MS analysis. It should enable researchers to apply MS-based proteomics to a wide-variety of studies focused on BALF clinical specimens.
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Affiliation(s)
- Danielle O Weise
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Medical School, University of Minnesota, Minneapolis, MN, USA
| | - Monica E Kruk
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Medical School, University of Minnesota, Minneapolis, MN, USA
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Todd W Markowski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Alan Mickelson
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Medical School, University of Minnesota, Minneapolis, MN, USA
| | - Laurie L Parker
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Christine H Wendt
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Medical School, University of Minnesota, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA.
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Samorodnitsky S, Lock EF, Kruk M, Morris A, Leung JM, Kunisaki KM, Griffin TJ, Wendt CH. Lung proteome and metabolome endotype in HIV-associated obstructive lung disease. ERJ Open Res 2023; 9:00332-2022. [PMID: 36949960 PMCID: PMC10026002 DOI: 10.1183/23120541.00332-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
Purpose Obstructive lung disease is increasingly common among persons with HIV, both smokers and nonsmokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease. Methods Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status and antiretroviral treatment. 6414 proteins were measured using SomaScan® aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1 s % predicted (FEV1 % pred). Significant proteins were entered into a pathway over-representation analysis. We also constructed protein-driven endotypes using K-means clustering and performed over-representation analysis on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort. Results After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found 575 proteins that were significantly correlated with FEV1 % pred after multiple comparisons adjustment. We identified two protein-driven endotypes, one of which was associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma. Conclusion Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1 % pred; however, we identified a unique protein endotype associated with insulin and apoptotic pathways.
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Affiliation(s)
| | | | - Monica Kruk
- University of Minnesota, Minneapolis, MN, USA
| | - Alison Morris
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Ken M. Kunisaki
- University of Minnesota, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | | | - Chris H. Wendt
- University of Minnesota, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Corresponding author: Chris Wendt ()
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9
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Wendt CH, Samorodnitsky S, Lock EF, Kruk M, Morris A, Leung JM, Kunisaki KM, Griffin TJ. Lung and Plasma Metabolome in HIV-Associated Obstructive Lung Disease. J Acquir Immune Defic Syndr 2022; 91:312-318. [PMID: 35849661 PMCID: PMC9588728 DOI: 10.1097/qai.0000000000003061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/05/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND HIV is a risk factor for obstructive lung disease (OLD), independent of smoking. We used mass spectrometry (MS) approaches to identify metabolomic biomarkers that inform mechanistic pathogenesis of OLD in persons with HIV (PWH). METHODS We obtained bronchoalveolar lavage fluid (BALF) samples from 52 PWH, in case:control (+OLD/-OLD) pairs matched on age, smoking status, and antiretroviral treatment. Four hundred nine metabolites from 8 families were measured on BALF and plasma samples using a MS-based Biocrates platform. After filtering metabolites with a high proportion of missing values and values below the level of detection, we performed univariate testing using paired t tests followed by false discovery rate corrections. We used distance-weighted discrimination (DWD) to test for an overall difference in the metabolite profile between cases and controls. RESULTS After filtering, there were 252 BALF metabolites for analysis from 8 metabolite families. DWD testing found that collectively, BALF metabolites differentiated cases from controls, whereas plasma metabolites did not. In BALF samples, we identified 3 metabolites that correlated with OLD at the false discovery rate of 10%; all were in the phosphatidylcholine family. We identified additional BALF metabolites when analyzing lung function as a continuous variable, and these included acylcarnitines, triglycerides, and a cholesterol ester. CONCLUSIONS Collectively, BALF metabolites differentiate PWH with and without OLD. These included several BALF lipid metabolites. These findings were limited to BALF and were not found in plasma from the same individuals. Phosphatidylcholine, the most common lipid component of surfactant, was the predominant lipid metabolite differentially expressed.
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Affiliation(s)
- Chris H. Wendt
- Minneapolis VA Health Care System, Minneapolis, MN, U.S
- University of Minnesota, Minneapolis, MN, U.S
| | | | | | - Monica Kruk
- University of Minnesota, Minneapolis, MN, U.S
| | - Alison Morris
- University of Pittsburgh School of Medicine, Pittsburgh, PA, U.S
| | | | - Ken M. Kunisaki
- Minneapolis VA Health Care System, Minneapolis, MN, U.S
- University of Minnesota, Minneapolis, MN, U.S
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Mehta S, Carvalho VM, Rajczewski AT, Pible O, Grüning BA, Johnson JE, Wagner R, Armengaud J, Griffin TJ, Jagtap PD. Catching the Wave: Detecting Strain-Specific SARS-CoV-2 Peptides in Clinical Samples Collected during Infection Waves from Diverse Geographical Locations. Viruses 2022; 14:2205. [PMID: 36298760 PMCID: PMC9609567 DOI: 10.3390/v14102205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/05/2022] Open
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a major health crisis worldwide with its continuously emerging new strains, resulting in new viral variants that drive "waves" of infection. PCR or antigen detection assays have been routinely used to detect clinical infections; however, the emergence of these newer strains has presented challenges in detection. One of the alternatives has been to detect and characterize variant-specific peptide sequences from viral proteins using mass spectrometry (MS)-based methods. MS methods can potentially help in both diagnostics and vaccine development by understanding the dynamic changes in the viral proteome associated with specific strains and infection waves. In this study, we developed an accessible, flexible, and shareable bioinformatics workflow that was implemented in the Galaxy Platform to detect variant-specific peptide sequences from MS data derived from the clinical samples. We demonstrated the utility of the workflow by characterizing published clinical data from across the world during various pandemic waves. Our analysis identified six SARS-CoV-2 variant-specific peptides suitable for confident detection by MS in commonly collected clinical samples.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Andrew T. Rajczewski
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, 30200 Bagnols-sur-Cèze, France
| | - Björn A. Grüning
- Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Reid Wagner
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, 30200 Bagnols-sur-Cèze, France
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
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11
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Afgan E, Nekrutenko A, Grüning BA, Blankenberg D, Goecks J, Schatz MC, Ostrovsky AE, Mahmoud A, Lonie AJ, Syme A, Fouilloux A, Bretaudeau A, Nekrutenko A, Kumar A, Eschenlauer AC, DeSanto AD, Guerler A, Serrano-Solano B, Batut B, Grüning BA, Langhorst BW, Carr B, Raubenolt BA, Hyde CJ, Bromhead CJ, Barnett CB, Royaux C, Gallardo C, Blankenberg D, Fornika DJ, Baker D, Bouvier D, Clements D, de Lima Morais DA, Tabernero DL, Lariviere D, Nasr E, Afgan E, Zambelli F, Heyl F, Psomopoulos F, Coppens F, Price GR, Cuccuru G, Corguillé GL, Von Kuster G, Akbulut GG, Rasche H, Hotz HR, Eguinoa I, Makunin I, Ranawaka IJ, Taylor JP, Joshi J, Hillman-Jackson J, Goecks J, Chilton JM, Kamali K, Suderman K, Poterlowicz K, Yvan LB, Lopez-Delisle L, Sargent L, Bassetti ME, Tangaro MA, van den Beek M, Čech M, Bernt M, Fahrner M, Tekman M, Föll MC, Schatz MC, Crusoe MR, Roncoroni M, Kucher N, Coraor N, Stoler N, Rhodes N, Soranzo N, Pinter N, Goonasekera NA, Moreno PA, Videm P, Melanie P, Mandreoli P, Jagtap PD, Gu Q, Weber RJM, Lazarus R, Vorderman RHP, Hiltemann S, Golitsynskiy S, Garg S, Bray SA, Gladman SL, Leo S, Mehta SP, Griffin TJ, Jalili V, Yves V, Wen V, Nagampalli VK, Bacon WA, de Koning W, Maier W, Briggs PJ. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Res 2022; 50:W345-W351. [PMID: 35446428 PMCID: PMC9252830 DOI: 10.1093/nar/gkac247] [Citation(s) in RCA: 223] [Impact Index Per Article: 111.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/17/2022] [Accepted: 03/30/2022] [Indexed: 01/19/2023] Open
Abstract
Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.
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12
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Rajczewski AT, Han Q, Mehta S, Kumar P, Jagtap PD, Knutson CG, Fox JG, Tretyakova NY, Griffin TJ. Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow. Proteomes 2022; 10:proteomes10020011. [PMID: 35466239 PMCID: PMC9036229 DOI: 10.3390/proteomes10020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/27/2022] [Accepted: 04/07/2022] [Indexed: 11/24/2022] Open
Abstract
Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2−/−Il10−/− mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Qiyuan Han
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Charles G. Knutson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (C.G.K.); (J.G.F.)
| | - James G. Fox
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (C.G.K.); (J.G.F.)
| | - Natalia Y. Tretyakova
- Department of Medicinal Chemistry, the Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
- Correspondence:
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Abstract
INTRODUCTION Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems. AREAS COVERED Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics. EXPERT COMMENTARY Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Coauthor, Research Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
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14
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 PMCID: PMC8674281 DOI: 10.1038/s41467-021-27542-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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15
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 DOI: 10.1101/2021.03.05.433915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 05/21/2023] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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Lamprecht AL, Palmblad M, Ison J, Schwämmle V, Al Manir MS, Altintas I, Baker CJO, Ben Hadj Amor A, Capella-Gutierrez S, Charonyktakis P, Crusoe MR, Gil Y, Goble C, Griffin TJ, Groth P, Ienasescu H, Jagtap P, Kalaš M, Kasalica V, Khanteymoori A, Kuhn T, Mei H, Ménager H, Möller S, Richardson RA, Robert V, Soiland-Reyes S, Stevens R, Szaniszlo S, Verberne S, Verhoeven A, Wolstencroft K. Perspectives on automated composition of workflows in the life sciences. F1000Res 2021; 10:897. [PMID: 34804501 PMCID: PMC8573700 DOI: 10.12688/f1000research.54159.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/27/2021] [Indexed: 12/29/2022] Open
Abstract
Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.
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Affiliation(s)
| | - Magnus Palmblad
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Jon Ison
- French Institute of Bioinformatics, 91057 Évry, France
| | | | | | - Ilkay Altintas
- University of California San Diego, La Jolla, CA, 92093, USA
| | - Christopher J. O. Baker
- University of New Brunswick, Saint John, E2L 4L5, Canada
- IPSNP Computing Inc., Saint John, E2L 4S6, Canada
| | | | | | | | | | - Yolanda Gil
- University of Southern California, Marina Del Rey, CA, 90292, USA
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Paul Groth
- University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Hans Ienasescu
- Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Pratik Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | | | | | | | - Tobias Kuhn
- VU Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | | | - Steffen Möller
- IBIMA, Rostock University Medical Center, 18057 Rostock, Germany
| | | | | | - Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
- Informatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Robert Stevens
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | | | - Suzan Verberne
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
| | - Aswin Verhoeven
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
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17
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Rajczewski AT, Ndreu L, Pujari SS, Griffin TJ, Törnqvist MÅ, Karlsson I, Tretyakova NY. Novel 4-Hydroxybenzyl Adducts in Human Hemoglobin: Structures and Mechanisms of Formation. Chem Res Toxicol 2021; 34:1769-1781. [PMID: 34110810 PMCID: PMC10159211 DOI: 10.1021/acs.chemrestox.1c00111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Humans are exposed to large numbers of electrophiles from their diet, the environment, and endogenous physiological processes. Adducts formed at the N-terminal valine of hemoglobin are often used as biomarkers of human exposure to electrophilic compounds. We previously reported the formation of hemoglobin N-terminal valine adducts (added mass, 106.042 Da) in the blood of human smokers and nonsmokers and identified their structure as 4-hydroxybenzyl-Val. In the present work, mass spectrometry-based proteomics was utilized to identify additional sites for 4-hydroxybenzyl adduct formation at internal nucleophilic amino acid side chains within hemoglobin. Hemoglobin isolated from human blood was treated with para-quinone methide (para-QM) followed by global nanoLC-MS/MS and targeted nanoLC-MS/MS to identify amino acid residues containing the 4-hydroxybenzyl modification. Our experiments revealed the formation of 4-hydroxybenzyl adducts at the αHis20, αTyr24, αTyr42, αHis45, βSer72, βThr84, βThr87, βSer89, βHis92, βCys93, βCys112, βThr123, and βHis143 residues (in addition to N-terminal valine) through characteristic MS/MS spectra. These amino acid side chains had variable reactivity toward para-QM with αHis45, αTyr42, βCys93, βHis92, and βSer72 forming the largest numbers of adducts upon exposure to para-QM. Two additional mechanisms for formation of 4-hydroxybenzyl adducts in humans were investigated: exposure to 4-hydroxybenzaldehyde (4-HBA) followed by reduction and UV-mediated reactions of hemoglobin with tyrosine. Exposure of hemoglobin to a 5-fold molar excess of 4-HBA followed by reduction with sodium cyanoborohydride produced 4-hydroxybenzyl adducts at several amino acid side chains of which αHis20, αTyr24, αTyr42, αHis45, βSer44, βThr84, and βHis92 were verified in targeted mass spectrometry experiments. Similarly, exposure of human blood to ultraviolet radiation produced 4-hydroxybenzyl adducts at αHis20, αTyr24, αTyr42, αHis45, βSer44, βThr84, and βSer89. Overall, our results reveal that 4-hydroxybenzyl adducts form at multiple nucleophilic sites of hemoglobin and that para-QM is the most likely source of these adducts in humans.
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Affiliation(s)
- Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Lorena Ndreu
- Department of Environmental Science, Stockholm University, Stockholm SE-106 91, Sweden
| | - Suresh S Pujari
- Department of Medicinal Chemistry and the Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Margareta Å Törnqvist
- Department of Environmental Science, Stockholm University, Stockholm SE-106 91, Sweden
| | - Isabella Karlsson
- Department of Environmental Science, Stockholm University, Stockholm SE-106 91, Sweden
| | - Natalia Y Tretyakova
- Department of Medicinal Chemistry and the Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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18
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Rajczewski AT, Mehta S, Nguyen DDA, Grüning B, Johnson JE, McGowan T, Griffin TJ, Jagtap PD. A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19). Clin Proteomics 2021; 18:15. [PMID: 33971807 PMCID: PMC8107781 DOI: 10.1186/s12014-021-09321-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/01/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). RESULTS Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. CONCLUSION We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.
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Affiliation(s)
- Andrew T Rajczewski
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Dinh Duy An Nguyen
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Björn Grüning
- Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA.
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Mehta S, Kumar P, Crane M, Johnson JE, Sajulga R, Nguyen DDA, McGowan T, Arntzen MØ, Griffin TJ, Jagtap PD. Updates on metaQuantome Software for Quantitative Metaproteomics. J Proteome Res 2021; 20:2130-2137. [PMID: 33683127 DOI: 10.1021/acs.jproteome.0c00960] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Marie Crane
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Dinh Duy An Nguyen
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås 1432, Norway
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
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20
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Rajczewski AT, Mehta S, Nguyen DDA, Grüning BA, Johnson JE, McGowan T, Griffin TJ, Jagtap PD. A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19). medRxiv 2021:2021.02.09.21251427. [PMID: 33688669 PMCID: PMC7941646 DOI: 10.1101/2021.02.09.21251427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Dinh Duy An Nguyen
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Björn A. Grüning
- Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
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21
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/12/2021] [Indexed: 12/13/2022] Open
Abstract
The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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22
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Mehta S, Crane M, Leith E, Batut B, Hiltemann S, Arntzen MØ, Kunath BJ, Pope PB, Delogu F, Sajulga R, Kumar P, Johnson JE, Griffin TJ, Jagtap PD. ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Res 2021; 10:103. [PMID: 34484688 PMCID: PMC8383124 DOI: 10.12688/f1000research.28608.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 12/13/2022] Open
Abstract
The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.
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Affiliation(s)
- Subina Mehta
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Marie Crane
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Emma Leith
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Bérénice Batut
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | | | | | - Ray Sajulga
- University of Minnesota, Twin Cities, MN, 55455, USA
| | - Praveen Kumar
- University of Minnesota, Twin Cities, MN, 55455, USA
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23
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Thuy-Boun PS, Mehta S, Gruening B, McGowan T, Nguyen A, Rajczewski A, Johnson JE, Griffin TJ, Wolan DW, Jagtap PD. Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms. J Proteome Res 2021; 20:1451-1454. [PMID: 33393790 PMCID: PMC7805602 DOI: 10.1021/acs.jproteome.0c00822] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Indexed: 01/06/2023]
Abstract
In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.
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Affiliation(s)
| | | | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany
| | | | - An Nguyen
- University of Minnesota, Minneapolis, MN, USA
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24
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Boylan KLM, Afiuni-Zadeh S, Geller MA, Argenta PA, Griffin TJ, Skubitz APN. Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics. Clin Proteomics 2021; 18:4. [PMID: 33413078 PMCID: PMC7792339 DOI: 10.1186/s12014-020-09309-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/14/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The purpose of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer protein biomarkers. METHODS Proteins were concentrated by acetone precipitation from the cell-free supernatant of the liquid-based Pap test fixative or eluted from the cervical swab. Protein was also extracted from the patient's tumor tissue. The protein samples were digested into peptides with trypsin, then the peptides were run on 2D-liquid chromatography mass spectrometry (2D-LCMS). The data was searched against a human protein database for the identification of peptides and proteins in each biospecimen. The proteins that were identified were classified for cellular localization and molecular function by bioinformatics integration. RESULTS We identified almost 5000 proteins total in the three matched biospecimens. More than 2000 proteins were expressed in each of the three biospecimens, including several known ovarian cancer biomarkers such as CA125, HE4, and mesothelin. By Scaffold analysis of the protein Gene Ontology categories and functional analysis using PANTHER, the proteins were classified by cellular localization and molecular function, demonstrating that the Pap test fluid and cervical swab proteins are similar to each other, and also to the tumor extract. CONCLUSIONS Our results suggest that Pap test fixatives and cervical swabs are a rich source of tumor-specific biomarkers for ovarian cancer, which could be developed as a test for ovarian cancer detection.
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Affiliation(s)
- Kristin L M Boylan
- Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, MMC 395, 420 Delaware St. SE, Minneapolis, MN, 55455, USA.,Ovarian Cancer Early Detection Program, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Somaieh Afiuni-Zadeh
- Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, MMC 395, 420 Delaware St. SE, Minneapolis, MN, 55455, USA.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Health, Toronto, ON, M5G 1X5, Canada
| | - Melissa A Geller
- Department of Obstetrics, Gynecology, & Women's Health, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Peter A Argenta
- Department of Obstetrics, Gynecology, & Women's Health, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, & Biophysics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Amy P N Skubitz
- Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, MMC 395, 420 Delaware St. SE, Minneapolis, MN, 55455, USA. .,Department of Obstetrics, Gynecology, & Women's Health, University of Minnesota Medical School, Minneapolis, MN, USA. .,Ovarian Cancer Early Detection Program, University of Minnesota Medical School, Minneapolis, MN, USA.
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Guerrero CR, Maier LA, Griffin TJ, Higgins L, Najt CP, Perlman DM, Bhargava M. Application of Proteomics in Sarcoidosis. Am J Respir Cell Mol Biol 2020; 63:727-738. [PMID: 32804537 DOI: 10.1165/rcmb.2020-0070ps] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/17/2020] [Indexed: 02/03/2023] Open
Abstract
Sarcoidosis is a multisystem disease with heterogeneity in manifestations and outcomes. System-level studies leveraging "omics" technologies are expected to define mechanisms contributing to sarcoidosis heterogeneous manifestations and course. With improvements in mass spectrometry (MS) and bioinformatics, it is possible to study protein abundance for a large number of proteins simultaneously. Contemporary fast-scanning MS enables the acquisition of spectral data for deep coverage of the proteins with data-dependent or data-independent acquisition MS modes. Studies leveraging MS-based proteomics in sarcoidosis have characterized BAL fluid (BALF), alveolar macrophages, plasma, and exosomes. These studies identified several differentially expressed proteins, including protocadherin-2 precursor, annexin A2, pulmonary surfactant A2, complement factors C3, vitamin-D-binding protein, cystatin B, and amyloid P, comparing subjects with sarcoidosis with control subjects. Other studies identified ceruloplasmin, complement factors B, C3, and 1, and others with differential abundance in sarcoidosis compared with other interstitial lung diseases. Using quantitative proteomics, most recent studies found differences in PI3K/Akt/mTOR, MAP kinase, pluripotency-associated transcriptional factor, and hypoxia response pathways. Other studies identified increased clathrin-mediated endocytosis and Fcγ receptor-mediated phagocytosis pathways in sarcoidosis alveolar macrophages. Although studies in mixed BAL and blood cells or plasma are limited, some of the changes in lung compartment are detected in the blood cells and plasma. We review proteomics for sarcoidosis with a focus on the existing MS data acquisition strategies, bioinformatics for spectral data analysis to infer protein identity and quantity, unique aspects about biospecimen collection and processing for lung-related proteomics, and proteomics studies conducted to date in sarcoidosis.
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Affiliation(s)
- Candance R Guerrero
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Lisa A Maier
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, Colorado
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Charles P Najt
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - David M Perlman
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
| | - Maneesh Bhargava
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
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Sajulga R, Easterly C, Riffle M, Mesuere B, Muth T, Mehta S, Kumar P, Johnson J, Gruening BA, Schiebenhoefer H, Kolmeder CA, Fuchs S, Nunn BL, Rudney J, Griffin TJ, Jagtap PD. Survey of metaproteomics software tools for functional microbiome analysis. PLoS One 2020; 15:e0241503. [PMID: 33170893 PMCID: PMC7654790 DOI: 10.1371/journal.pone.0241503] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 10/15/2020] [Indexed: 11/23/2022] Open
Abstract
To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.
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Affiliation(s)
- Ray Sajulga
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Caleb Easterly
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Michael Riffle
- University of Washington, Seattle, Washington, United States of America
| | | | - Thilo Muth
- Federal Institute for Materials Research and Testing, Berlin, Germany
| | - Subina Mehta
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Praveen Kumar
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - James Johnson
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | | | | | | | | | - Brook L. Nunn
- University of Washington, Seattle, Washington, United States of America
| | - Joel Rudney
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Timothy J. Griffin
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Pratik D. Jagtap
- University of Minnesota, Minneapolis, Minnesota, United States of America
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Bhargava M, Viken KJ, Barkes B, Griffin TJ, Gillespie M, Jagtap PD, Sajulga R, Peterson EJ, Dincer HE, Li L, Restrepo CI, O'Connor BP, Fingerlin TE, Perlman DM, Maier LA. Novel protein pathways in development and progression of pulmonary sarcoidosis. Sci Rep 2020; 10:13282. [PMID: 32764642 PMCID: PMC7413390 DOI: 10.1038/s41598-020-69281-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/17/2020] [Indexed: 12/15/2022] Open
Abstract
Pulmonary involvement occurs in up to 95% of sarcoidosis cases. In this pilot study, we examine lung compartment-specific protein expression to identify pathways linked to development and progression of pulmonary sarcoidosis. We characterized bronchoalveolar lavage (BAL) cells and fluid (BALF) proteins in recently diagnosed sarcoidosis cases. We identified 4,306 proteins in BAL cells, of which 272 proteins were differentially expressed in sarcoidosis compared to controls. These proteins map to novel pathways such as integrin-linked kinase and IL-8 signaling and previously implicated pathways in sarcoidosis, including phagosome maturation, clathrin-mediated endocytic signaling and redox balance. In the BALF, the differentially expressed proteins map to several pathways identified in the BAL cells. The differentially expressed BALF proteins also map to aryl hydrocarbon signaling, communication between innate and adaptive immune response, integrin, PTEN and phospholipase C signaling, serotonin and tryptophan metabolism, autophagy, and B cell receptor signaling. Additional pathways that were different between progressive and non-progressive sarcoidosis in the BALF included CD28 signaling and PFKFB4 signaling. Our studies demonstrate the power of contemporary proteomics to reveal novel mechanisms operational in sarcoidosis. Application of our workflows in well-phenotyped large cohorts maybe beneficial to identify biomarkers for diagnosis and prognosis and therapeutically tenable molecular mechanisms.
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Affiliation(s)
- Maneesh Bhargava
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Minnesota, MMC 276, 420 Delaware St SE, Minneapolis, MN, USA.
| | - K J Viken
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Minnesota, MMC 276, 420 Delaware St SE, Minneapolis, MN, USA
| | - B Barkes
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, CO, USA
| | - T J Griffin
- Biochemistry, Molecular Biology and Biophysics, College of Biological Sciences, University of Minnesota, Minneapolis, MN, USA
| | - M Gillespie
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, CO, USA
| | - P D Jagtap
- Biochemistry, Molecular Biology and Biophysics, College of Biological Sciences, University of Minnesota, Minneapolis, MN, USA
| | - R Sajulga
- Biochemistry, Molecular Biology and Biophysics, College of Biological Sciences, University of Minnesota, Minneapolis, MN, USA
| | - E J Peterson
- Center for Immunology, University of Minnesota, Minneapolis, MN, USA
| | - H E Dincer
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Minnesota, MMC 276, 420 Delaware St SE, Minneapolis, MN, USA
| | - L Li
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, CO, USA
| | - C I Restrepo
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, CO, USA
| | - B P O'Connor
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - T E Fingerlin
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - D M Perlman
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Minnesota, MMC 276, 420 Delaware St SE, Minneapolis, MN, USA
| | - L A Maier
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, CO, USA
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Kumar P, Johnson JE, Easterly C, Mehta S, Sajulga R, Nunn B, Jagtap PD, Griffin TJ. A Sectioning and Database Enrichment Approach for Improved Peptide Spectrum Matching in Large, Genome-Guided Protein Sequence Databases. J Proteome Res 2020; 19:2772-2785. [DOI: 10.1021/acs.jproteome.0c00260] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Praveen Kumar
- Bioinformatics and Computational Biology, University of Minnesota−Rochester, Rochester, Minnesota 55904, United States
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Caleb Easterly
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Subina Mehta
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ray Sajulga
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Brook Nunn
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Pratik D. Jagtap
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Timothy J. Griffin
- Biochemistry Molecular Biology and Biophysics, University of Minnesota−Twin Cities, Minneapolis, Minnesota 55455, United States
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McGowan T, Johnson JE, Kumar P, Sajulga R, Mehta S, Jagtap PD, Griffin TJ. Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration. Gigascience 2020; 9:giaa025. [PMID: 32236523 PMCID: PMC7102281 DOI: 10.1093/gigascience/giaa025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/13/2020] [Accepted: 02/24/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Proteogenomics integrates genomics, transcriptomics, and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing, and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FINDINGS MVP is built as an HTML Galaxy plug-in, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input-a custom data type (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. CONCLUSIONS MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.
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Affiliation(s)
- Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, 599 Walter Library, 117 Pleasant Street SE, Minneapolis, MN 55455, USA
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, 599 Walter Library, 117 Pleasant Street SE, Minneapolis, MN 55455, USA
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
- Bioinformatics and Computational Biology program, University of Minnesota-Rochester, 111 South Broadway, Suite 300, Rochester, MN 55904, USA
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6–155 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455, USA
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Xavier JB, Young VB, Skufca J, Ginty F, Testerman T, Pearson AT, Macklin P, Mitchell A, Shmulevich I, Xie L, Caporaso JG, Crandall KA, Simone NL, Godoy-Vitorino F, Griffin TJ, Whiteson KL, Gustafson HH, Slade DJ, Schmidt TM, Walther-Antonio MRS, Korem T, Webb-Robertson BJM, Styczynski MP, Johnson WE, Jobin C, Ridlon JM, Koh AY, Yu M, Kelly L, Wargo JA. The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View. Trends Cancer 2020; 6:192-204. [PMID: 32101723 PMCID: PMC7098063 DOI: 10.1016/j.trecan.2020.01.004] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/29/2019] [Accepted: 01/06/2020] [Indexed: 02/06/2023]
Abstract
The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
| | - Vincent B Young
- Department of Internal Medicine, Division of Infectious Diseases, The University of Michigan Medical School, Ann Arbor, MI, USA
| | - Joseph Skufca
- Department of Mathematics, Clarkson University, Potsdam, NY, USA
| | | | - Traci Testerman
- Department of Pathology, Microbiology, and Immunology, University of South Carolina School of Medicine, Columbia, SC, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, Comprehensive Cancer Center, University of Chicago, Chicago, Illinois, IL, USA
| | - Paul Macklin
- Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Amir Mitchell
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Lei Xie
- Hunter College, Department of Computer Science, New York, NY, USA
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Nicole L Simone
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Filipa Godoy-Vitorino
- Department of Microbiology and Medical Zoology, School of Medicine, University of Puerto Rico, San Juan, Puerto Rico
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Katrine L Whiteson
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA, USA
| | - Heather H Gustafson
- Seattle Children's Research Institute, Ben Towne Center for Childhood Cancer Research, Seattle, WA, USA
| | - Daniel J Slade
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | | | - Marina R S Walther-Antonio
- Department of Surgery, Department of Obstetrics and Gynecology, and Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Tal Korem
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Mark P Styczynski
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - W Evan Johnson
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Christian Jobin
- Departments of Medicine, Anatomy, and Cell Biology, and of Infectious Diseases and Immunology, University of Florida, Gainesville, FL, USA
| | - Jason M Ridlon
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Andrew Y Koh
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Yu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | | | - Jennifer A Wargo
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Hubler SL, Kumar P, Mehta S, Easterly C, Johnson JE, Jagtap PD, Griffin TJ. Challenges in Peptide-Spectrum Matching: A Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits. J Proteome Res 2019; 19:161-173. [DOI: 10.1021/acs.jproteome.9b00478] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Boylan KL, Rogers AC, Geller MA, Argenta PA, Griffin TJ, Skubitz AP. Abstract DP-002: COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT. Clin Cancer Res 2019. [DOI: 10.1158/1557-3265.ovcasymp18-dp-002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Early detection is the key to increased survival for women with ovarian cancer, yet a screening tool has yet to be developed that is adequately sensitive and specific enough for use in the general population. In contrast, screening for cervical cancer by Pap tests has been routinely performed for over 50 years. In the liquid-based Pap test, cells are collected from the cervix and placed into an alcohol-based fixative and then examined for abnormal cells. Since ovarian cancer cells have been observed in Pap tests, we reasoned that ovarian cancer peptide biomarkers may also be present. Our central hypothesis is that proteins shed by ovarian cancer cells can be detected during routine Pap tests by mass spectrometry (MS)-based proteomics. In particular, when collected at the time of cervical cancer Pap test screening, the alcohol-based Pap test fixative and cervical swabs are ideal for biomarker discovery since they are derived from a site near the ovarian cancer (i.e. proteins may be secreted or shed from the tumor and flow through the fallopian tube into the uterus and out the cervical opening). Recently, the fimbria of the fallopian tube have been suggested to be the true precursor to ovarian cancer, strengthening our hypothesis that ovarian cancer proteins will be found in the lower genital tract, perhaps even at early stages. To demonstrate the feasibility of using Pap tests as a biospecimen for proteomics, we previously examined the proteins present in residual Pap test fixative samples from women with normal cervical cytology by MS and described 152 proteins in the “Normal Pap Proteome.” The objective of this study was to identify and compare the proteins from three different sources from the same ovarian cancer patient: (i) the residual Pap test fixative, (ii) a Merocel swab of the cervix, and (iii) the primary ovarian cancer tumor tissue. Proteins were concentrated from the cell-free supernatant of the Pap test fixative or eluted from the swab, and then trypsin digested using the filter-aided sample preparation method. A total protein extract from the patient's tumor tissue was digested by standard in-solution trypsin digestion. The samples were run on 2D-liquid chromatography MS/MS, followed by bioinformatics integration. We identified over 5000 proteins total in the three samples. More than 2000 proteins were expressed in all three ovarian cancer samples, including several known ovarian cancer biomarkers such as CA125. By Scaffold analysis of the Gene Ontology nomenclature of the proteins, we classified the proteins by both cellular localization and biological processes. Additional matched samples from patients will be used to build a library of proteins and peptides that are specific to ovarian cancer for use in the development of targeted MS assays. We conclude that quantification of proteins from Pap test fixatives and cervical swabs will prove to be a rich source of biomarkers for ovarian cancer detection.
Citation Format: Kristin L.M. Boylan, Anna C. Rogers, Melissa A. Geller, Peter A. Argenta, Timothy J. Griffin, and Amy P.N. Skubitz. COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr DP-002.
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Affiliation(s)
| | | | | | | | - Timothy J. Griffin
- 3Department of Biochemistry, Molecular Biology, & Biophysics, University of Minnesota Medical School, Minneapolis, MN
| | - Amy P.N. Skubitz
- 1Department of Laboratory Medicine & Pathology
- 2Department of Obstetrics, Gynecology, & Women's Health; and
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Easterly CW, Sajulga R, Mehta S, Johnson J, Kumar P, Hubler S, Mesuere B, Rudney J, Griffin TJ, Jagtap PD. metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes. Mol Cell Proteomics 2019; 18:S82-S91. [PMID: 31235611 PMCID: PMC6692774 DOI: 10.1074/mcp.ra118.001240] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 06/21/2019] [Indexed: 01/15/2023] Open
Abstract
Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.
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Affiliation(s)
- Caleb W Easterly
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN
| | - Ray Sajulga
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN
| | - Subina Mehta
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN
| | - Praveen Kumar
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN; Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN
| | - Shane Hubler
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN
| | - Bart Mesuere
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Joel Rudney
- ‡School of Dentistry, University of Minnesota, Minneapolis, MN
| | - Timothy J Griffin
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN
| | - Pratik D Jagtap
- Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN.
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Sandri BJ, Masvidal L, Murie C, Bartish M, Avdulov S, Higgins L, Markowski T, Peterson M, Bergh J, Yang P, Rolny C, Limper AH, Griffin TJ, Bitterman PB, Wendt CH, Larsson O. Distinct Cancer-Promoting Stromal Gene Expression Depending on Lung Function. Am J Respir Crit Care Med 2019; 200:348-358. [PMID: 30742544 PMCID: PMC6680296 DOI: 10.1164/rccm.201801-0080oc] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 02/08/2019] [Indexed: 12/31/2022] Open
Abstract
Rationale: Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis.Objectives: To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer.Methods: The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome.Measurements and Main Results: Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction).Conclusions: Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.
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Affiliation(s)
- Brian J. Sandri
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Laia Masvidal
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Carl Murie
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Margarita Bartish
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Svetlana Avdulov
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Todd Markowski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Mark Peterson
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Jonas Bergh
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | | | - Charlotte Rolny
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Andrew H. Limper
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota; and
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Peter B. Bitterman
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Chris H. Wendt
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
- Pulmonary, Allergy, Critical Care, and Sleep Medicine, Veterans Affairs Medical Center, Minneapolis, Minnesota
| | - Ola Larsson
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
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Kumar P, Panigrahi P, Johnson J, Weber WJ, Mehta S, Sajulga R, Easterly C, Crooker BA, Heydarian M, Anamika K, Griffin TJ, Jagtap PD. QuanTP: A Software Resource for Quantitative Proteo-Transcriptomic Comparative Data Analysis and Informatics. J Proteome Res 2018; 18:782-790. [DOI: 10.1021/acs.jproteome.8b00727] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Praveen Kumar
- Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, Minnesota 55904, United States
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | | | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Wanda J. Weber
- Department of Animal Science, University of Minnesota, St. Paul, Minnesota 55108, United States
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Brian A. Crooker
- Department of Animal Science, University of Minnesota, St. Paul, Minnesota 55108, United States
| | - Mohammad Heydarian
- Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Krishanpal Anamika
- LABS, Persistent Systems, Aryabhata-Pingala, Erandwane, Pune 411004, India
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
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36
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Argentini A, Staes A, Grüning B, Mehta S, Easterly C, Griffin TJ, Jagtap P, Impens F, Martens L. Update on the moFF Algorithm for Label-Free Quantitative Proteomics. J Proteome Res 2018; 18:728-731. [DOI: 10.1021/acs.jproteome.8b00708] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrea Argentini
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - An Staes
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Baden-Württemberg 79110, Germany
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Pratik Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Francis Impens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
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37
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Jagtap PD, Viken KJ, Johnson J, McGowan T, Pendleton KM, Griffin TJ, Hunter RC, Rudney JD, Bhargava M. BAL Fluid Metaproteome in Acute Respiratory Failure. Am J Respir Cell Mol Biol 2018; 59:648-652. [PMID: 30382775 PMCID: PMC6236685 DOI: 10.1165/rcmb.2018-0068le] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
| | - Kevin J. Viken
- University of Minnesota Medical SchoolMinneapolis, Minnesota
| | - James Johnson
- University of Minnesota Supercomputing InstituteMinneapolis, Minnesotaand
| | - Thomas McGowan
- University of Minnesota Supercomputing InstituteMinneapolis, Minnesotaand
| | | | | | - Ryan C. Hunter
- University of Minnesota Medical SchoolMinneapolis, Minnesota
| | - Joel D. Rudney
- University of Minnesota School of DentistryMinneapolis, Minnesota
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38
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Abstract
Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.
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Affiliation(s)
- James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA.,Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, MN, 55904, USA
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Mark Esler
- Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Arthur C Eschenlauer
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA.,Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Adrian D Hegeman
- Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
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39
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Johnson JE, Kumar P, Easterly C, Esler M, Mehta S, Eschenlauer AC, Hegeman AD, Jagtap PD, Griffin TJ. Improve your Galaxy text life: The Query Tabular Tool. F1000Res 2018; 7:1604. [PMID: 30519459 PMCID: PMC6248266 DOI: 10.12688/f1000research.16450.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/02/2019] [Indexed: 10/04/2023] Open
Abstract
Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.
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Affiliation(s)
- James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
- Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, MN, 55904, USA
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Mark Esler
- Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Arthur C. Eschenlauer
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
- Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Adrian D. Hegeman
- Department of Horticulture, University of Minnesota, St. Paul, MN, 55108, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, 55455, USA
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40
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Li D, Huang F, Zhao Y, Villata PW, Griffin TJ, Zhang L, Li L, Yu F. Plasma lipoproteome in Alzheimer's disease: a proof-of-concept study. Clin Proteomics 2018; 15:31. [PMID: 30250409 PMCID: PMC6147047 DOI: 10.1186/s12014-018-9207-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 09/15/2018] [Indexed: 12/11/2022] Open
Abstract
Background Although total plasma lipoproteome consists of proteins that have shown promises as biomarkers that can identify Alzheimer's disease (AD), effect sizes are modest. The objective of this study is to provide initial proof-of-concept that the plasma lipoproteome more likely differ between AD cases and controls when measured in individual plasma lipoprotein fractions than when measured as total in immunodepleted plasma. Methods We first developed a targeted proteomics method based on selected reaction monitoring (SRM) and liquid chromatography and tandem mass spectrometry for measurement of 120 tryptic peptides from 79 proteins that are commonly present in plasma lipoproteins. Then in a proof-of concept case-control study of 5 AD cases and 5 sex- and age-matched controls, we applied the targeted proteomic method and performed relatively quantification of 120 tryptic peptides in plasma lipoprotein fractions (fractionated by sequential gradient ultracentrifugation) and in immunodepleted plasma (of albumin and IgG). Unadjusted p values from two-sample t-tests and overall fold change was used to evaluate a peptide relative difference between AD cases and controls, with lower p values (< 0.05) or greater fold differences (> 1.05 or < 0.95) suggestive of greater peptide/protein differences. Results Within-day and between-days technical precisions (mean %CV [SD] of all SRM transitions) of the targeted proteomic method were 3.95% (2.65) and 9.31% (5.59), respectively. Between-days technical precisions (mean % CV [SD]) of the entire plasma lipoproteomic workflow including plasma lipoprotein fractionation was 27.90% (14.61). Ten tryptic peptides that belonged to 5 proteins in plasma lipoproteins had unadjusted p values < 0.05, compared to no peptides in immunodepleted plasma. Furthermore, 27, 32, 17, and 20 tryptic peptides in VLDL, IDL, LDL and HDL, demonstrated overall peptide fold differences > 1.05 or < 0.95, compared to only 6 tryptic peptides in immunodepleted plasma. The overall comparisons, therefore, suggested greater peptide/protein differences in plasma lipoproteome when measured in individual plasma lipoproteins than as total in immunodepleted plasma. Specifically, protein complement C3's peptide IHWESASLLR, had unadjusted p values of 0.00007, 0.00012, and 0.0006 and overall 1.25, 1.17, 1.14-fold changes in VLDL, IDL, and LDL, respectively. After positive False Discovery Rate (pFDR) adjustment, the complement C3 peptide IHWESASLLR in VLDL remained statistically different (adjusted p value < 0.05). Discussion The findings may warrant future studies to investigate plasma lipoproteome when measured in individual plasma lipoprotein fractions for AD diagnosis.
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Affiliation(s)
- Danni Li
- 1Department of Laboratory Medicine and Pathology, University of Minnesota, 420 Delaware Street SE, MMC 609, Minneapolis, MN 55455 USA
| | - Fangying Huang
- 1Department of Laboratory Medicine and Pathology, University of Minnesota, 420 Delaware Street SE, MMC 609, Minneapolis, MN 55455 USA
| | - Yingchun Zhao
- 2Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455 USA
| | - Peter W Villata
- 2Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455 USA
| | - Timothy J Griffin
- 3Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455 USA
| | - Lin Zhang
- 4Department of Biostatistics, University of Minnesota, Minneapolis, MN 55455 USA
| | - Ling Li
- 5Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455 USA
| | - Fang Yu
- 6School of Nursing, University of Minnesota, Minneapolis, MN 55455 USA
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41
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Folsom TD, Higgins L, Markowski TW, Griffin TJ, Fatemi SH. Quantitative proteomics of forebrain subcellular fractions in fragile X mental retardation 1 knockout mice following acute treatment with 2-Methyl-6-(phenylethynyl)pyridine: Relevance to developmental study of schizophrenia. Synapse 2018; 73:e22069. [PMID: 30176067 DOI: 10.1002/syn.22069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 08/13/2018] [Accepted: 08/30/2018] [Indexed: 12/22/2022]
Abstract
The fragile X mental retardation 1 knockout (Fmr1 KO) mouse replicates behavioral deficits associated with autism, fragile X syndrome, and schizophrenia. Less is known whether protein expression changes are consistent with findings in subjects with schizophrenia. In the current study, we used liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics to determine the protein expression of four subcellular fractions in the forebrains of Fmr1 KO mice vs. C57BL/6 J mice and the effect of a negative allosteric modulator of mGluR5-2-Methyl-6-(phenylethynyl)pyridine (MPEP)-on protein expression. Strain- and treatment-specific differential expression of proteins was observed, many of which have previously been observed in the brains of subjects with schizophrenia. Western blotting verified the direction and magnitude of change for several proteins in different subcellular fractions as follows: neurofilament light protein (NEFL) and 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNP) in the total homogenate; heterogeneous nuclear ribonucleoproteins C1/C2 (HNRNPC) and heterogeneous nuclear ribonucleoprotein D0 (HNRNPD) in the nuclear fraction; excitatory amino acid transporter 2 (EAAT2) and ras-related protein rab 3a (RAB3A) in the synaptic fraction; and ras-related protein rab 35 (RAB35) and neuromodulin (GAP43) in the rough endoplasmic reticulum fraction. Individuals with FXS do not display symptoms of schizophrenia. However, the biomarkers that have been identified suggest that the Fmr1 KO model could potentially be useful in the study of schizophrenia.
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Affiliation(s)
- Timothy D Folsom
- Department of Psychiatry, Division of Neuroscience Research, University of Minnesota Medical School, Minneapolis, Minnesota
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Todd W Markowski
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Medical School, Minneapolis, Minnesota
| | - S Hossein Fatemi
- Department of Psychiatry, Division of Neuroscience Research, University of Minnesota Medical School, Minneapolis, Minnesota.,Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota
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42
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Sajulga R, Mehta S, Kumar P, Johnson JE, Guerrero CR, Ryan MC, Karchin R, Jagtap PD, Griffin TJ. Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics. J Proteome Res 2018; 17:4329-4336. [DOI: 10.1021/acs.jproteome.8b00404] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Ray Sajulga
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
- Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, Minnesota 55904, United States
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Candace R. Guerrero
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Michael C. Ryan
- In-Silico Solutions, Falls Church, Virginia 22043, United States
| | - Rachel Karchin
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, United States
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21217, United States
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, United States
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43
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Afiuni-Zadeh S, Boylan KLM, Jagtap PD, Griffin TJ, Rudney JD, Peterson ML, Skubitz APN. Evaluating the potential of residual Pap test fluid as a resource for the metaproteomic analysis of the cervical-vaginal microbiome. Sci Rep 2018; 8:10868. [PMID: 30022083 PMCID: PMC6052116 DOI: 10.1038/s41598-018-29092-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 07/04/2018] [Indexed: 01/30/2023] Open
Abstract
The human cervical-vaginal area contains proteins derived from microorganisms that may prevent or predispose women to gynecological conditions. The liquid Pap test fixative is an unexplored resource for analysis of microbial communities and the microbe-host interaction. Previously, we showed that the residual cell-free fixative from discarded Pap tests of healthy women could be used for mass spectrometry (MS) based proteomic identification of cervical-vaginal proteins. In this study, we reprocessed these MS raw data files for metaproteomic analysis to characterize the microbial community composition and function of microbial proteins in the cervical-vaginal region. This was accomplished by developing a customized protein sequence database encompassing microbes likely present in the vagina. High-mass accuracy data were searched against the protein FASTA database using a two-step search method within the Galaxy for proteomics platform. Data was analyzed by MEGAN6 (MetaGenomeAnalyzer) for phylogenetic and functional characterization. We identified over 300 unique peptides from a variety of bacterial phyla and Candida. Peptides corresponding to proteins involved in carbohydrate metabolism, oxidation-reduction, and transport were identified. By identifying microbial peptides in Pap test supernatants it may be possible to acquire a functional signature of these microbes, as well as detect specific proteins associated with cervical health and disease.
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Affiliation(s)
- Somaieh Afiuni-Zadeh
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kristin L M Boylan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Center for Mass Spectrometry and Proteomics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
- Center for Mass Spectrometry and Proteomics, University of Minnesota, Minneapolis, MN, USA
| | - Joel D Rudney
- Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, MN, USA
| | | | - Amy P N Skubitz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
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44
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Sandri BJ, Kaplan A, Hodgson SW, Peterson M, Avdulov S, Higgins L, Markowski T, Yang P, Limper AH, Griffin TJ, Bitterman P, Lock EF, Wendt CH. Multi-omic molecular profiling of lung cancer in COPD. Eur Respir J 2018; 52:13993003.02665-2017. [PMID: 29794131 DOI: 10.1183/13993003.02665-2017] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 05/06/2018] [Indexed: 12/14/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesise that the COPD stroma contains molecular mechanisms supporting tumourigenesis.We conducted an unbiased multi-omic analysis to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumour and adjacent non-malignant tissue) and those with COPD without lung cancer for profiling of proteomic and mRNA (both cytoplasmic and polyribosomal). We used the Joint and Individual Variation Explained (JIVE) method to integrate and analyse across the three datasets.JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, adjacent and control). Predictive variables that associated with the tumour, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas predictive variables associated with adjacent tissue, compared to controls, were represented at the translatomic level. Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase-protein kinase B signalling pathways as important signals in the tumour adjacent stroma.The multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.
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Affiliation(s)
- Brian J Sandri
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Dept of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA.,Both authors contributed equally
| | - Adam Kaplan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.,Both authors contributed equally
| | - Shane W Hodgson
- Pulmonary, Allergy, Critical Care, and Sleep Medicine, Veterans Affairs Medical Center, Minneapolis, MN, USA
| | - Mark Peterson
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Dept of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Svetlana Avdulov
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Dept of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - LeeAnn Higgins
- Dept of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Todd Markowski
- Dept of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Ping Yang
- Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Andrew H Limper
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Timothy J Griffin
- Dept of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Peter Bitterman
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Dept of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Eric F Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Chris H Wendt
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Dept of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA.,Pulmonary, Allergy, Critical Care, and Sleep Medicine, Veterans Affairs Medical Center, Minneapolis, MN, USA
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45
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Park JM, Seo M, Jung CH, Grunwald D, Stone M, Otto NM, Toso E, Ahn Y, Kyba M, Griffin TJ, Higgins L, Kim DH. ULK1 phosphorylates Ser30 of BECN1 in association with ATG14 to stimulate autophagy induction. Autophagy 2018; 14:584-597. [PMID: 29313410 DOI: 10.1080/15548627.2017.1422851] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
ULK1 (unc51-like autophagy activating kinase 1) is a serine/threonine kinase that plays a key role in regulating macroautophagy/autophagy induction in response to amino acid starvation. Despite the recent progress in understanding ULK1 functions, the molecular mechanism by which ULK1 regulates the induction of autophagy remains elusive. In this study, we determined that ULK1 phosphorylates Ser30 of BECN1 (Beclin 1) in association with ATG14 (autophagy-related 14) but not with UVRAG (UV radiation resistance associated). The Ser30 phosphorylation was induced by deprivation of amino acids or treatments with Torin 1 or rapamycin, the conditions that inhibit MTORC1 (mechanistic target of rapamycin complex 1), and requires ATG13 and RB1CC1 (RB1 inducible coiled-coil 1), proteins that interact with ULK1. Hypoxia or glutamine deprivation, which inhibit MTORC1, was also able to increase the phosphorylation in a manner dependent upon ULK1 and ULK2. Blocking the BECN1 phosphorylation by replacing Ser30 with alanine suppressed the amino acid starvation-induced activation of the ATG14-containing PIK3C3/VPS34 (phosphatidylinositol 3-kinase catalytic subunit type 3) kinase, and reduced autophagy flux and the formation of phagophores and autophagosomes. The Ser30-to-Ala mutation did not affect the ULK1-mediated phosphorylations of BECN1 Ser15 or ATG14 Ser29, indicating that the BECN1 Ser30 phosphorylation might regulate autophagy independently of those 2 sites. Taken together, these results demonstrate that BECN1 Ser30 is a ULK1 target site whose phosphorylation activates the ATG14-containing PIK3C3 complex and stimulates autophagosome formation in response to amino acid starvation, hypoxia, and MTORC1 inhibition.
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Affiliation(s)
- Ji-Man Park
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
| | - Minchul Seo
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
| | - Chang Hwa Jung
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA.,b Division of Metabolism and Functionality Research , Korea Food Research Institute , Korea
| | - Douglas Grunwald
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
| | - Matthew Stone
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA.,d Center for Mass Spectrometry and Proteomics , University of Minnesota , Minneapolis , MN , USA
| | - Neil Michael Otto
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
| | - Erik Toso
- c Department of Pediatrics , University of Minnesota , Minneapolis , MN , USA
| | - Yeseul Ahn
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
| | - Michael Kyba
- c Department of Pediatrics , University of Minnesota , Minneapolis , MN , USA
| | - Timothy J Griffin
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA.,d Center for Mass Spectrometry and Proteomics , University of Minnesota , Minneapolis , MN , USA
| | - LeeAnn Higgins
- d Center for Mass Spectrometry and Proteomics , University of Minnesota , Minneapolis , MN , USA
| | - Do-Hyung Kim
- a Department of Biochemistry , Molecular Biology, and Biophysics , University of Minnesota , Minneapolis, MN , USA
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46
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Blank C, Easterly C, Gruening B, Johnson J, Kolmeder CA, Kumar P, May D, Mehta S, Mesuere B, Brown Z, Elias JE, Hervey WJ, McGowan T, Muth T, Nunn B, Rudney J, Tanca A, Griffin TJ, Jagtap PD. Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes 2018; 6:proteomes6010007. [PMID: 29385081 PMCID: PMC5874766 DOI: 10.3390/proteomes6010007] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 01/26/2018] [Accepted: 01/26/2018] [Indexed: 01/12/2023] Open
Abstract
The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics "Contribution Fest" undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.
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Affiliation(s)
- Clemens Blank
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany.
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg im Breisgau, Germany.
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Carolin A Kolmeder
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland.
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Damon May
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Bart Mesuere
- Computational Biology Group, Ghent University, Krijgslaan 281, B-9000 Ghent, Belgium.
| | - Zachary Brown
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Joshua E Elias
- Department of Chemical & Systems Biology, Stanford University, Stanford, CA 94305, USA.
| | - W Judson Hervey
- Center for Bio/Molecular Science & Engineering, Naval Research Laboratory, Washington, DC 20375, USA.
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.
| | - Brook Nunn
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Joel Rudney
- Department of Diagnostic and Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Alessandro Tanca
- Porto Conte Ricerche Science and Technology Park of Sardinia, 07041 Alghero, Italy.
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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47
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Torres SMF, Furrow E, Souza CP, Granick JL, de Jong EP, Griffin TJ, Wang X. Salivary proteomics of healthy dogs: An in depth catalog. PLoS One 2018; 13:e0191307. [PMID: 29329347 PMCID: PMC5766244 DOI: 10.1371/journal.pone.0191307] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 01/02/2018] [Indexed: 12/19/2022] Open
Abstract
Objective To provide an in-depth catalog of the salivary proteome and endogenous peptidome of healthy dogs, evaluate proteins and peptides with antimicrobial properties, and compare the most common salivary proteins and peptides between different breed phylogeny groups. Methods 36 healthy dogs without evidence of periodontal disease representing four breed phylogeny groups, based upon single nucleotide polymorphism haplotypes (ancient, herding/sighthound, and two miscellaneous groups). Saliva collected from dogs was pooled by phylogeny group and analyzed using nanoscale liquid chromatography-tandem mass spectrometry. Resulting tandem mass spectra were compared to databases for identification of endogenous peptides and inferred proteins. Results 2,491 proteins and endogenous peptides were found in the saliva of healthy dogs with no periodontal disease. All dog phylogeny groups’ saliva was rich in proteins and peptides with antimicrobial functions. The ancient breeds group was distinct in that it contained unique proteins and was missing many proteins and peptides present in the other groups. Conclusions and clinical relevance Using a sophisticated nanoscale liquid chromatography-tandem mass spectrometry, we were able to identify 10-fold more salivary proteins than previously reported in dogs. Seven of the top 10 most abundant proteins or peptides serve immune functions and many more with various antimicrobial mechanisms were found. This is the most comprehensive analysis of healthy canine saliva to date, and will provide the groundwork for future studies analyzing salivary proteins and endogenous peptides in disease states.
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Affiliation(s)
- Sheila M. F. Torres
- Veterinary Clinical Sciences Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- * E-mail:
| | - Eva Furrow
- Veterinary Clinical Sciences Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Clarissa P. Souza
- Veterinary Clinical Sciences Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
- Clinical Sciences Department, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, United States of America
| | - Jennifer L. Granick
- Veterinary Clinical Sciences Department, College of Veterinary Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Ebbing P. de Jong
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, United States of America
- Department of Biochemistry and Molecular Biochemistry, SUNY Upstate Medical University, Syracuse, New York, United States of America
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Xiong Wang
- Department of Veterinary Biomedical Sciences, University of Minnesota, Saint Paul, Minnesota, United States of America
- Minnesota Department of Health, Saint Paul, Minnesota, United States of America
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48
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Murie C, Sandri B, Sandberg AS, Griffin TJ, Lehtiö J, Wendt C, Larsson O. Normalization of mass spectrometry data (NOMAD). Adv Biol Regul 2018; 67:128-133. [PMID: 29174395 PMCID: PMC5885284 DOI: 10.1016/j.jbior.2017.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 04/13/2023]
Abstract
iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.
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Affiliation(s)
- Carl Murie
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Brian Sandri
- Division of Pulmonary and Critical Care Medicine and VAMC, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Ann-Sofi Sandberg
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN, USA; Center for Mass Spectrometry and Proteomics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Janne Lehtiö
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Christine Wendt
- Division of Pulmonary and Critical Care Medicine and VAMC, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
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49
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Chambers MC, Jagtap PD, Johnson JE, McGowan T, Kumar P, Onsongo G, Guerrero CR, Barsnes H, Vaudel M, Martens L, Grüning B, Cooke IR, Heydarian M, Reddy KL, Griffin TJ. An Accessible Proteogenomics Informatics Resource for Cancer Researchers. Cancer Res 2017; 77:e43-e46. [PMID: 29092937 DOI: 10.1158/0008-5472.can-17-0331] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 04/07/2017] [Accepted: 06/30/2017] [Indexed: 11/16/2022]
Abstract
Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.
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Affiliation(s)
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota.,Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, Minnesota
| | - Getiria Onsongo
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Candace R Guerrero
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.,Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Björn Grüning
- Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany.,Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany
| | - Ira R Cooke
- Comparative Genomics Centre and Department of Molecular and Cell Biology, James Cook University, Queensland, Australia
| | | | - Karen L Reddy
- Department of Biological Chemistry, Center for Epigenetics and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota.
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50
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Pett SL, Kunisaki KM, Wentworth D, Griffin TJ, Kalomenidis I, Nahra R, Montejano Sanchez R, Hodgson SW, Ruxrungtham K, Dwyer D, Davey RT, Wendt CH. Increased Indoleamine-2,3-Dioxygenase Activity Is Associated With Poor Clinical Outcome in Adults Hospitalized With Influenza in the INSIGHT FLU003Plus Study. Open Forum Infect Dis 2017; 5:ofx228. [PMID: 29322062 PMCID: PMC5753217 DOI: 10.1093/ofid/ofx228] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 10/24/2017] [Indexed: 02/01/2023] Open
Abstract
Background Indoleamine-2,3-dioxygenase (IDO) mediated tryptophan (TRP) depletion has antimicrobial and immuno-regulatory effects. Increased kynurenine (KYN)-to-TRP (KT) ratios, reflecting increased IDO activity, have been associated with poorer outcomes from several infections. Methods We performed a case-control (1:2; age and sex matched) analysis of adults hospitalized with influenza A(H1N1)pdm09 with protocol-defined disease progression (died/transferred to ICU/mechanical ventilation) after enrollment (cases) or survived without progression (controls) over 60 days of follow-up. Conditional logistic regression was used to analyze the relationship between baseline KT ratio and other metabolites and disease progression. Results We included 32 cases and 64 controls with a median age of 52 years; 41% were female, and the median durations of influenza symptoms prior to hospitalization were 8 and 6 days for cases and controls, respectively (P = .04). Median baseline KT ratios were 2-fold higher in cases (0.24 mM/M; IQR, 0.13-0.40) than controls (0.12; IQR, 0.09-0.17; P ≤ .001). When divided into tertiles, 59% of cases vs 20% of controls had KT ratios in the highest tertile (0.21-0.84 mM/M). When adjusted for symptom duration, the odds ratio for disease progression for those in the highest vs lowest tertiles of KT ratio was 9.94 (95% CI, 2.25-43.90). Conclusions High KT ratio was associated with poor outcome in adults hospitalized with influenza A(H1N1)pdm09. The clinical utility of this biomarker in this setting merits further exploration. ClinicalTrialsgov Identifier NCT01056185.
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Affiliation(s)
- Sarah L Pett
- Medical Research Council Clinical Trials Unit (MRC CTU), Institute of Clinical Trials and Methodology, University College London, UK.,Clinical Research Group, Infections and Population Health, UCL, London, UK.,Kirby Institute, University of New South Wales, Kensington, Australia
| | - Ken M Kunisaki
- Minneapolis VA Health Care System, Minneapolis, Minnesota.,Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Deborah Wentworth
- Division of Biostatistics, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Ioannis Kalomenidis
- 1st Department of Critical Care and Pulmonary Medicine, University of Athens School of Medicine, Evangelismos General Hospital, Athens, Greece
| | - Raquel Nahra
- Cooper University Hospital, Division of Infectious Disease, Camden, New Jersey
| | | | | | - Kiat Ruxrungtham
- HIV-NAT, Thai Red Cross AIDS Research Center, Bangkok, Thailand.,Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Dominic Dwyer
- Institute of Clinical Pathology and Medical Research, Pathology West and NSW Health Pathology, Westmead Hospital and University of Sydney, Westmead, Australia
| | - Richard T Davey
- National National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
| | - Chris H Wendt
- Minneapolis VA Health Care System, Minneapolis, Minnesota.,Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota
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