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Odenkirk MT, Zheng X, Kyle JE, Stratton KG, Nicora CD, Bloodsworth KJ, Mclean CA, Masters CL, Monroe ME, Doecke JD, Smith RD, Burnum-Johnson KE, Roberts BR, Baker ES. Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions. J Proteome Res 2024; 23:2970-2985. [PMID: 38236019 PMCID: PMC11255128 DOI: 10.1021/acs.jproteome.3c00604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States of America
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kent J Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Catriona A Mclean
- Anatomical Pathology, Alfred Hospital, Prahran, Victoria 3181, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - James D Doecke
- CSIRO Health and Biosecurity, Herston, Queensland 4029, Australia
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Blaine R Roberts
- Department of Biochemistry, Emory University, Atlanta, Georgia 30322, United States of America
- Department of Neurology, Emory University, Atlanta, Georgia 30322, United States of America
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States of America
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Stratton KG, Claborne DM, Degnan DJ, Richardson RE, White AM, McCue LA, Webb-Robertson BJM, Bramer LM. PMart Web Application: Marketplace for Interactive Analysis of Panomics Data. J Proteome Res 2024; 23:3310-3317. [PMID: 38085827 DOI: 10.1021/acs.jproteome.3c00512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2024]
Abstract
PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.
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Affiliation(s)
- Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Daniel M Claborne
- AI and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Rachel E Richardson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Amanda M White
- AI and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Lee Ann McCue
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
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Beyene HB, Huynh K, Wang T, Paul S, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Giles C, Meikle PJ. Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study. EBioMedicine 2024; 105:105187. [PMID: 38861870 PMCID: PMC11215217 DOI: 10.1016/j.ebiom.2024.105187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle. METHODS We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years). FINDINGS In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score. INTERPRETATION The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings. FUNDING National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Sudip Paul
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- Medical School, University of Western Australia, Perth, WA, Australia; Cardiometabolic Service, Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- Medical School, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Queen Elizabeth II Medical Centre, Nedlands, WA, Australia; School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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4
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Eisfeld AJ, Anderson LN, Fan S, Walters KB, Halfmann PJ, Westhoff Smith D, Thackray LB, Tan Q, Sims AC, Menachery VD, Schäfer A, Sheahan TP, Cockrell AS, Stratton KG, Webb-Robertson BJM, Kyle JE, Burnum-Johnson KE, Kim YM, Nicora CD, Peralta Z, N'jai AU, Sahr F, van Bakel H, Diamond MS, Baric RS, Metz TO, Smith RD, Kawaoka Y, Waters KM. A compendium of multi-omics data illuminating host responses to lethal human virus infections. Sci Data 2024; 11:328. [PMID: 38565538 PMCID: PMC10987564 DOI: 10.1038/s41597-024-03124-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.
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Affiliation(s)
- Amie J Eisfeld
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Lindsey N Anderson
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Shufang Fan
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Coronavirus and Other Respiratory Viruses Laboratory Branch (CRVLB), Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, GA, 30329, USA
| | - Kevin B Walters
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, 21702, USA
| | - Peter J Halfmann
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Danielle Westhoff Smith
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Larissa B Thackray
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Qing Tan
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Amy C Sims
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
- Nuclear, Chemistry, and Biosciences Division; National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Vineet D Menachery
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, 77555, USA
| | - Alexandra Schäfer
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
| | - Timothy P Sheahan
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Adam S Cockrell
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
- Solid Biosciences, Charlston, MA, 02139, USA
| | - Kelly G Stratton
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Kristin E Burnum-Johnson
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Young-Mo Kim
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Carrie D Nicora
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Zuleyma Peralta
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA
- Partillion Bioscience, Los Angeles, CA, 90064, USA
| | - Alhaji U N'jai
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Biological Sciences, Fourah Bay College, Freetown, Sierra Leone
- Department of Microbiology, College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Sierra Leone
- Department of Medical Education, California University of Science and Medicine, Colton, CA, 92324, USA
| | - Foday Sahr
- Department of Microbiology, College of Medicine and Health Sciences, University of Sierra Leone, Freetown, Sierra Leone
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, USA
| | - Michael S Diamond
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Ralph S Baric
- Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, 27599, USA
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Thomas O Metz
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Richard D Smith
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, 108-8639, Tokyo, Japan
- The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, Tokyo, 108-8639, Japan
| | - Katrina M Waters
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
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Dakic A, Wu J, Wang T, Huynh K, Mellett N, Duong T, Beyene HB, Magliano DJ, Shaw JE, Carrington MJ, Inouye M, Yang JY, Figtree GA, Curran JE, Blangero J, Simes J, Giles C, Meikle PJ. Imputation of plasma lipid species to facilitate integration of lipidomic datasets. Nat Commun 2024; 15:1540. [PMID: 38378775 PMCID: PMC10879118 DOI: 10.1038/s41467-024-45838-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024] Open
Abstract
Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.
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Affiliation(s)
- Aleksandar Dakic
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Jingqin Wu
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia
| | - Natalie Mellett
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Thy Duong
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia
| | | | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Melinda J Carrington
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia
| | - Michael Inouye
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
| | - Jean Y Yang
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - Gemma A Figtree
- Kolling Institute of Medical Research, The University of Sydney, St Leonards, NSW, 2065, Australia
- Department of Cardiology, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine at University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - John Simes
- National Health and Medical Research Council of Australia (NHMRC) Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia.
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia.
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia.
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC, 3086, Australia.
- Baker Department of Cardiometabolic Health, The University of Melbourne, VIC, 3010, Australia.
- Department of Diabetes, Central Clinical School, Monash University, Clayton, VIC, 3800, Australia.
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6
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Carvalho LB, Teigas-Campos PAD, Jorge S, Protti M, Mercolini L, Dhir R, Wiśniewski JR, Lodeiro C, Santos HM, Capelo JL. Normalization methods in mass spectrometry-based analytical proteomics: A case study based on renal cell carcinoma datasets. Talanta 2024; 266:124953. [PMID: 37490822 DOI: 10.1016/j.talanta.2023.124953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/07/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023]
Abstract
Normalization is a crucial step in proteomics data analysis as it enables data adjustment and enhances comparability between datasets by minimizing multiple sources of variability, such as sampling, sample handling, storage, treatment, and mass spectrometry measurements. In this study, we investigated different normalization methods, including Z-score normalization, median divide normalization, and quantile normalization, to evaluate their performance using a case study based on renal cell carcinoma datasets. Our results demonstrate that when comparing datasets by pairs, both the Z-score and quantile normalization methods consistently provide better results in terms of the number of proteins identified and quantified as well as in identifying statistically significant up or down-regulated proteins. However, when three or more datasets are compared at the same time the differences are found to be negligible.
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Affiliation(s)
- Luis B Carvalho
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal
| | - Pedro A D Teigas-Campos
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal
| | - Susana Jorge
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal
| | - Michele Protti
- Research Group of Pharmaco-Toxicological Analysis (PTA Lab), Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy
| | - Laura Mercolini
- Research Group of Pharmaco-Toxicological Analysis (PTA Lab), Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy
| | - Rajiv Dhir
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jacek R Wiśniewski
- Biochemical Proteomics Group, Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany
| | - Carlos Lodeiro
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal
| | - Hugo M Santos
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal; Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
| | - José L Capelo
- BIOSCOPE Group, LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, FCT NOVA, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal; PROTEOMASS Scientific Society, Madan Park, 2829-516, Caparica, Portugal.
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7
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Hildebrand GA, Honeker LK, Freire-Zapata V, Ayala-Ortiz C, Rajakaruna S, Fudyma J, Daber LE, AminiTabrizi R, Chu RL, Toyoda J, Flowers SE, Hoyt DW, Hamdan R, Gil-Loaiza J, Shi L, Dippold MA, Ladd SN, Werner C, Meredith LK, Tfaily MM. Uncovering the dominant role of root metabolism in shaping rhizosphere metabolome under drought in tropical rainforest plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165689. [PMID: 37481084 DOI: 10.1016/j.scitotenv.2023.165689] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/29/2023] [Accepted: 07/19/2023] [Indexed: 07/24/2023]
Abstract
Plant-soil-microbe interactions are crucial for driving rhizosphere processes that contribute to metabolite turnover and nutrient cycling. With the increasing frequency and severity of water scarcity due to climate warming, understanding how plant-mediated processes, such as root exudation, influence soil organic matter turnover in the rhizosphere is essential. In this study, we used 16S rRNA gene amplicon sequencing, rhizosphere metabolomics, and position-specific 13C-pyruvate labeling to examine the effects of three different plant species (Piper auritum, Hibiscus rosa sinensis, and Clitoria fairchildiana) and their associated microbial communities on soil organic carbon turnover in the rhizosphere. Our findings indicate that in these tropical plants, the rhizosphere metabolome is primarily shaped by the response of roots to drought rather than direct shifts in the rhizosphere bacterial community composition. Specifically, the reduced exudation of plant roots had a notable effect on the metabolome of the rhizosphere of P. auritum, with less reliance on neighboring microbes. Contrary to P. auritum, H. rosa sinensis and C. fairchildiana experienced changes in their exudate composition during drought, causing alterations to the bacterial communities in the rhizosphere. This, in turn, had a collective impact on the rhizosphere's metabolome. Furthermore, the exclusion of phylogenetically distant microbes from the rhizosphere led to shifts in its metabolome. Additionally, C. fairchildiana appeared to be associated with only a subset of symbiotic bacteria under drought conditions. These results indicate that plant species-specific microbial interactions systematically change with the root metabolome. As roots respond to drought, their associated microbial communities adapt, potentially reinforcing the drought tolerance strategies of plant roots. These findings have significant implications for maintaining plant health and preference during drought stress and improving plant performance under climate change.
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Affiliation(s)
- Gina A Hildebrand
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA
| | - Linnea K Honeker
- BIO5 Institute, The University of Arizona, 1657 E Helen St., Tucson, AZ 85719, USA; School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St., Tucson, AZ 85721, USA
| | - Viviana Freire-Zapata
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA
| | - Christian Ayala-Ortiz
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA
| | - Sumudu Rajakaruna
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA
| | - Jane Fudyma
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA; Department of Plant Pathology, University of California, Davis, One Shields Avenue, Davis, CA 95816, USA
| | - L Erik Daber
- Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Roya AminiTabrizi
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA
| | - Rosalie L Chu
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Jason Toyoda
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Sarah E Flowers
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
| | - David W Hoyt
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Rasha Hamdan
- Department of Chemistry and Biochemistry, Lebanese University, Beirut, Lebanon
| | - Juliana Gil-Loaiza
- School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St., Tucson, AZ 85721, USA
| | - Lingling Shi
- Geo-Biosphere Interactions, Department of Geosciences, University of Tuebingen, Schnarrenbergstrasse 94-96, 72076 Tuebingen, Germany
| | - Michaela A Dippold
- Geo-Biosphere Interactions, Department of Geosciences, University of Tuebingen, Schnarrenbergstrasse 94-96, 72076 Tuebingen, Germany
| | - S Nemiah Ladd
- Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany; Department of Environmental Science, University of Basel, Bernoullistrasse 30/32, 4056 Basel, Switzerland
| | - Christiane Werner
- Georges-Köhler-Allee 53/54, University of Freiburg, 79110 Freiburg, Germany
| | - Laura K Meredith
- BIO5 Institute, The University of Arizona, 1657 E Helen St., Tucson, AZ 85719, USA; School of Natural Resources and the Environment, University of Arizona, 1064 E Lowell St., Tucson, AZ 85721, USA; Biosphere 2, University of Arizona, 32540 S Biosphere Rd, Oracle, AZ 85739, USA
| | - Malak M Tfaily
- Department of Environmental Science, University of Arizona, 1177 E 4th St., AZ 85721, USA; BIO5 Institute, The University of Arizona, 1657 E Helen St., Tucson, AZ 85719, USA; Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA.
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8
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Nakayasu ES, Gritsenko MA, Kim YM, Kyle JE, Stratton KG, Nicora CD, Munoz N, Navarro KM, Claborne D, Gao Y, Weitz KK, Paurus VL, Bloodsworth KJ, Allen KA, Bramer LM, Montes F, Clark KA, Tietje G, Teeguarden J, Burnum-Johnson KE. Elucidating regulatory processes of intense physical activity by multi-omics analysis. Mil Med Res 2023; 10:48. [PMID: 37853489 PMCID: PMC10583322 DOI: 10.1186/s40779-023-00477-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/28/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment. METHODS To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of three biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- versus pre-exercise were compared by Student's t-test followed by pathway analysis and comparison between the different omics modalities. RESULTS Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection. CONCLUSION This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.
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Affiliation(s)
- Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA.
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Young-Mo Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Nathalie Munoz
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Kathleen M Navarro
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Western States Division, Denver, CO, 80204, USA
| | - Daniel Claborne
- Computational Analytics Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Vanessa L Paurus
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Kent J Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Kelsey A Allen
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA
| | - Fernando Montes
- Los Angeles County Fire Department, Los Angeles, CA, 90063, USA
| | - Kathleen A Clark
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Respiratory Health Division, Morgantown, WV, 26505, USA
| | - Grant Tietje
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Justin Teeguarden
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA.
- Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, 97331, USA.
| | - Kristin E Burnum-Johnson
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, USA.
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9
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Beyene HB, Giles C, Huynh K, Wang T, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Cadby G, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Meikle PJ. Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts. Nat Commun 2023; 14:6280. [PMID: 37805498 PMCID: PMC10560260 DOI: 10.1038/s41467-023-41963-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/26/2023] [Indexed: 10/09/2023] Open
Abstract
Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, WA, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- School of Medicine, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands, WA, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - Gemma Cadby
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia.
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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10
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Jones J, MacKrell EJ, Wang TY, Lomenick B, Roukes ML, Chou TF. Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization. BMC Bioinformatics 2023; 24:239. [PMID: 37280522 DOI: 10.1186/s12859-023-05360-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/25/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object. RESULTS The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms. CONCLUSIONS Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.
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Affiliation(s)
- Jeff Jones
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, 91125, USA.
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA, 91125, USA.
| | - Elliot J MacKrell
- Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA, 91125, USA
| | - Ting-Yu Wang
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Brett Lomenick
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Michael L Roukes
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA, 91125, USA
| | - Tsui-Fen Chou
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, 91125, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
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11
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Merrihew GE, Park J, Plubell D, Searle BC, Keene CD, Larson EB, Bateman R, Perrin RJ, Chhatwal JP, Farlow MR, McLean CA, Ghetti B, Newell KL, Frosch MP, Montine TJ, MacCoss MJ. A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer's disease. Sci Data 2023; 10:206. [PMID: 37059743 PMCID: PMC10104800 DOI: 10.1038/s41597-023-02057-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/08/2023] [Indexed: 04/16/2023] Open
Abstract
Alzheimer's disease (AD) is a looming public health disaster with limited interventions. Alzheimer's is a complex disease that can present with or without causative mutations and can be accompanied by a range of age-related comorbidities. This diverse presentation makes it difficult to study molecular changes specific to AD. To better understand the molecular signatures of disease we constructed a unique human brain sample cohort inclusive of autosomal dominant AD dementia (ADD), sporadic ADD, and those without dementia but with high AD histopathologic burden, and cognitively normal individuals with no/minimal AD histopathologic burden. All samples are clinically well characterized, and brain tissue was preserved postmortem by rapid autopsy. Samples from four brain regions were processed and analyzed by data-independent acquisition LC-MS/MS. Here we present a high-quality quantitative dataset at the peptide and protein level for each brain region. Multiple internal and external control strategies were included in this experiment to ensure data quality. All data are deposited in the ProteomeXchange repositories and available from each step of our processing.
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Affiliation(s)
- Gennifer E Merrihew
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, USA
| | - Jea Park
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, USA
| | - Deanna Plubell
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, USA
| | - Brian C Searle
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, 43210, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, 98195, USA
| | - Eric B Larson
- Department of Medicine, University of Washington, Seattle, Washington, 98195, USA
| | - Randall Bateman
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Box 8111, St. Louis, Missouri, 63110, USA
| | - Richard J Perrin
- Department of Pathology, Washington University School of Medicine, 660 South Euclid Avenue, Box 8111, St. Louis, Missouri, 63110, USA
| | - Jasmeer P Chhatwal
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, 15 Parkman St, Suite 835, Boston, Massachusetts, 02114, USA
| | - Martin R Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Catriona A McLean
- Department of Anatomical Pathology, Alfred Health, Melbourne, VIC, 3004, Australia
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, and Massachusetts Alzheimer Disease Research Center, Massachusetts General Hospital, Boston, Massachusetts, 02114, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA.
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, USA.
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12
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Bramer LM, Hontz RD, Eisfeld AJ, Sims AC, Kim YM, Stratton KG, Nicora CD, Gritsenko MA, Schepmoes AA, Akasaka O, Koga M, Tsutsumi T, Nakamura M, Nakachi I, Baba R, Tateno H, Suzuki S, Nakajima H, Kato H, Ishida K, Ishii M, Uwamino Y, Mitamura K, Paurus VL, Nakayasu ES, Attah IK, Letizia AG, Waters KM, Metz TO, Corson K, Kawaoka Y, Gerbasi VR, Yotsuyanagi H, Iwatsuki-Horimoto K. Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections. Heliyon 2023; 9:e13795. [PMID: 36915486 PMCID: PMC9988701 DOI: 10.1016/j.heliyon.2023.e13795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 03/09/2023] Open
Abstract
The detailed mechanisms of COVID-19 infection pathology remain poorly understood. To improve our understanding of SARS-CoV-2 pathology, we performed a multi-omics and correlative analysis of an immunologically naïve SARS-CoV-2 clinical cohort from blood plasma of uninfected controls, mild, and severe infections. Consistent with previous observations, severe patient populations showed an elevation of pulmonary surfactant levels. Intriguingly, mild patients showed a statistically significant elevation in the carnosine dipeptidase modifying enzyme (CNDP1). Mild and severe patient populations showed a strong elevation in the metabolite L-cystine (oxidized form of the amino acid cysteine) and enzymes with roles in glutathione metabolism. Neutrophil extracellular traps (NETs) were observed in both mild and severe populations, and NET formation was higher in severe vs. mild samples. Our correlative analysis suggests a potential protective role for CNDP1 in suppressing PSPB release from the pulmonary space whereas NET formation correlates with increased PSPB levels and disease severity. In our discussion we put forward a possible model where NET formation drives pulmonary occlusions and CNDP1 promotes antioxidation, pleiotropic immune responses, and vasodilation by accelerating histamine synthesis.
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Affiliation(s)
- Lisa M Bramer
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert D Hontz
- U.S. Naval Medical Research Unit No. TWO (NAMRU-2), Singapore, Singapore
| | - Amie J Eisfeld
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Amy C Sims
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | | | | | - Osamu Akasaka
- Emergency Medical Center, Fujisawa City Hospital 2-6-1 Fujisawa, Fujisawa, Japan
| | - Michiko Koga
- Division of Infectious Diseases, Advanced Clinical Research Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Takeya Tsutsumi
- Division of Infectious Diseases, Advanced Clinical Research Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Morio Nakamura
- Department of Pulmonary Medicine, Tokyo Saiseikai Central, Tokyo, Japan
| | - Ichiro Nakachi
- Pulmonary Division, Department of Internal Medicine, Utsunomiya Hospital, Utsunomiya, Japan
| | - Rie Baba
- Pulmonary Division, Department of Internal Medicine, Utsunomiya Hospital, Utsunomiya, Japan
| | - Hiroki Tateno
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shoji Suzuki
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Hideaki Nakajima
- Department of Hematology and Clinical Immunology, University School of Medicine, Yokohama, Japan
| | - Hideaki Kato
- Department of Hematology and Clinical Immunology, University School of Medicine, Yokohama, Japan
| | | | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yoshifumi Uwamino
- Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Keiko Mitamura
- Division of Infection Control, Eiju General Hospital, Tokyo, Japan
| | | | | | - Isaac K Attah
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Andrew G Letizia
- U.S. Naval Medical Research Unit No. TWO (NAMRU-2), Singapore, Singapore
| | | | - Thomas O Metz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Karen Corson
- U.S. Naval Medical Research Unit No. TWO (NAMRU-2), Singapore, Singapore
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, USA.,Department of Microbiology and Immunology, Japan.,International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | | | - Hiroshi Yotsuyanagi
- Division of Infectious Diseases, Advanced Clinical Research Center, Institute of Medical Science, University of Tokyo
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13
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Ayala-Ortiz C, Graf-Grachet N, Freire-Zapata V, Fudyma J, Hildebrand G, AminiTabrizi R, Howard-Varona C, Corilo YE, Hess N, Duhaime MB, Sullivan MB, Tfaily MM. MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data. MICROBIOME 2023; 11:28. [PMID: 36803638 PMCID: PMC9936664 DOI: 10.1186/s40168-023-01476-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. RESULTS Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. CONCLUSION Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User's guide of MetaboDirect are freely available through ( https://github.com/Coayala/MetaboDirect ) and ( https://metabodirect.readthedocs.io/en/latest/ ), respectively. Video Abstract.
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Affiliation(s)
| | - Nathalia Graf-Grachet
- Department of Environmental, Science, University of Arizona, Tucson, AZ 85721 USA
- Present address: Roche, Pleasanton, CA 94588 USA
| | | | - Jane Fudyma
- Department of Environmental, Science, University of Arizona, Tucson, AZ 85721 USA
- Present address: University of California, Davis|Department of Plant Pathology, Davis, CA 95616-8751 USA
| | - Gina Hildebrand
- Department of Environmental, Science, University of Arizona, Tucson, AZ 85721 USA
| | - Roya AminiTabrizi
- Department of Environmental, Science, University of Arizona, Tucson, AZ 85721 USA
- Present address: University of Chicago Biological Sciences Division, Metabolomics Platform, Chicago, IL 60637 USA
| | - Cristina Howard-Varona
- Department of Microbiology, Ohio State University, Columbus, OH 43210 USA
- Center of Microbiome Science, Ohio State University, Columbus, OH 43210 USA
| | - Yuri E. Corilo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354 USA
| | - Nancy Hess
- Present address: University of California, Davis|Department of Plant Pathology, Davis, CA 95616-8751 USA
| | - Melissa B. Duhaime
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109 USA
| | - Matthew B. Sullivan
- Department of Microbiology, Ohio State University, Columbus, OH 43210 USA
- Center of Microbiome Science, Ohio State University, Columbus, OH 43210 USA
- Department of Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, OH 43210 USA
| | - Malak M. Tfaily
- Department of Environmental, Science, University of Arizona, Tucson, AZ 85721 USA
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354 USA
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14
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Degnan DJ, Stratton KG, Richardson R, Claborne D, Martin EA, Johnson NA, Leach D, Webb-Robertson BJM, Bramer LM. pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. J Proteome Res 2023; 22:570-576. [PMID: 36622218 DOI: 10.1021/acs.jproteome.2c00610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry data, tailored to specific characteristics of proteomic (isobaric or labeled), metabolomic, and lipidomic data sets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for transcriptomic data analyses. These improvements have made progress toward a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package's statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.
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15
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A Mineral-Doped Micromodel Platform Demonstrates Fungal Bridging of Carbon Hot Spots and Hyphal Transport of Mineral-Derived Nutrients. mSystems 2022; 7:e0091322. [PMID: 36394319 PMCID: PMC9765027 DOI: 10.1128/msystems.00913-22] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hot spots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood primarily due to the lack of a platform for spatially resolved analysis of biotic-driven mineral weathering. Here, we addressed this knowledge gap by demonstrating a mineral-doped soil micromodel platform where mineral weathering mechanisms can be studied. We directly visualize acquisition and transport of inorganic nutrients from minerals through fungal hyphae in the micromodel using a multimodal imaging approach. We found that Fusarium sp. strain DS 682, a representative of common saprotrophic soil fungus, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals. The fungus incorporated and translocated potassium (K) from K-rich mineral interfaces, as evidenced by visualization of mineral-derived nutrient transport and unique K chemical moieties following fungus-induced mineral weathering. Specific membrane transport proteins were expressed in the fungus in the presence of minerals, including those involved in oxidative phosphorylation pathways and the transmembrane transport of small-molecular-weight organic acids. This study establishes the significance of a spatial visualization platform for investigating microbial induced mineral weathering at microbially relevant scales. Moreover, we demonstrate the importance of fungal biology and nutrient translocation in maintaining fungal growth under water and carbon limitations in a reduced-complexity soil-like microenvironment. IMPORTANCE Fungal species are foundational members of soil microbiomes, where their contributions in accessing and transporting vital nutrients is key for community resilience. To date, the molecular mechanisms underlying fungal mineral weathering and nutrient translocation in low-nutrient environments remain poorly resolved due to the lack of a platform for spatial analysis of biotic weathering processes. Here, we addressed this knowledge gap by developing a mineral-doped soil micromodel platform. We demonstrate the function of this platform by directly probing fungal growth using spatially resolved optical and chemical imaging methodologies. We found the presence of minerals was required for fungal thigmotropism around obstacles and through soil-like pore spaces, and this was related to fungal transport of potassium (K) and corresponding K speciation from K-rich minerals. These findings provide new evidence and visualization into hyphal transport of mineral-derived nutrients under nutrient and water stresses.
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16
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Moraes ECDS, Martins-Gonçalves R, da Silva LR, Mandacaru SC, Melo RM, Azevedo-Quintanilha I, Perales J, Bozza FA, Souza TML, Castro-Faria-Neto HC, Hottz ED, Bozza PT, Trugilho MRO. Proteomic Profile of Procoagulant Extracellular Vesicles Reflects Complement System Activation and Platelet Hyperreactivity of Patients with Severe COVID-19. Front Cell Infect Microbiol 2022; 12:926352. [PMID: 35937696 PMCID: PMC9354812 DOI: 10.3389/fcimb.2022.926352] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/20/2022] [Indexed: 01/08/2023] Open
Abstract
Background Extracellular vesicles (EVs) are a valuable source of biomarkers and display the pathophysiological status of various diseases. In COVID-19, EVs have been explored in several studies for their ability to reflect molecular changes caused by SARS-CoV-2. Here we provide insights into the roles of EVs in pathological processes associated with the progression and severity of COVID-19. Methods In this study, we used a label-free shotgun proteomic approach to identify and quantify alterations in EV protein abundance in severe COVID-19 patients. We isolated plasma extracellular vesicles from healthy donors and patients with severe COVID-19 by size exclusion chromatography (SEC). Then, flow cytometry was performed to assess the origin of EVs and to investigate the presence of circulating procoagulant EVs in COVID-19 patients. A total protein extraction was performed, and samples were analyzed by nLC-MS/MS in a Q-Exactive HF-X. Finally, computational analysis was applied to signify biological processes related to disease pathogenesis. Results We report significant changes in the proteome of EVs from patients with severe COVID-19. Flow cytometry experiments indicated an increase in total circulating EVs and with tissue factor (TF) dependent procoagulant activity. Differentially expressed proteins in the disease groups were associated with complement and coagulation cascades, platelet degranulation, and acute inflammatory response. Conclusions The proteomic data reinforce the changes in the proteome of extracellular vesicles from patients infected with SARS-CoV-2 and suggest a role for EVs in severe COVID-19.
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Affiliation(s)
- Emilly Caroline dos Santos Moraes
- Laboratory of Toxinology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
- Laboratory of Immunopharmacology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
| | - Remy Martins-Gonçalves
- Laboratory of Immunopharmacology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
| | - Luana Rocha da Silva
- Laboratory of Toxinology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
- Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Samuel Coelho Mandacaru
- Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Reynaldo Magalhães Melo
- Laboratory Protein Chemistry and Biochemistry and Laboratory of Gene Biology, Department of Cell Biology, University of Brasília, Brasília, Brazil
| | | | - Jonas Perales
- Laboratory of Toxinology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
| | - Fernando A. Bozza
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
- D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Thiago Moreno Lopes Souza
- Laboratory of Immunopharmacology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
- Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Eugenio D. Hottz
- Laboratory of Immunothrombosis, Department of Biochemistry, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Patricia T. Bozza
- Laboratory of Immunopharmacology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
- *Correspondence: Monique R. O. Trugilho, , ; Patricia T. Bozza, ,
| | - Monique R. O. Trugilho
- Laboratory of Toxinology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil
- Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
- *Correspondence: Monique R. O. Trugilho, , ; Patricia T. Bozza, ,
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17
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Dayon L, Cominetti O, Affolter M. Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications. Expert Rev Proteomics 2022; 19:131-151. [PMID: 35466824 DOI: 10.1080/14789450.2022.2070477] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics. EXPERT OPINION While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
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18
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Clair G, Bramer LM, Misra R, McGraw MD, Bhattacharya S, Kitzmiller JA, Feng S, Danna VG, Bandyopadhyay G, Bhotika H, Huyck HL, Deutsch GH, Mariani TJ, Carson JP, Whitsett JA, Pryhuber GS, Adkins JN, Ansong C. Proteomic Analysis of Human Lung Development. Am J Respir Crit Care Med 2022; 205:208-218. [PMID: 34752721 PMCID: PMC8787240 DOI: 10.1164/rccm.202008-3303oc] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Rationale: The current understanding of human lung development derives mostly from animal studies. Although transcript-level studies have analyzed human donor tissue to identify genes expressed during normal human lung development, protein-level analysis that would enable the generation of new hypotheses on the processes involved in pulmonary development are lacking. Objectives: To define the temporal dynamic of protein expression during human lung development. Methods: We performed proteomics analysis of human lungs at 10 distinct times from birth to 8 years to identify the molecular networks mediating postnatal lung maturation. Measurements and Main Results: We identified 8,938 proteins providing a comprehensive view of the developing human lung proteome. The analysis of the data supports the existence of distinct molecular substages of alveolar development and predicted the age of independent human lung samples, and extensive remodeling of the lung proteome occurred during postnatal development. Evidence of post-transcriptional control was identified in early postnatal development. An extensive extracellular matrix remodeling was supported by changes in the proteome during alveologenesis. The concept of maturation of the immune system as an inherent part of normal lung development was substantiated by flow cytometry and transcriptomics. Conclusions: This study provides the first in-depth characterization of the human lung proteome during development, providing a unique proteomic resource freely accessible at Lungmap.net. The data support the extensive remodeling of the lung proteome during development, the existence of molecular substages of alveologenesis, and evidence of post-transcriptional control in early postnatal development.
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Affiliation(s)
| | | | - Ravi Misra
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - Matthew D. McGraw
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | | | - Joseph A. Kitzmiller
- Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, Ohio
| | | | | | - Gautam Bandyopadhyay
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - Harsh Bhotika
- Environmental Molecular Science Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Heidie L. Huyck
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - Gail H. Deutsch
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington; and
| | - Thomas J. Mariani
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - James P. Carson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, Texas
| | - Jeffrey A. Whitsett
- Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Gloria S. Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
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19
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Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q, Cui X, Hong J, Li X, Chen Y, Xue W, Zhu F. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 2021; 21:621-636. [PMID: 30649171 PMCID: PMC7299298 DOI: 10.1093/bib/bby127] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/19/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaofeng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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20
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Nakayasu ES, Gritsenko M, Piehowski PD, Gao Y, Orton DJ, Schepmoes AA, Fillmore TL, Frohnert BI, Rewers M, Krischer JP, Ansong C, Suchy-Dicey AM, Evans-Molina C, Qian WJ, Webb-Robertson BJM, Metz TO. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat Protoc 2021; 16:3737-3760. [PMID: 34244696 PMCID: PMC8830262 DOI: 10.1038/s41596-021-00566-6] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/26/2021] [Indexed: 02/06/2023]
Abstract
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
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Affiliation(s)
- Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Marina Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel J Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Athena A Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas L Fillmore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Brigitte I Frohnert
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Jeffrey P Krischer
- Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Charles Ansong
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Astrid M Suchy-Dicey
- Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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21
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Thompson AM, Stratton KG, Bramer LM, Zavoshy NS, McCue LA. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2021; 35:e9068. [PMID: 33590907 DOI: 10.1002/rcm.9068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 06/12/2023]
Abstract
RATIONALE Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data. METHODS We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide the selection of appropriate normalization approaches for two different FT-ICR-MS data sets. Furthermore, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses. RESULTS The normalization approach identified by SPANS differed for the two data sets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that total sum scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches. CONCLUSIONS Although there is no single, best normalization method for all data sets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. The number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.
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Affiliation(s)
- Allison M Thompson
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Kelly G Stratton
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Lisa M Bramer
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Nicole S Zavoshy
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Lee Ann McCue
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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22
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Barklis E, Alfadhli A, Kyle JE, Bramer LM, Bloodsworth KJ, Barklis RL, Leier HC, Petty RM, Zelnik ID, Metz TO, Futerman AH, Tafesse FG. Ceramide synthase 2 deletion decreases the infectivity of HIV-1. J Biol Chem 2021; 296:100340. [PMID: 33515546 PMCID: PMC7949126 DOI: 10.1016/j.jbc.2021.100340] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/19/2021] [Accepted: 01/22/2021] [Indexed: 01/01/2023] Open
Abstract
The lipid composition of HIV-1 virions is enriched in sphingomyelin (SM), but the roles that SM or other sphingolipids (SLs) might play in the HIV-1 replication pathway have not been elucidated. In human cells, SL levels are regulated by ceramide synthase (CerS) enzymes that produce ceramides, which can be converted to SMs, hexosylceramides, and other SLs. In many cell types, CerS2, which catalyzes the synthesis of very long chain ceramides, is the major CerS. We have examined how CerS2 deficiency affects the assembly and infectivity of HIV-1. As expected, we observed that very long chain ceramide, hexosylceramide, and SM were reduced in CerS2 knockout cells. CerS2 deficiency did not affect HIV-1 assembly or the incorporation of the HIV-1 envelope (Env) protein into virus particles, but it reduced the infectivites of viruses produced in the CerS2-deficient cells. The reduced viral infection levels were dependent on HIV-1 Env, since HIV-1 particles that were pseudotyped with the vesicular stomatitis virus glycoprotein did not exhibit reductions in infectivity. Moreover, cell-cell fusion assays demonstrated that the functional defect of HIV-1 Env in CerS2-deficient cells was independent of other viral proteins. Overall, our results indicate that the altered lipid composition of CerS2-deficient cells specifically inhibit the HIV-1 Env receptor binding and/or fusion processes.
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Affiliation(s)
- Eric Barklis
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA.
| | - Ayna Alfadhli
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, Washington, USA
| | - Lisa M Bramer
- Computing and Analytics Division, National Security Directorate PNNL, Richland, Washington, USA
| | - Kent J Bloodsworth
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, Washington, USA
| | - Robin Lid Barklis
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA
| | - Hans C Leier
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA
| | - R Max Petty
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA
| | - Iris D Zelnik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Thomas O Metz
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, Washington, USA
| | - Anthony H Futerman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Fikadu G Tafesse
- Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, Oregon, USA.
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23
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Gao Y, Fillmore TL, Munoz N, Bentley GJ, Johnson CW, Kim J, Meadows JA, Zucker JD, Burnet MC, Lipton AK, Bilbao A, Orton DJ, Kim YM, Moore RJ, Robinson EW, Baker SE, Webb-Robertson BJM, Guss AM, Gladden JM, Beckham GT, Magnuson JK, Burnum-Johnson KE. High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440. Front Bioeng Biotechnol 2020; 8:603488. [PMID: 33425868 PMCID: PMC7793925 DOI: 10.3389/fbioe.2020.603488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/10/2020] [Indexed: 11/13/2022] Open
Abstract
Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.
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Affiliation(s)
- Yuqian Gao
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Nathalie Munoz
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Gayle J Bentley
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,National Renewable Energy Laboratory, Golden, CO, United States
| | - Christopher W Johnson
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,National Renewable Energy Laboratory, Golden, CO, United States
| | - Joonhoon Kim
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Jamie A Meadows
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Sandia National Laboratories, Livermore, CA, United States
| | - Jeremy D Zucker
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Meagan C Burnet
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Anna K Lipton
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Aivett Bilbao
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Young-Mo Kim
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Ronald J Moore
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Errol W Robinson
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Scott E Baker
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Bobbie-Jo M Webb-Robertson
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Adam M Guss
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - John M Gladden
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Sandia National Laboratories, Livermore, CA, United States
| | - Gregg T Beckham
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,National Renewable Energy Laboratory, Golden, CO, United States
| | - Jon K Magnuson
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
| | - Kristin E Burnum-Johnson
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States.,Pacific Northwest National Laboratory, Richland, WA, United States
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24
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Zegeye EK, Sadler NC, Lomas GX, Attah IK, Jansson JK, Hofmockel KS, Anderton CR, Wright AT. Activity-Based Protein Profiling of Chitin Catabolism. Chembiochem 2020; 22:717-723. [PMID: 33049124 DOI: 10.1002/cbic.202000616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/12/2020] [Indexed: 01/09/2023]
Abstract
The microbial catabolism of chitin, an abundant and ubiquitous environmental organic polymer, is a fundamental cog in terrestrial and aquatic carbon and nitrogen cycles. Despite the importance of this critical bio-geochemical function, there is a limited understanding of the synergy between the various hydrolytic and accessory enzymes involved in chitin catabolism. To address this deficit, we synthesized activity-based probes (ABPs) designed to target active chitinolytic enzymes by modifying the chitin subunits N-acetyl glucosamine and chitotriose. The ABPs were used to determine the active complement of chitinolytic enzymes produced over time by the soil bacterium Cellvibrio japonicus treated with various C substrates. We demonstrate the utility of these ABPs in determining the synergy between various enzymes involved in chitin catabolism. The strategy can be used to gain molecular-level insights that can be used to better understand microbial roles in soil bio-geochemical cycling in the face of a changing climate.
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Affiliation(s)
- Elias K Zegeye
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, 1505 NE Stadium Way, Pullman, WA 99164, USA
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
| | - Natalie C Sadler
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
| | - Gerard X Lomas
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
| | - Isaac K Attah
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 3335 Innovation Boulevard, Richland, WA 99354, USA
| | - Janet K Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
| | - Kirsten S Hofmockel
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
- Department of Ecology, Evolution and Organismal Biology Iowa State University, 251 Bessey Hall, Ames, Iowa (USA) 50011
| | - Christopher R Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 3335 Innovation Boulevard, Richland, WA 99354, USA
| | - Aaron T Wright
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, 1505 NE Stadium Way, Pullman, WA 99164, USA
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Box 999, Richland, WA 99354, USA
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25
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Graw S, Tang J, Zafar MK, Byrd AK, Bolden C, Peterson EC, Byrum SD. proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification. ACS OMEGA 2020; 5:25625-25633. [PMID: 33073088 PMCID: PMC7557219 DOI: 10.1021/acsomega.0c02564] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 08/14/2020] [Indexed: 05/13/2023]
Abstract
The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed "proteiNorm". The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.
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Affiliation(s)
- Stefan Graw
- Department
of Biochemistry and Molecular Biology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
- Arkansas
Children’s Research Institute, Little Rock, Arkansas, 72202 United States
| | - Jillian Tang
- Department
of Biochemistry and Molecular Biology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
| | - Maroof K Zafar
- Department
of Biochemistry and Molecular Biology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
| | - Alicia K Byrd
- Department
of Biochemistry and Molecular Biology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
| | - Chris Bolden
- Department
of Pharmacology and Toxicology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
| | - Eric C. Peterson
- Department
of Pharmacology and Toxicology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
| | - Stephanie D Byrum
- Department
of Biochemistry and Molecular Biology, University
of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205 United States
- Arkansas
Children’s Research Institute, Little Rock, Arkansas, 72202 United States
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26
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Bradley R, Langley BO, Ryan JJ, Phipps J, Hanes DA, Stack E, Jansson JK, Metz TO, Stevens JF. Xanthohumol microbiome and signature in healthy adults (the XMaS trial): a phase I triple-masked, placebo-controlled clinical trial. Trials 2020; 21:835. [PMID: 33028396 PMCID: PMC7542976 DOI: 10.1186/s13063-020-04769-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/24/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Natural products may provide a source for the discovery and development of adjunctive pharmacological interventions to modulate the inflammatory pathways contributing to chronic disease. Xanthohumol, a flavonoid from the hops plant (Humulus lupulus), has antioxidant and anti-inflammatory properties and may act as a prebiotic to the intestinal microbiota. Xanthohumol is not currently approved as a drug by the US Food and Drug Administration (FDA), but is available as a dietary supplement and ingredient in medical foods. To formally test the safety of xanthohumol, a phase I clinical trial ("XMaS") was designed and approved under an Investigational New Drug application to the US FDA. The main objective is to examine the clinical safety and subjective tolerability of xanthohumol in healthy adults compared to placebo. Additional aims are to monitor biomarkers related to inflammation, gut permeability, bile acid metabolism, routes, and in vivo products of xanthohumol metabolism, and to evaluate xanthohumol's impact on gut microbial composition. METHODS The safety and tolerability of xanthohumol in healthy adults will be evaluated in a triple-masked, randomized, placebo-controlled trial. Participants will be randomized to either 24 mg/day of xanthohumol or placebo for 8 weeks. Blood cell counts, hepatic and renal function tests, electrolytes, and self-reported health-related quality of life measures will be collected every 2 weeks. Participants will be queried for adverse events throughout the trial. Xanthohumol metabolites in blood, urine, and stool will be measured. Biomarkers to be evaluated include plasma tumor necrosis factor-alpha, various interleukins, soluble CD14, lipopolysaccharide-binding protein, fecal calprotectin, and bile acids to assess impact on inflammatory and gut permeability-related mechanisms in vivo. Stool samples will be analyzed to determine effects on the gut microbiome. DISCUSSION This phase I clinical trial of xanthohumol will assess safety and tolerability in healthy adults, collect extensive biomarker data for assessment of potential mechanism(s), and provide comparison data necessary for future phase II trials in chronic disease(s). The design and robustness of the planned safety and mechanistic evaluations planned provide a model for drug discovery pursuits from natural products. TRIAL REGISTRATION ClinicalTrials.gov NCT03735420 . Registered on November 8, 2018.
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Affiliation(s)
- Ryan Bradley
- National University of Natural Medicine, Portland, USA.
| | | | | | - John Phipps
- National University of Natural Medicine, Portland, USA
| | | | - Emily Stack
- National University of Natural Medicine, Portland, USA
| | | | - Thomas O Metz
- Pacific Northwest National Laboratory, Richland, USA
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27
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Beyene HB, Olshansky G, T. Smith AA, Giles C, Huynh K, Cinel M, Mellett NA, Cadby G, Hung J, Hui J, Beilby J, Watts GF, Shaw JS, Moses EK, Magliano DJ, Meikle PJ. High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies. PLoS Biol 2020; 18:e3000870. [PMID: 32986697 PMCID: PMC7544135 DOI: 10.1371/journal.pbio.3000870] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 10/08/2020] [Accepted: 09/01/2020] [Indexed: 12/11/2022] Open
Abstract
Obesity and related metabolic diseases show clear sex-related differences. The growing burden of these diseases calls for better understanding of the age- and sex-related metabolic consequences. High-throughput lipidomic analyses of population-based cohorts offer an opportunity to identify disease-risk-associated biomarkers and to improve our understanding of lipid metabolism and biology at a population level. Here, we comprehensively examined the relationship between lipid classes/subclasses and molecular species with age, sex, and body mass index (BMI). Furthermore, we evaluated sex specificity in the association of the plasma lipidome with age and BMI. Some 747 targeted lipid measures, representing 706 molecular lipid species across 36 classes/subclasses, were measured using a high-performance liquid chromatography coupled mass spectrometer on a total of 10,339 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), with 563 lipid species being validated externally on 4,207 participants of the Busselton Health Study (BHS). Heat maps were constructed to visualise the relative differences in lipidomic profile between men and women. Multivariable linear regression analyses, including sex-interaction terms, were performed to assess the associations of lipid species with cardiometabolic phenotypes. Associations with age and sex were found for 472 (66.9%) and 583 (82.6%) lipid species, respectively. We further demonstrated that age-associated lipidomic fingerprints differed by sex. Specific classes of ether-phospholipids and lysophospholipids (calculated as the sum composition of the species within the class) were inversely associated with age in men only. In analyses with women alone, higher triacylglycerol and lower lysoalkylphosphatidylcholine species were observed among postmenopausal women compared with premenopausal women. We also identified sex-specific associations of lipid species with obesity. Lysophospholipids were negatively associated with BMI in both sexes (with a larger effect size in men), whilst acylcarnitine species showed opposing associations based on sex (positive association in women and negative association in men). Finally, by utilising specific lipid ratios as a proxy for enzymatic activity, we identified stearoyl CoA desaturase (SCD-1), fatty acid desaturase 3 (FADS3), and plasmanylethanolamine Δ1-desaturase activities, as well as the sphingolipid metabolic pathway, as constituent perturbations of cardiometabolic phenotypes. Our analyses elucidate the effect of age and sex on lipid metabolism by offering a comprehensive view of the lipidomic profiles associated with common cardiometabolic risk factors. These findings have implications for age- and sex-dependent lipid metabolism in health and disease and suggest the need for sex stratification during lipid biomarker discovery, establishing biological reference intervals for assessment of disease risk.
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Affiliation(s)
- Habtamu B. Beyene
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | | | | | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, Australia
| | | | - Gemma Cadby
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Joseph Hung
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
| | - Jennie Hui
- School of Population and Global Health, University of Western Australia, Perth, Australia
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia
| | - John Beilby
- PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia
| | - Gerald F. Watts
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia
| | | | - Eric K. Moses
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Dianna J. Magliano
- Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Peter J. Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
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28
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Brombacher E, Schad A, Kreutz C. Tail-Robust Quantile Normalization. Proteomics 2020; 20:e2000068. [PMID: 32865322 DOI: 10.1002/pmic.202000068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/25/2020] [Indexed: 11/07/2022]
Abstract
High-throughput biological data-such as mass spectrometry (MS)-based proteomics data-suffer from systematic non-biological variance due to systematic errors. This hinders the estimation of "real" biological signals and, in turn, decreases the power of statistical tests and biases the identification of differentially expressed proteins. To remove such unintended variation, while retaining the biological signal of interest, analysis workflows for quantitative MS data typically comprise normalization prior to their statistical analysis. Several normalization methods, such as quantile normalization (QN), have originally been developed for microarray data. In contrast to microarray data proteomics data may contain features, in the form of protein intensities that are consistently high across experimental conditions and, hence, are encountered in the tails of the protein intensity distribution. If QN is applied in the presence of such proteins statistical inferences of the features' intensity profiles are impeded due to the biased estimation of their variance. A freely available, novel approach is introduced which serves as an improvement of the classical QN by preserving the biological signals of features in the tails of the intensity distribution and by accounting for sample-dependent missing values (MVs): The "tail-robust quantile normalization" (TRQN).
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Affiliation(s)
- Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, 79104, Freiburg, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, 79104, Freiburg, Germany.,German Cancer Consortium (DKTK), 79106, Freiburg, Germany.,German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Ariane Schad
- Center for Biosystems Analysis (ZBSA), University of Freiburg, 79104, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104, Freiburg, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, 79104, Freiburg, Germany
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29
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Leier HC, Weinstein JB, Kyle JE, Lee JY, Bramer LM, Stratton KG, Kempthorne D, Navratil AR, Tafesse EG, Hornemann T, Messer WB, Dennis EA, Metz TO, Barklis E, Tafesse FG. A global lipid map defines a network essential for Zika virus replication. Nat Commun 2020; 11:3652. [PMID: 32694525 PMCID: PMC7374707 DOI: 10.1038/s41467-020-17433-9] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 06/23/2020] [Indexed: 02/07/2023] Open
Abstract
Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.
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Affiliation(s)
- Hans C Leier
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA
| | - Jules B Weinstein
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, WA, 99352, USA
| | - Joon-Yong Lee
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, WA, 99352, USA
| | - Lisa M Bramer
- Computing and Analytics Division, National Security Directorate, PNNL, Richland, WA, 99352, USA
| | - Kelly G Stratton
- Computing and Analytics Division, National Security Directorate, PNNL, Richland, WA, 99352, USA
| | - Douglas Kempthorne
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA
- Center for Diversity and Inclusion, OHSU, Portland, OR, 97239, USA
| | - Aaron R Navratil
- Departments of Chemistry & Biochemistry and Pharmacology, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA
| | - Endale G Tafesse
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Thorsten Hornemann
- University Zurich and University Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
| | - William B Messer
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA
- Department of Medicine, Division of Infectious Diseases, OHSU, Portland, Oregon, 97239, USA
| | - Edward A Dennis
- Departments of Chemistry & Biochemistry and Pharmacology, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA
| | - Thomas O Metz
- Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory (PNNL), Richland, WA, 99352, USA
| | - Eric Barklis
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA
| | - Fikadu G Tafesse
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University (OHSU), Portland, OR, 97239, USA.
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30
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Nakayasu ES, Syed F, Tersey SA, Gritsenko MA, Mitchell HD, Chan CY, Dirice E, Turatsinze JV, Cui Y, Kulkarni RN, Eizirik DL, Qian WJ, Webb-Robertson BJM, Evans-Molina C, Mirmira RG, Metz TO. Comprehensive Proteomics Analysis of Stressed Human Islets Identifies GDF15 as a Target for Type 1 Diabetes Intervention. Cell Metab 2020; 31:363-374.e6. [PMID: 31928885 PMCID: PMC7319177 DOI: 10.1016/j.cmet.2019.12.005] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 09/03/2019] [Accepted: 12/12/2019] [Indexed: 01/03/2023]
Abstract
Type 1 diabetes (T1D) results from the progressive loss of β cells, a process propagated by pro-inflammatory cytokine signaling that disrupts the balance between pro- and anti-apoptotic proteins. To identify proteins involved in this process, we performed comprehensive proteomics of human pancreatic islets treated with interleukin-1β and interferon-γ, leading to the identification of 11,324 proteins, of which 387 were significantly regulated by treatment. We then tested the function of growth/differentiation factor 15 (GDF15), which was repressed by the treatment. We found that GDF15 translation was blocked during inflammation, and it was depleted in islets from individuals with T1D. The addition of exogenous GDF15 inhibited interleukin-1β+interferon-γ-induced apoptosis of human islets. Administration of GDF15 reduced by 53% the incidence of diabetes in NOD mice. Our approach provides a unique resource for the identification of the human islet proteins regulated by cytokines and was effective in discovering a potential target for T1D therapy.
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Affiliation(s)
- Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Farooq Syed
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sarah A Tersey
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Chi Yuet Chan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ercument Dirice
- Department of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Department of Medicine, Brigham and Women's Hospital, and Harvard Stem Cell Institute, Boston, MA, USA
| | - Jean-Valery Turatsinze
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Yi Cui
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rohit N Kulkarni
- Department of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Department of Medicine, Brigham and Women's Hospital, and Harvard Stem Cell Institute, Boston, MA, USA
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA; Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Raghavendra G Mirmira
- Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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31
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Piehowski PD, Zhu Y, Bramer LM, Stratton KG, Zhao R, Orton DJ, Moore RJ, Yuan J, Mitchell HD, Gao Y, Webb-Robertson BJM, Dey SK, Kelly RT, Burnum-Johnson KE. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nat Commun 2020; 11:8. [PMID: 31911630 PMCID: PMC6946663 DOI: 10.1038/s41467-019-13858-z] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 11/20/2019] [Indexed: 12/19/2022] Open
Abstract
Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation. Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.
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Affiliation(s)
- Paul D Piehowski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ying Zhu
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lisa M Bramer
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kelly G Stratton
- National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel J Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jia Yuan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hugh D Mitchell
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Sudhansu K Dey
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Ryan T Kelly
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA. .,Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA.
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32
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McClure RS, Wendler JP, Adkins JN, Swanstrom J, Baric R, Kaiser BLD, Oxford KL, Waters KM, McDermott JE. Unified feature association networks through integration of transcriptomic and proteomic data. PLoS Comput Biol 2019; 15:e1007241. [PMID: 31527878 PMCID: PMC6748406 DOI: 10.1371/journal.pcbi.1007241] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 07/02/2019] [Indexed: 11/18/2022] Open
Abstract
High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.
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Affiliation(s)
- Ryan S. McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Jason P. Wendler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Joshua N. Adkins
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Jesica Swanstrom
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
| | - Ralph Baric
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
| | - Brooke L. Deatherage Kaiser
- Signatures Science and Technology Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Kristie L. Oxford
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Katrina M. Waters
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
| | - Jason E. McDermott
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA, United States of America
- Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, OR, United States of America
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33
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Tang J, Fu J, Wang Y, Luo Y, Yang Q, Li B, Tu G, Hong J, Cui X, Chen Y, Yao L, Xue W, Zhu F. Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains. Mol Cell Proteomics 2019; 18:1683-1699. [PMID: 31097671 PMCID: PMC6682996 DOI: 10.1074/mcp.ra118.001169] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 04/28/2019] [Indexed: 12/13/2022] Open
Abstract
The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.
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Affiliation(s)
- Jing Tang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China; ¶Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Fu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Bo Li
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Gao Tu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jiajun Hong
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xuejiao Cui
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yuzong Chen
- ‖Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Lixia Yao
- **Department of Health Sciences Research, Mayo Clinic, Rochester MN 55905, United States
| | - Weiwei Xue
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
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34
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Tang J, Wang Y, Li Y, Zhang Y, Zhang R, Xiao Z, Luo Y, Guo X, Tao L, Lou Y, Xue W, Zhu F. Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics. Curr Pharm Des 2019; 25:1536-1553. [PMID: 31258068 DOI: 10.2174/1381612825666190618123306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 06/11/2019] [Indexed: 11/22/2022]
Abstract
Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Xueying Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 401331, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China
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35
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Bramer LM, Stratton KG, White AM, Bleeker AH, Kobold MA, Waters KM, Metz TO, Rodland KD, Webb-Robertson BJM. P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data. J Proteome Res 2019; 18:1426-1432. [PMID: 30667224 DOI: 10.1021/acs.jproteome.8b00840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.
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Affiliation(s)
- Lisa M Bramer
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Kelly G Stratton
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Amanda M White
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Ameila H Bleeker
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Markus A Kobold
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Katrina M Waters
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Thomas O Metz
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Karin D Rodland
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States.,Department of Cell, Developmental, and Cancer Biology , Oregon Health & Science University , Portland , Oregon 97221 , United States
| | - Bobbie-Jo M Webb-Robertson
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
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36
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Stratton K, Webb-Robertson BJM, McCue LA, Stanfill B, Claborne D, Godinez I, Johansen T, Thompson AM, Burnum-Johnson KE, Waters KM, Bramer LM. pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data. J Proteome Res 2019; 18:1418-1425. [PMID: 30638385 PMCID: PMC6750869 DOI: 10.1021/acs.jproteome.8b00760] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Indexed: 02/06/2023]
Abstract
Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.
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Affiliation(s)
- Kelly
G. Stratton
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Bobbie-Jo M. Webb-Robertson
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Lee Ann McCue
- Earth
& Biological Sciences Directorate, Pacific
Northwest National Laboratory, 902 Battelle Boulavard, Richland, Washington 99354, United States
| | - Bryan Stanfill
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Daniel Claborne
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Iobani Godinez
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Thomas Johansen
- Department
of Statistics, Florida State University, 117 North Woodward Avenue, Tallahassee, Florida 32306, United States
| | - Allison M. Thompson
- Earth
& Biological Sciences Directorate, Pacific
Northwest National Laboratory, 902 Battelle Boulavard, Richland, Washington 99354, United States
| | - Kristin E. Burnum-Johnson
- Earth
& Biological Sciences Directorate, Pacific
Northwest National Laboratory, 902 Battelle Boulavard, Richland, Washington 99354, United States
| | - Katrina M. Waters
- Earth
& Biological Sciences Directorate, Pacific
Northwest National Laboratory, 902 Battelle Boulavard, Richland, Washington 99354, United States
| | - Lisa M. Bramer
- National
Security Directorate, Pacific Northwest
National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
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37
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Plasma lipidome reveals critical illness and recovery from human Ebola virus disease. Proc Natl Acad Sci U S A 2019; 116:3919-3928. [PMID: 30808769 DOI: 10.1073/pnas.1815356116] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Ebola virus disease (EVD) often leads to severe and fatal outcomes in humans with early supportive care increasing the chances of survival. Profiling the human plasma lipidome provides insight into critical illness as well as diseased states, as lipids have essential roles as membrane structural components, signaling molecules, and energy sources. Here we show that the plasma lipidomes of EVD survivors and fatalities from Sierra Leone, infected during the 2014-2016 Ebola virus outbreak, were profoundly altered. Focusing on how lipids are associated in human plasma, while factoring in the state of critical illness, we found that lipidome changes were related to EVD outcome and could identify states of disease and recovery. Specific changes in the lipidome suggested contributions from extracellular vesicles, viremia, liver dysfunction, apoptosis, autophagy, and general critical illness, and we identified possible targets for therapies enhancing EVD survival.
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38
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Na-Nan K, Chaiprasit K, Pukkeeree P. Factor analysis-validated comprehensive employee job performance scale. INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2018. [DOI: 10.1108/ijqrm-06-2017-0117] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to develop an employee job performance (EJP) scale questionnaire that encompasses the job time, job quality and job quantity dimensions of employee performance.
Design/methodology/approach
In this study, the questionnaire questions were first validated using exploratory factor analysis (EFA) and then confirmatory factor analysis (CFA) along the three performance dimensions. The research samples were auto-parts assembly line workers. The factor analysis results confirmed the validity of the questionnaire as a reliable employee performance evaluation tool, as evidenced by the composite reliability (CR) and the average variance extracted (AVE).
Findings
The questionnaire questions identified three factors of EJP, namely, job time, job quality and job quantity, with 13 items. The factor analysis results confirmed the validity of the questionnaire as a reliable employee performance evaluation tool, as evidenced by a CR of 0.894 and AVE of 0.739, in addition to χ2=63.340, df=54, p=0.180, goodness of fit index=0.976, adjusted goodness of fit index=0.960, RMSEA=0.021 and root mean square residue=0.014.
Research limitations/implications
The EJP scale questionnaire was examined using only EFA and CFA. These scales are expected to help academics, researchers and practitioners test theories. Because of the features of the sample, the final results should be considered carefully.
Practical implications
Importantly, the factor analysis results suggest that the proposed EJP scale questionnaire can be applied to various industries and settings either as is or with minimal modifications.
Originality/value
The EJP questionnaire is novel and can serve as an excellent EJP tool to measure employees’ behavioral output. The questionnaire can provide empirical data on employee performance output based on the employee’s perspective.
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Välikangas T, Suomi T, Elo LL. A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 2018; 19:1-11. [PMID: 27694351 PMCID: PMC5862339 DOI: 10.1093/bib/bbw095] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Indexed: 12/25/2022] Open
Abstract
To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.
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Affiliation(s)
- Tommi Välikangas
- Computational Biomedicine Group at the Turku Centre for Biotechnology Finland
| | - Tomi Suomi
- Computational Biomedicine research group at the Turku Centre for Biotechnology Finland
| | - Laura L Elo
- Computational Biomedicine at Turku Centre for Biotechnology, University of Turku, Finland
- Corresponding author. Laura L. Elo, Turku Centre for Biotechnology, FI-20520 Turku, Finland. Tel.: +358-2-333-8009; Fax: +358-2-251 8808; E-mail:
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40
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Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery. Proteomes 2018; 6:proteomes6020020. [PMID: 29701723 PMCID: PMC6027525 DOI: 10.3390/proteomes6020020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 04/19/2018] [Accepted: 04/25/2018] [Indexed: 01/15/2023] Open
Abstract
Protein biomarkers are of great benefit for clinical research and applications, as they are powerful means for diagnosing, monitoring and treatment prediction of different diseases. Even though numerous biomarkers have been reported, the translation to clinical practice is still limited. This mainly due to: (i) incorrect biomarker selection, (ii) insufficient validation of potential biomarkers, and (iii) insufficient clinical use. In this review, we focus on the biomarker selection process and critically discuss the chemometrical and statistical decisions made in proteomics biomarker discovery to increase to selection of high value biomarkers. The characteristics of the data, the computational resources, the type of biomarker that is searched for and the validation strategy influence the decision making of the chemometrical and statistical methods and a decision made for one component directly influences the choice for another. Incorrect decisions could increase the false positive and negative rate of biomarkers which requires independent confirmation of outcome by other techniques and for comparison between different related studies. There are few guidelines for authors regarding data analysis documentation in peer reviewed journals, making it hard to reproduce successful data analysis strategies. Here we review multiple chemometrical and statistical methods for their value in proteomics-based biomarker discovery and propose to include key components in scientific documentation.
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MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape. Proc Natl Acad Sci U S A 2018; 115:E1012-E1021. [PMID: 29339515 PMCID: PMC5798318 DOI: 10.1073/pnas.1706928115] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ-dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.
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Wulff JE, Mitchell MW. A Comparison of Various Normalization Methods for LC/MS Metabolomics Data. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/abb.2018.98022] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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43
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Abstract
The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor.IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but is also vital to the development of microbial cell factories that can provide us with sustainable fuels and oleochemicals, we envision that our report introduces foundational work to further unravel the regulation of lipid accumulation in eukaryal cells.
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Khadempour L, Burnum-Johnson KE, Baker ES, Nicora CD, Webb-Robertson BJM, White RA, Monroe ME, Huang EL, Smith RD, Currie CR. The fungal cultivar of leaf-cutter ants produces specific enzymes in response to different plant substrates. Mol Ecol 2016; 25:5795-5805. [PMID: 27696597 DOI: 10.1111/mec.13872] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 09/17/2016] [Accepted: 09/19/2016] [Indexed: 01/20/2023]
Abstract
Herbivores use symbiotic microbes to help derive energy and nutrients from plant material. Leaf-cutter ants are a paradigmatic example, cultivating their mutualistic fungus Leucoagaricus gongylophorus on plant biomass that workers forage from a diverse collection of plant species. Here, we investigate the metabolic flexibility of the ants' fungal cultivar for utilizing different plant biomass. Using feeding experiments and a novel approach in metaproteomics, we examine the enzymatic response of L. gongylophorus to leaves, flowers, oats or a mixture of all three. Across all treatments, our analysis identified and quantified 1766 different fungal proteins, including 161 putative biomass-degrading enzymes. We found significant differences in the protein profiles in the fungus gardens of subcolonies fed different plant substrates. When provided with leaves or flowers, which contain the majority of their energy as recalcitrant plant polymers, the fungus gardens produced more proteins predicted to break down cellulose: endoglucanase, exoglucanase and β-glucosidase. Further, the complete metaproteomes for the leaves and flowers treatments were very similar, while the mixed substrate treatment closely resembled the treatment with oats alone. This indicates that when provided a mixture of plant substrates, fungus gardens preferentially break down the simpler, more digestible substrates. This flexible, substrate-specific enzymatic response of the fungal cultivar allows leaf-cutter ants to derive energy from a wide range of substrates, which likely contributes to their ability to be dominant generalist herbivores.
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Affiliation(s)
- Lily Khadempour
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.,Department of Zoology, University of Wisconsin-Madison, Madison, WI, 53706, USA.,Department of Energy Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Erin S Baker
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | | | - Richard A White
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Eric L Huang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Cameron R Currie
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.,Department of Energy Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, 53706, USA
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45
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Oxford KL, Wendler JP, McDermott JE, White III RA, Powell JD, Jacobs JM, Adkins JN, Waters KM. The landscape of viral proteomics and its potential to impact human health. Expert Rev Proteomics 2016; 13:579-91. [DOI: 10.1080/14789450.2016.1184091] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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46
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MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses. mSystems 2016; 1:mSystems00043-16. [PMID: 27822525 PMCID: PMC5069757 DOI: 10.1128/msystems.00043-16] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 03/31/2016] [Indexed: 01/14/2023] Open
Abstract
In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available.
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47
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Nakayasu ES, Nicora CD, Sims AC, Burnum-Johnson KE, Kim YM, Kyle JE, Matzke MM, Shukla AK, Chu RK, Schepmoes AA, Jacobs JM, Baric RS, Webb-Robertson BJ, Smith RD, Metz TO. MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses. mSystems 2016. [PMID: 27822525 DOI: 10.1128/msystems.00043-16.editor] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023] Open
Abstract
Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available.
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Affiliation(s)
- Ernesto S Nakayasu
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Carrie D Nicora
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Amy C Sims
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kristin E Burnum-Johnson
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Young-Mo Kim
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jennifer E Kyle
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Melissa M Matzke
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Anil K Shukla
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Rosalie K Chu
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Athena A Schepmoes
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jon M Jacobs
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Ralph S Baric
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Richard D Smith
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Thomas O Metz
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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48
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Tóth E, Hevér H, Ozohanics O, Telekes A, Vékey K, Drahos L. Simple correction improving long-term reproducibility of HPLC-MS. JOURNAL OF MASS SPECTROMETRY : JMS 2015; 50:1130-1135. [PMID: 26456781 DOI: 10.1002/jms.3629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 06/24/2015] [Accepted: 06/25/2015] [Indexed: 06/05/2023]
Abstract
Chromatographic peak areas in long series of high-performance liquid chromatography-MS experiments often vary, which decrease reproducibility and may cause bias in the results. It was found that the sensitivity of various components change differently; in our case, variability is in the order of 20-40%, and it is most likely due to changing conditions in electrospray ionization (ESI). The most often used peak area correction methods do not take this effect into account. The change in peak areas can be well described by a polynomial function; we found that a fourth-order polynomial is most often suitable. We suggest a simple correction algorithm based on polynomial fitting. When the experiments were inherently well reproducible, this correction improved reproducibility from 12% to 3% (on average for various components). When random errors were larger, this improvement was less significant (15% to 12% in nano-ESI) but nevertheless essential in order to avoid possible bias in the results.
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Affiliation(s)
- Eszter Tóth
- MS Proteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
- School of Ph.D. Studies, Semmelweis University, Üllői út 26, H-1085, Budapest, Hungary
| | - Helga Hevér
- Gedeon Richter PLC, H-1475, Budapest, PO Box 27, Hungary
- Department of Inorganic and Analytical Chemistry, Budapest University of Technology and Economics, Szent Gellért tér 4., H-1111, Budapest, Hungary
| | - Olivér Ozohanics
- MS Proteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
- MTA-TTK NAP B MS Neuroproteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
| | - András Telekes
- Department of Oncology, Bajcsy-Zsilinszky Municipal Hospital, Maglódi út 89, H-1106, Budapest, Hungary
| | - Károly Vékey
- MS Proteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
- Core Technologies Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
| | - László Drahos
- MS Proteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
- MTA-TTK NAP B MS Neuroproteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1519, Budapest, PO Box 286, Hungary
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49
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Lovelace ES, Wagoner J, MacDonald J, Bammler T, Bruckner J, Brownell J, Beyer R, Zink EM, Kim YM, Kyle JE, Webb-Robertson BJ, Waters KM, Metz TO, Farin F, Oberlies NH, Polyak SJ. Silymarin Suppresses Cellular Inflammation By Inducing Reparative Stress Signaling. JOURNAL OF NATURAL PRODUCTS 2015; 78:1990-2000. [PMID: 26186142 PMCID: PMC4703094 DOI: 10.1021/acs.jnatprod.5b00288] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Silymarin, a characterized extract of the seeds of milk thistle (Silybum marianum), suppresses cellular inflammation. To define how this occurs, transcriptional profiling, metabolomics, and signaling studies were performed in human liver and T cell lines. Cellular stress and metabolic pathways were modulated within 4 h of silymarin treatment: activation of Activating Transcription Factor 4 (ATF-4) and adenosine monophosphate protein kinase (AMPK) and inhibition of mammalian target of rapamycin (mTOR) signaling, the latter being associated with induction of DNA-damage-inducible transcript 4 (DDIT4). Metabolomics analyses revealed silymarin suppression of glycolytic, tricarboxylic acid (TCA) cycle, and amino acid metabolism. Anti-inflammatory effects arose with prolonged (i.e., 24 h) silymarin exposure, with suppression of multiple pro-inflammatory mRNAs and signaling pathways including nuclear factor kappa B (NF-κB) and forkhead box O (FOXO). Studies with murine knock out cells revealed that silymarin inhibition of both mTOR and NF-κB was partially AMPK dependent, whereas silymarin inhibition of mTOR required DDIT4. Other natural products induced similar stress responses, which correlated with their ability to suppress inflammation. Thus, natural products activate stress and repair responses that culminate in an anti-inflammatory cellular phenotype. Natural products like silymarin may be useful as tools to define how metabolic, stress, and repair pathways regulate cellular inflammation.
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Affiliation(s)
- Erica S. Lovelace
- Department of Laboratory Medicine, University of Washington, Seattle, WA, United States, 98104
| | - Jessica Wagoner
- Department of Laboratory Medicine, University of Washington, Seattle, WA, United States, 98104
| | - James MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, United States, 98105
| | - Theo Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, United States, 98105
| | - Jacob Bruckner
- Department of Laboratory Medicine, University of Washington, Seattle, WA, United States, 98104
| | | | - Richard Beyer
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, United States, 98105
| | - Erika M. Zink
- Biological Sciences Division Pacific Northwest National Laboratory, Richland, WA, United States
| | - Young-Mo Kim
- Biological Sciences Division Pacific Northwest National Laboratory, Richland, WA, United States
| | - Jennifer E. Kyle
- Biological Sciences Division Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Katrina M. Waters
- Biological Sciences Division Pacific Northwest National Laboratory, Richland, WA, United States
| | - Thomas O. Metz
- Biological Sciences Division Pacific Northwest National Laboratory, Richland, WA, United States
| | - Federico Farin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, United States, 98105
| | - Nicholas H. Oberlies
- Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, NC, United States
| | - Stephen J. Polyak
- Department of Laboratory Medicine, University of Washington, Seattle, WA, United States, 98104
- Department of Global Health, University of Washington, Seattle, WA, United States, 98104
- Department of Microbiology, University of Washington, Seattle, WA, United States, 98104Center for Ecogenetics and Environmental Health, University of Washington, Seattle, United States, 98105
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50
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Webb-Robertson BJM, Matzke MM, Datta S, Payne SH, Kang J, Bramer LM, Nicora CD, Shukla AK, Metz TO, Rodland KD, Smith RD, Tardiff MF, McDermott JE, Pounds JG, Waters KM. Bayesian proteoform modeling improves protein quantification of global proteomic measurements. Mol Cell Proteomics 2015; 13:3639-46. [PMID: 25433089 DOI: 10.1074/mcp.m113.030932] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.
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Affiliation(s)
- Bobbie-Jo M Webb-Robertson
- From the ‡Applied Statistics and Computational Modeling, Pacific Northwest National Laboratory, Richland, WA 99354;
| | - Melissa M Matzke
- §Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Susmita Datta
- ¶Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202
| | - Samuel H Payne
- ‖Omics Technology Development and Production, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Jiyun Kang
- ‖Omics Technology Development and Production, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Lisa M Bramer
- From the ‡Applied Statistics and Computational Modeling, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Carrie D Nicora
- ‖Omics Technology Development and Production, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Anil K Shukla
- ‖Omics Technology Development and Production, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Thomas O Metz
- ¶¶Omics Biological Applications, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Karin D Rodland
- ‡‡Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Richard D Smith
- ‡‡Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Mark F Tardiff
- From the ‡Applied Statistics and Computational Modeling, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Jason E McDermott
- §Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Joel G Pounds
- ‡‡Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354
| | - Katrina M Waters
- ‡‡Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354
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