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Nhieu J, Wei CW, Ludwig M, Drake JM, Wei LN. CRABP1-complexes in exosome secretion. Cell Commun Signal 2024; 22:381. [PMID: 39075476 PMCID: PMC11285139 DOI: 10.1186/s12964-024-01749-w] [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: 05/10/2024] [Accepted: 07/13/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Cellular retinoic acid binding protein 1 (CRABP1) mediates rapid, non-canonical activity of retinoic acid (RA) by forming signalosomes via protein-protein interactions. Two signalosomes have been identified previously: CRABP1-MAPK and CRABP1-CaMKII. Crabp1 knockout (CKO) mice exhibited altered exosome profiles, but the mechanism of CRABP1 action was unclear. This study aimed to screen for and identify novel CRABP1 signalosomes that could modulate exosome secretion by using a combinatorial approach involving biochemical, bioinformatic and molecular studies. METHODS Immunoprecipitation coupled with mass spectrometry (IP-MS) identified candidate CRABP1-interacting proteins which were subsequently analyzed using GO Term Enrichment, Functional Annotation Clustering; and Pathway Analysis. Gene expression analysis of CKO samples revealed altered expression of genes related to exosome biogenesis and secretion. The effect of CRABP1 on exosome secretion was then experimentally validated using CKO mice and a Crabp1 knockdown P19 cell line. RESULTS IP-MS identified CRABP1-interacting targets. Bioinformatic analyses revealed significant association with actin cytoskeletal dynamics, kinases, and exosome secretion. The effect of CRABP1 on exosome secretion was experimentally validated by comparing circulating exosome numbers of CKO and wild type (WT) mice, and secreted exosomes from WT and siCRABP1-P19 cells. Pathway analysis identified kinase signaling and Arp2/3 complex as the major pathways where CRABP1-signalosomes modulate exosome secretion, which was validated in the P19 system. CONCLUSION The combinatorial approach allowed efficient screening for and identification of novel CRABP1-signalosomes. The results uncovered a novel function of CRABP1 in modulating exosome secretion, and suggested that CRABP1 could play roles in modulating intercellular communication and signal propagation.
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Affiliation(s)
- Jennifer Nhieu
- Department of Pharmacology, University of Minnesota, 6-120 Jackson Hall, 321 Church St. SE, Minneapolis, MN, 55455, USA
| | - Chin-Wen Wei
- Department of Pharmacology, University of Minnesota, 6-120 Jackson Hall, 321 Church St. SE, Minneapolis, MN, 55455, USA
| | - Megan Ludwig
- Department of Pharmacology, University of Minnesota, 6-120 Jackson Hall, 321 Church St. SE, Minneapolis, MN, 55455, USA
| | - Justin M Drake
- Department of Pharmacology, University of Minnesota, 6-120 Jackson Hall, 321 Church St. SE, Minneapolis, MN, 55455, USA
| | - Li-Na Wei
- Department of Pharmacology, University of Minnesota, 6-120 Jackson Hall, 321 Church St. SE, Minneapolis, MN, 55455, USA.
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2
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Joseph S, Zhang X, Droby G, Wu D, Bae-Jump V, Lyons S, Mordant A, Mills A, Herring L, Rushing B, Bowser J, Vaziri C. MAPK14 /p38α Shapes the Molecular Landscape of Endometrial Cancer and promotes Tumorigenic Characteristics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600674. [PMID: 38979238 PMCID: PMC11230443 DOI: 10.1101/2024.06.25.600674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The molecular underpinnings of H igh G rade E ndometrial C arcinoma (HGEC) metastatic growth and survival are poorly understood. Here we show that ascites-derived and primary tumor HGEC cell lines in 3D spheroid culture faithfully recapitulate key features of malignant peritoneal effusion and exhibit fundamentally distinct transcriptomic, proteomic and metabolomic landscapes when compared with conventional 2D monolayers. Using genetic screening platform we identify MAPK14 (which encodes the protein kinase p38α) as a specific requirement for HGEC in spheroid culture. MAPK14 /p38α has broad roles in programing the phosphoproteome, transcriptome and metabolome of HGEC spheroids, yet has negligible impact on monolayer cultures. MAPK14 promotes tumorigenicity in vivo and is specifically required to sustain a sub-population of spheroid cells that is enriched in cancer stemness markers. Therefore, spheroid growth of HGEC activates unique biological programs, including p38α signaling, that cannot be captured using 2D culture models and are highly relevant to malignant disease pathology.
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Bhosale SD, Moulder R, Suomi T, Ruohtula T, Honkanen J, Virtanen SM, Ilonen J, Elo LL, Knip M, Lahesmaa R. Serum proteomics of mother-infant dyads carrying HLA-conferred type 1 diabetes risk. iScience 2024; 27:110048. [PMID: 38883825 PMCID: PMC11176638 DOI: 10.1016/j.isci.2024.110048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/22/2024] [Accepted: 05/17/2024] [Indexed: 06/18/2024] Open
Abstract
In-utero and dietary factors make important contributions toward health and development in early childhood. In this respect, serum proteomics of maturing infants can provide insights into studies of childhood diseases, which together with perinatal proteomes could reveal further biological perspectives. Accordingly, to determine differences between feeding groups and changes in infancy, serum proteomics analyses of mother-infant dyads with HLA-conferred susceptibility to type 1 diabetes (n = 22), weaned to either an extensively hydrolyzed or regular cow's milk formula, were made. The LC-MS/MS analyses included samples from the beginning of third trimester, the time of delivery, 3 months postpartum, cord blood, and samples from the infants at 3, 6, 9, and 12 months. Correlations between ranked protein intensities were detected within the dyads, together with perinatal and age-related changes. Comparison with intestinal permeability data revealed a number of significant correlations, which could merit further consideration in this context.
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Affiliation(s)
- Santosh D Bhosale
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Robert Moulder
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Terhi Ruohtula
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jarno Honkanen
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Suvi M Virtanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland
- Center for Child Health Research and Research, Development and Innovation Center, Tampere University Hospital, Tampere, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mikael Knip
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Center for Child Health Research and Research, Development and Innovation Center, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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4
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Pathmasiri W, Rushing BR, McRitchie S, Choudhari M, Du X, Smirnov A, Pelleigrini M, Thompson MJ, Sakaguchi CA, Nieman DC, Sumner SJ. Untargeted metabolomics reveal signatures of a healthy lifestyle. Sci Rep 2024; 14:13630. [PMID: 38871777 DOI: 10.1038/s41598-024-64561-z] [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: 02/17/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Blake R Rushing
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Susan McRitchie
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Mansi Choudhari
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Xiuxia Du
- College of Computing and Informatics, University of North Carolina at Charlotte, Kannapolis, NC, 28081, USA
| | - Alexsandr Smirnov
- College of Computing and Informatics, University of North Carolina at Charlotte, Kannapolis, NC, 28081, USA
| | - Matteo Pelleigrini
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael J Thompson
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Camila A Sakaguchi
- Human Performance Laboratory, Department of Biology, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA
| | - David C Nieman
- Human Performance Laboratory, Department of Biology, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA.
| | - Susan J Sumner
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA.
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5
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Mi Y, Burnham KL, Charles PD, Heilig R, Vendrell I, Whalley J, Torrance HD, Antcliffe DB, May SM, Neville MJ, Berridge G, Hutton P, Geoghegan CG, Radhakrishnan J, Nesvizhskii AI, Yu F, Davenport EE, McKechnie S, Davies R, O'Callaghan DJP, Patel P, Del Arroyo AG, Karpe F, Gordon AC, Ackland GL, Hinds CJ, Fischer R, Knight JC. High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response. Sci Transl Med 2024; 16:eadh0185. [PMID: 38838133 DOI: 10.1126/scitranslmed.adh0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.
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Affiliation(s)
- Yuxin Mi
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Katie L Burnham
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Philip D Charles
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Raphael Heilig
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Iolanda Vendrell
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Justin Whalley
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Hew D Torrance
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
| | - David B Antcliffe
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Shaun M May
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
| | - Georgina Berridge
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Paula Hutton
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK
| | - Cyndi G Geoghegan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Jayachandran Radhakrishnan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | | | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emma E Davenport
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Stuart McKechnie
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK
| | - Roger Davies
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
| | - David J P O'Callaghan
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Parind Patel
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Ana G Del Arroyo
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
| | - Anthony C Gordon
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Gareth L Ackland
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Charles J Hinds
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Roman Fischer
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
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6
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Newman NK, Macovsky MS, Rodrigues RR, Bruce AM, Pederson JW, Padiadpu J, Shan J, Williams J, Patil SS, Dzutsev AK, Shulzhenko N, Trinchieri G, Brown K, Morgun A. Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions. Nat Protoc 2024; 19:1750-1778. [PMID: 38472495 DOI: 10.1038/s41596-024-00960-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/29/2023] [Indexed: 03/14/2024]
Abstract
We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .
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Affiliation(s)
- Nolan K Newman
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | | | - Richard R Rodrigues
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Amanda M Bruce
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jacob W Pederson
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Jyothi Padiadpu
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jigui Shan
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Joshua Williams
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sankalp S Patil
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Amiran K Dzutsev
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Natalia Shulzhenko
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Giorgio Trinchieri
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Kevin Brown
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
| | - Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
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7
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Peng H, Wang H, Kong W, Li J, Goh WWB. Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference. Nat Commun 2024; 15:3922. [PMID: 38724498 PMCID: PMC11082229 DOI: 10.1038/s41467-024-47899-w] [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: 06/19/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.
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Affiliation(s)
- Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - He Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Jinyan Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore.
- Center of AI in Medicine, Nanyang Technological University, Singapore, Singapore.
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
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8
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Tsantilas KA, Merrihew GE, Robbins JE, Johnson RS, Park J, Plubell DL, Huang E, Riffle M, Sharma V, MacLean BX, Eckels J, Wu CC, Bereman MS, Spencer SE, Hoofnagle AN, MacCoss MJ. A framework for quality control in quantitative proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589318. [PMID: 38645098 PMCID: PMC11030400 DOI: 10.1101/2024.04.12.589318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.
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Affiliation(s)
- Kristine A. Tsantilas
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Julia E. Robbins
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Richard S. Johnson
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Jea Park
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Deanna L. Plubell
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Washington 98195, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Josh Eckels
- LabKey, 500 Union St #1000, Seattle, Washington 98101, United States
| | - Christine C. Wu
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael S. Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27607
| | - Sandra E. Spencer
- Canada’s Michael Smith Genome Sciences Centre (BC Cancer Research Institute), University of British Columbia, Vancouver, British Columbia V5Z 4S6, Canada
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Washington 98195, United States
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9
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De Silva S, Alli-Shaik A, Gunaratne J. FlexStat: combinatory differentially expressed protein extraction. BIOINFORMATICS ADVANCES 2024; 4:vbae056. [PMID: 38681522 PMCID: PMC11055397 DOI: 10.1093/bioadv/vbae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/25/2024] [Accepted: 04/10/2024] [Indexed: 05/01/2024]
Abstract
Motivation Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience. Results To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive datasets. Availability and implementation FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.
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Affiliation(s)
- Senuri De Silva
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore
| | - Asfa Alli-Shaik
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
| | - Jayantha Gunaratne
- Translational Biomedical Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138673, Singapore
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore
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10
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van Heugten MH, Blijdorp CJ, Arjune S, van Willigenburg H, Bezstarosti K, Demmers JA, Musterd-Bhaggoe U, Meijer E, Gansevoort RT, Zietse R, Hayat S, Kramann R, Müller RU, Salih M, Hoorn EJ. Matrix Metalloproteinase-7 in Urinary Extracellular Vesicles Identifies Rapid Disease Progression in Autosomal Dominant Polycystic Kidney Disease. J Am Soc Nephrol 2024; 35:321-334. [PMID: 38073039 PMCID: PMC10914202 DOI: 10.1681/asn.0000000000000277] [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: 05/17/2023] [Accepted: 11/12/2023] [Indexed: 03/02/2024] Open
Abstract
SIGNIFICANCE STATEMENT There is an unmet need for biomarkers of disease progression in autosomal dominant polycystic kidney disease (ADPKD). This study investigated urinary extracellular vesicles (uEVs) as a source of such biomarkers. Proteomic analysis of uEVs identified matrix metalloproteinase 7 (MMP-7) as a biomarker predictive of rapid disease progression. In validation studies, MMP-7 was predictive in uEVs but not in whole urine, possibly because uEVs are primarily secreted by tubular epithelial cells. Indeed, single-nucleus RNA sequencing showed that MMP-7 was especially increased in proximal tubule and thick ascending limb cells, which were further characterized by a profibrotic phenotype. Together, these data suggest that MMP-7 is a biologically plausible and promising uEV biomarker for rapid disease progression in ADPKD. BACKGROUND In ADPKD, there is an unmet need for early markers of rapid disease progression to facilitate counseling and selection for kidney-protective therapy. Our aim was to identify markers for rapid disease progression in uEVs. METHODS Six paired case-control groups ( n =10-59/group) of cases with rapid disease progression and controls with stable disease were formed from two independent ADPKD cohorts, with matching by age, sex, total kidney volume, and genetic variant. Candidate uEV biomarkers were identified by mass spectrometry and further analyzed using immunoblotting and an ELISA. Single-nucleus RNA sequencing of healthy and ADPKD tissue was used to identify the cellular origin of the uEV biomarker. RESULTS In the discovery proteomics experiments, the protein abundance of MMP-7 was significantly higher in uEVs of patients with rapid disease progression compared with stable disease. In the validation groups, a significant >2-fold increase in uEV-MMP-7 in patients with rapid disease progression was confirmed using immunoblotting. By contrast, no significant difference in MMP-7 was found in whole urine using ELISA. Compared with healthy kidney tissue, ADPKD tissue had significantly higher MMP-7 expression in proximal tubule and thick ascending limb cells with a profibrotic phenotype. CONCLUSIONS Among patients with ADPKD, rapid disease progressors have higher uEV-associated MMP-7. Our findings also suggest that MMP-7 is a biologically plausible biomarker for more rapid disease progression.
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Affiliation(s)
- Martijn H. van Heugten
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Charles J. Blijdorp
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Sita Arjune
- Department II of Internal Medicine and Center for Molecular Medicine, University of Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Faculty of Medicine and University Hospital Cologne, Center for Rare Diseases Cologne, University of Cologne, Cologne, Germany
| | - Hester van Willigenburg
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Karel Bezstarosti
- Proteomics Center, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Usha Musterd-Bhaggoe
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Esther Meijer
- Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
| | - Ron T. Gansevoort
- Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert Zietse
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Sikander Hayat
- Medical Faculty, Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Rafael Kramann
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Medical Faculty, Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Division of Nephrology, RWTH Aachen University, Aachen, Germany
| | - Roman-Ulrich Müller
- Department II of Internal Medicine and Center for Molecular Medicine, University of Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Faculty of Medicine and University Hospital Cologne, Center for Rare Diseases Cologne, University of Cologne, Cologne, Germany
| | - Mahdi Salih
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ewout J. Hoorn
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
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11
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Shi M, Evans CA, McQuillan JL, Noirel J, Pandhal J. LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems. J Proteome Res 2024; 23:999-1013. [PMID: 38354288 PMCID: PMC10913063 DOI: 10.1021/acs.jproteome.3c00714] [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: 02/16/2024]
Abstract
The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.
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Affiliation(s)
- Mengxun Shi
- Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K
| | - Caroline A Evans
- Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K
| | - Josie L McQuillan
- Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K
| | - Josselin Noirel
- GBCM Laboratory (EA7528), Conservatoire National des Arts et Métiers, HESAM Université, 2 rue Conté, Paris 75003, France
| | - Jagroop Pandhal
- Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K
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12
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Pettersen VK. Metaproteomics of Gut Fungi in Gnotobiotic Mice. Methods Mol Biol 2024; 2820:127-137. [PMID: 38941020 DOI: 10.1007/978-1-0716-3910-8_12] [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: 06/29/2024]
Abstract
Intestinal fungi are a fundamental component of the gut microbiome and play important roles in mammalian host biology. At the same time, the contribution of gut fungi to host health and disease remains understudied due to their low abundance. In that respect, gnotobiotic animals with defined microbial populations of reduced complexity represent a well-suited model system that highlights the effects of low abundant gut fungi on host physiology and other members of the microbial community. In this chapter, a label-free quantitative metaproteomic approach for the characterization of simplified microbial communities in gnotobiotic mice is presented. The model allows for exploring various research questions on the role of gut fungi in disease pathogenesis, microbial ecosystem maturation, or host-microbiome crosstalk.
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Affiliation(s)
- Veronika Kuchařová Pettersen
- Host-Microbe Interaction Research Group, Department of Medical Biology, UiT The Arctic University of Norway, Tromsø, Norway.
- Paediatric Research Group, Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
- Centre for New Antibacterial Strategies, UiT The Arctic University of Norway, Tromsø, Norway.
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13
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Punzalan C, Wang L, Bajrami B, Yao X. Measurement and utilization of the proteomic reactivity by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024; 43:166-192. [PMID: 36924435 DOI: 10.1002/mas.21837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.
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Affiliation(s)
- Clodette Punzalan
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
| | - Lei Wang
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- AD Bio US, Takeda, Lexington, Massachusetts, 02421, USA
| | - Bekim Bajrami
- Chemical Biology & Proteomics, Biogen, Cambridge, Massachusetts, USA
| | - Xudong Yao
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- Institute for Systems Biology, University of Connecticut, Storrs, Connecticut, USA
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14
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Abid MSR, Qiu H, Checco JW. Label-Free Quantitation of Endogenous Peptides. Methods Mol Biol 2024; 2758:125-150. [PMID: 38549012 PMCID: PMC11027169 DOI: 10.1007/978-1-0716-3646-6_7] [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: 04/02/2024]
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.
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Affiliation(s)
| | - Haowen Qiu
- Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, USA
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James W Checco
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
- The Nebraska Center for Integrated Biomolecular Communication (NCIBC), University of Nebraska-Lincoln, Lincoln, NE, USA.
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15
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Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [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: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
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Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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16
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Meyer Cifuentes IE, Degenhardt J, Neumann-Schaal M, Jehmlich N, Ngugi DK, Öztürk B. Comparative biodegradation analysis of three compostable polyesters by a marine microbial community. Appl Environ Microbiol 2023; 89:e0106023. [PMID: 38014952 PMCID: PMC10734441 DOI: 10.1128/aem.01060-23] [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: 07/03/2023] [Accepted: 09/20/2023] [Indexed: 11/29/2023] Open
Abstract
IMPORTANCE Biodegradable plastics can be used in applications where the end product cannot be efficiently recycled due to high levels of contaminations, e.g., food or soil. Some of these plastics have a dedicated end of life, such as composting, but their degradation in the marine environment is poorly understood. In this study we showed that marine microbial communities can degrade a range of biodegradable polymers with different physical and chemical properties and use these as a sole carbon source for growth. We have also provided insights into the degradation mechanisms using a combined metagenomic and metaproteomic approach. In addition, we have identified three new enzymes that are capable of degrading both aliphatic polymers and aliphatic-aromatic copolymers, which can be used for biotechnological applications.
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Affiliation(s)
- Ingrid E. Meyer Cifuentes
- Junior Research Group Microbial Biotechnology, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
| | - Julius Degenhardt
- Junior Research Group Microbial Biotechnology, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
| | - Meina Neumann-Schaal
- Research Group Metabolomics, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany
| | - David Kamanda Ngugi
- Department of Microorganisms, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
| | - Başak Öztürk
- Junior Research Group Microbial Biotechnology, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
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17
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Chua AE, Pfeifer L, Sekera ER, Hummon AB, Desaire H. Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2775-2784. [PMID: 37897440 PMCID: PMC10919320 DOI: 10.1021/jasms.3c00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a "DIY" aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.
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Affiliation(s)
- Aleesa E. Chua
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
| | - Leah Pfeifer
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
| | - Emily R. Sekera
- Department of Chemistry and Biochemistry and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Amanda B. Hummon
- Department of Chemistry and Biochemistry and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, Kansas, United States, 66045
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18
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Zhao T, Zeng J, Zhang R, Pu L, Wang H, Pan L, Jiang Y, Dai X, Sha Y, Han L. Proteomic advance of ischemic stroke: preclinical, clinical, and intervention. Metab Brain Dis 2023; 38:2521-2546. [PMID: 37440002 DOI: 10.1007/s11011-023-01262-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/01/2023] [Indexed: 07/14/2023]
Abstract
Ischemic stroke (IS) is the most common type of stroke and is characterized by high rates of mortality and long-term injury. The prediction and early diagnosis of IS are therefore crucial for optimal clinical intervention. Proteomics has provided important techniques for exploring protein markers associated with IS, but there has been no systematic evaluation and review of research that has used these techniques. Here, we review the differential proteins that have been found in cell- and animal- based studies and clinical trials of IS in the past 10 years; determine the key pathological proteins that have been identified in clinical trials; summarize the target proteins affected by interventions aimed at treating IS, with a focus on traditional Chinese medicine treatments. Overall, we clarify findings and problems that have been identified in recent proteomics research on IS and provide suggestions for improvements in this area. We also suggest areas that could be explored for determining the pathogenesis and developing interventions for IS.
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Affiliation(s)
- Tian Zhao
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Jingjing Zeng
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Ruijie Zhang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Liyuan Pu
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Han Wang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Lifang Pan
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Yannan Jiang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China
| | - Xiaoyu Dai
- Department of Anus & Intestine Surgery, Ningbo No.2 Hospital, Ningbo, 315000, China
| | - Yuyi Sha
- Department of Intensive Care Medicine, Ningbo No.2 Hospital, Ningbo, 315000, China.
| | - Liyuan Han
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, 41 Northwest Street, Ningbo, 315000, Zhejiang, China.
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, 315000, China.
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19
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Pi H, Sun R, McBride JR, Kruse ARS, Gibson-Corley KN, Krystofiak ES, Nicholson MR, Spraggins JM, Zhou Q, Skaar EP. Clostridioides difficile ferrosome organelles combat nutritional immunity. Nature 2023; 623:1009-1016. [PMID: 37968387 PMCID: PMC10822667 DOI: 10.1038/s41586-023-06719-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/05/2023] [Indexed: 11/17/2023]
Abstract
Iron is indispensable for almost all forms of life but toxic at elevated levels1-4. To survive within their hosts, bacterial pathogens have evolved iron uptake, storage and detoxification strategies to maintain iron homeostasis1,5,6. Recent studies showed that three Gram-negative environmental anaerobes produce iron-containing ferrosome granules7,8. However, it remains unclear whether ferrosomes are generated exclusively by Gram-negative bacteria. The Gram-positive bacterium Clostridioides difficile is the leading cause of nosocomial and antibiotic-associated infections in the USA9. Here we report that C. difficile undergoes an intracellular iron biomineralization process and stores iron in membrane-bound ferrosome organelles containing non-crystalline iron phosphate biominerals. We found that a membrane protein (FezA) and a P1B6-ATPase transporter (FezB), repressed by both iron and the ferric uptake regulator Fur, are required for ferrosome formation and play an important role in iron homeostasis during transition from iron deficiency to excess. Additionally, ferrosomes are often localized adjacent to cellular membranes as shown by cryo-electron tomography. Furthermore, using two mouse models of C. difficile infection, we demonstrated that the ferrosome system is activated in the inflamed gut to combat calprotectin-mediated iron sequestration and is important for bacterial colonization and survival during C. difficile infection.
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Affiliation(s)
- Hualiang Pi
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology, & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University School of Medicine, New Haven, CT, USA
| | - Rong Sun
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James R McBride
- Vanderbilt Institute of Nanoscale Science and Engineering, Vanderbilt University, Nashville, TN, USA
| | - Angela R S Kruse
- Mass Spectrometry Research Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Katherine N Gibson-Corley
- Department of Pathology, Microbiology, & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan S Krystofiak
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Maribeth R Nicholson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiangjun Zhou
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA.
- Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Center for Structural Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Eric P Skaar
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN, USA.
- Department of Pathology, Microbiology, & Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
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20
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Ewald J, Zhou G, Lu Y, Xia J. Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms. Curr Protoc 2023; 3:e922. [PMID: 37929753 DOI: 10.1002/cpz1.922] [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: 11/07/2023]
Abstract
ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.
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Affiliation(s)
- Jessica Ewald
- Institute of Parasitology, McGill University, Montreal, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, Canada
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21
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Moulder R, Välikangas T, Hirvonen MK, Suomi T, Brorsson CA, Lietzén N, Bruggraber SFA, Overbergh L, Dunger DB, Peakman M, Chmura PJ, Brunak S, Schulte AM, Mathieu C, Knip M, Elo LL, Lahesmaa R. Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory. Diabetologia 2023; 66:1983-1996. [PMID: 37537394 PMCID: PMC10542287 DOI: 10.1007/s00125-023-05974-9] [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: 03/10/2023] [Accepted: 06/06/2023] [Indexed: 08/05/2023]
Abstract
AIMS/HYPOTHESIS There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs). METHODS Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194). RESULTS Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family. CONCLUSIONS/INTERPRETATION The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.
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Affiliation(s)
- Robert Moulder
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - M Karoliina Hirvonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline A Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David B Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, Boston, MA, USA
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Soren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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22
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Unterberg M, Ehrentraut SF, Bracht T, Wolf A, Haberl H, von Busch A, Rump K, Ziehe D, Bazzi M, Thon P, Sitek B, Marcus K, Bayer M, Schork K, Eisenacher M, Ellger B, Oswald D, Wappler F, Defosse J, Henzler D, Köhler T, Zarbock A, Putensen CP, Schewe JC, Frey UH, Anft M, Babel N, Steinmann E, Brüggemann Y, Trilling M, Schlüter A, Nowak H, Adamzik M, Rahmel T, Koos B. Human cytomegalovirus seropositivity is associated with reduced patient survival during sepsis. Crit Care 2023; 27:417. [PMID: 37907989 PMCID: PMC10619294 DOI: 10.1186/s13054-023-04713-1] [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] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/26/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Sepsis is one of the leading causes of death. Treatment attempts targeting the immune response regularly fail in clinical trials. As HCMV latency can modulate the immune response and changes the immune cell composition, we hypothesized that HCMV serostatus affects mortality in sepsis patients. METHODS We determined the HCMV serostatus (i.e., latency) of 410 prospectively enrolled patients of the multicenter SepsisDataNet.NRW study. Patients were recruited according to the SEPSIS-3 criteria and clinical data were recorded in an observational approach. We quantified 13 cytokines at Days 1, 4, and 8 after enrollment. Proteomics data were analyzed from the plasma samples of 171 patients. RESULTS The 30-day mortality was higher in HCMV-seropositive patients than in seronegative sepsis patients (38% vs. 25%, respectively; p = 0.008; HR, 1.656; 95% CI 1.135-2.417). This effect was observed independent of age (p = 0.010; HR, 1.673; 95% CI 1.131-2.477). The predictive value on the outcome of the increased concentrations of IL-6 was present only in the seropositive cohort (30-day mortality, 63% vs. 24%; HR 3.250; 95% CI 2.075-5.090; p < 0.001) with no significant differences in serum concentrations of IL-6 between the two groups. Procalcitonin and IL-10 exhibited the same behavior and were predictive of the outcome only in HCMV-seropositive patients. CONCLUSION We suggest that the predictive value of inflammation-associated biomarkers should be re-evaluated with regard to the HCMV serostatus. Targeting HCMV latency might open a new approach to selecting suitable patients for individualized treatment in sepsis.
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Affiliation(s)
- M Unterberg
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - S F Ehrentraut
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - T Bracht
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
| | - A Wolf
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - H Haberl
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - A von Busch
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - K Rump
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - D Ziehe
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - M Bazzi
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - P Thon
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - B Sitek
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
| | - K Marcus
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
- Medical Proteome Analysis, Center for Proteindiagnostics (PRODI), Ruhr University Bochum, 44801, Bochum, Germany
| | - M Bayer
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
| | - K Schork
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
- Medical Proteome Analysis, Center for Proteindiagnostics (PRODI), Ruhr University Bochum, 44801, Bochum, Germany
| | - M Eisenacher
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801, Bochum, Germany
- Medical Proteome Analysis, Center for Proteindiagnostics (PRODI), Ruhr University Bochum, 44801, Bochum, Germany
| | - B Ellger
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Klinikum Westfalen, Dortmund, Germany
| | - D Oswald
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Klinikum Westfalen, Dortmund, Germany
| | - F Wappler
- Department of Anaesthesiology and Operative Intensive Care Medicine, University of Witten/Herdecke, Cologne Merheim Medical School, Cologne, Germany
| | - J Defosse
- Department of Anaesthesiology and Operative Intensive Care Medicine, University of Witten/Herdecke, Cologne Merheim Medical School, Cologne, Germany
| | - D Henzler
- Department of Anesthesiology, Surgical Intensive Care, Emergency and Pain Medicine, Ruhr-University Bochum, Klinikum Herford, Herford, Germany
| | - T Köhler
- Department of Anesthesiology, Surgical Intensive Care, Emergency and Pain Medicine, Ruhr-University Bochum, Klinikum Herford, Herford, Germany
- Department of Anesthesiology and Intensive Care Medicine, AMEOS-Klinikum Halberstadt, Halberstadt, Germany
| | - A Zarbock
- Klinik für Anästhesiologie, Operative Intensivmedizin und Schmerztherapie, Universitätsklinikum Münster, Münster, Germany
| | - C P Putensen
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - J C Schewe
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - U H Frey
- Marien Hospital Herne, Universitätsklinikum der Ruhr-Universität Bochum, Bochum, Germany
| | - M Anft
- Center for Translational Medicine, Medical Clinic I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - N Babel
- Center for Translational Medicine, Medical Clinic I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - E Steinmann
- Department of Molecular and Medical Virology, Ruhr University Bochum, 44801, Bochum, Germany
| | - Y Brüggemann
- Department of Molecular and Medical Virology, Ruhr University Bochum, 44801, Bochum, Germany
| | - M Trilling
- Institute for Virology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - A Schlüter
- Knappschaft Kliniken GmbH, Recklinghausen, Germany
| | - H Nowak
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
- Center for Artficial Intelligence, Medical Informatics and Data Science, University Hospital Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - M Adamzik
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - T Rahmel
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - B Koos
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany.
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23
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Wang E, Pan AL, Bagchi P, Ranjaraju S, Seyfried NT, Ehrlich ME, Salton SR, Zhang B. Proteomic signaling of dual specificity phosphatase 4 (DUSP4) in Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-3453503. [PMID: 37886598 PMCID: PMC10602176 DOI: 10.21203/rs.3.rs-3453503/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
DUSP4 is a member of the DUSP (Dual-Specificity Phosphatase) subfamily that is selective to the mitogen-activated protein kinases (MAPK) and has been implicated in a range of biological processes and functions in Alzheimer's disease (AD). In this study, we utilized stereotactic delivery of adeno-associated virus (AAV)-DUSP4 to overexpress DUSP4 in the dorsal hippocampus of 5xFAD and wildtype (WT) mice, then used mass spectrometry (MS)-based proteomics along with label-free quantification to profile the proteome and phosphoproteome in the hippocampus. We identified patterns of protein expression and phosphorylation that are modulated in 5xFAD mice and examined the sex-specific impact of DUSP4 overexpression on the 5xFAD proteome/phosphoproteome. In 5xFAD mice, a substantial number of proteins were up- or down-regulated in both male and female mice in comparison to age and sex-matched WT mice, many of which are involved in AD-related biological processes, such as the activated immune response or suppression of synaptic activities. Upon DUSP4 overexpression, significantly regulated proteins were found in pathways that were suppressed, such as the immune response, in male 5xFAD mice. In contrast, such a shift was absent in female mice. For the phosphoproteome, we detected an array of phosphorylation sites that are regulated in 5xFAD compared to WT, and are modulated by DUSP4 overexpression in each sex. Interestingly, the changes in 5xFAD- and DUSP4-associated phosphorylation occurred in opposite directions. Strikingly, both the 5xFAD- and DUSP4-associated phosphorylation changes were found for the most part in neurons, and play key roles in neuronal processes and synaptic function. Site-centric pathway analysis revealed that both the 5xFAD- and DUSP4-associated phosphorylation sites were enriched for a number of kinase sets in female, but only a limited number of sets of kinases in male mice. Taken together, our results suggest that male and female 5xFAD mice respond to DUSP4 overexpression via shared and sex-specific molecular mechanisms, which might underly similar reductions in amyloid pathology in both sexes, while learning deficits were reduced in only females with DUSP4 overexpression. Finally, we validated our findings with the sex-specific AD-associated proteomes in human cohorts and further developed DUSP4-centric proteomic network models and signaling maps for each sex.
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Affiliation(s)
| | | | | | | | | | | | | | - Bin Zhang
- Icahn School of Medicine at Mount Sinai
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24
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Wang E, Pan AL, Bagchi P, Ranjaraju S, Seyfried NT, Ehrlich ME, Salton SR, Zhang B. Proteomic signaling of dual specificity phosphatase 4 (DUSP4) in Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.557390. [PMID: 37745468 PMCID: PMC10515873 DOI: 10.1101/2023.09.13.557390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
DUSP4 is a member of the DUSP (Dual-Specificity Phosphatase) subfamily that is selective to the mitogen-activated protein kinases (MAPK) and has been implicated in a range of biological processes and functions in Alzheimer's disease (AD). In this study, we utilized stereotactic delivery of adeno-associated virus (AAV)-DUSP4 to overexpress DUSP4 in the dorsal hippocampus of 5×FAD and wildtype (WT) mice, then used mass spectrometry (MS)-based proteomics along with label-free quantification to profile the proteome and phosphoproteome in the hippocampus. We identified patterns of protein expression and phosphorylation that are modulated in 5×FAD mice and examined the sex-specific impact of DUSP4 overexpression on the 5×FAD proteome/phosphoproteome. In 5×FAD mice, a substantial number of proteins were up- or down-regulated in both male and female mice in comparison to age and sex-matched WT mice, many of which are involved in AD-related biological processes, such as the activated immune response or suppression of synaptic activities. Upon DUSP4 overexpression, significantly regulated proteins were found in pathways that were suppressed, such as the immune response, in male 5×FAD mice. In contrast, such a shift was absent in female mice. For the phosphoproteome, we detected an array of phosphorylation sites that are regulated in 5×FAD compared to WT, and are modulated by DUSP4 overexpression in each sex. Interestingly, the changes in 5×FAD- and DUSP4-associated phosphorylation occurred in opposite directions. Strikingly, both the 5×FAD- and DUSP4-associated phosphorylation changes were found for the most part in neurons, and play key roles in neuronal processes and synaptic function. Site-centric pathway analysis revealed that both the 5×FAD- and DUSP4-associated phosphorylation sites were enriched for a number of kinase sets in female, but only a limited number of sets of kinases in male mice. Taken together, our results suggest that male and female 5×FAD mice respond to DUSP4 overexpression via shared and sex-specific molecular mechanisms, which might underly similar reductions in amyloid pathology in both sexes, while learning deficits were reduced in only females with DUSP4 overexpression. Finally, we validated our findings with the sex-specific AD-associated proteomes in human cohorts and further developed DUSP4-centric proteomic network models and signaling maps for each sex.
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25
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Zhang H, Steele JR, Kahrood HV, Lucas DD, Shah AD, Schittenhelm RB. Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data. J Proteome Res 2023; 22:2890-2899. [PMID: 37584946 DOI: 10.1021/acs.jproteome.3c00186] [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: 08/17/2023]
Abstract
Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.
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Affiliation(s)
- Haijian Zhang
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Joel R Steele
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Hossein Valipour Kahrood
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Deanna Deveson Lucas
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anup D Shah
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Platform, Monash Biomedicine Discovery Institute & Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
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26
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Hardt R, Dehghani A, Schoor C, Gödderz M, Cengiz Winter N, Ahmadi S, Sharma R, Schork K, Eisenacher M, Gieselmann V, Winter D. Proteomic investigation of neural stem cell to oligodendrocyte precursor cell differentiation reveals phosphorylation-dependent Dclk1 processing. Cell Mol Life Sci 2023; 80:260. [PMID: 37594553 PMCID: PMC10439241 DOI: 10.1007/s00018-023-04892-8] [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: 04/21/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 08/19/2023]
Abstract
Oligodendrocytes are generated via a two-step mechanism from pluripotent neural stem cells (NSCs): after differentiation of NSCs to oligodendrocyte precursor/NG2 cells (OPCs), they further develop into mature oligodendrocytes. The first step of this differentiation process is only incompletely understood. In this study, we utilized the neurosphere assay to investigate NSC to OPC differentiation in a time course-dependent manner by mass spectrometry-based (phospho-) proteomics. We identify doublecortin-like kinase 1 (Dclk1) as one of the most prominently regulated proteins in both datasets, and show that it undergoes a gradual transition between its short/long isoform during NSC to OPC differentiation. This is regulated by phosphorylation of its SP-rich region, resulting in inhibition of proteolytic Dclk1 long cleavage, and therefore Dclk1 short generation. Through interactome analyses of different Dclk1 isoforms by proximity biotinylation, we characterize their individual putative interaction partners and substrates. All data are available via ProteomeXchange with identifier PXD040652.
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Affiliation(s)
- Robert Hardt
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
| | - Alireza Dehghani
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
- Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach, Germany
| | - Carmen Schoor
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
| | - Markus Gödderz
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
| | - Nur Cengiz Winter
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
- Institute of Human Genetics, University Hospital Cologne, 50931, Cologne, Germany
| | - Shiva Ahmadi
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
- Bayer Pharmaceuticals, 42113, Wuppertal, Germany
| | - Ramesh Sharma
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
| | - Karin Schork
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, 44801, Bochum, Germany
- Medical Proteome Analysis, Center for Protein Diagnostics, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Martin Eisenacher
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, 44801, Bochum, Germany
- Medical Proteome Analysis, Center for Protein Diagnostics, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Volkmar Gieselmann
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany
| | - Dominic Winter
- Institute for Biochemistry and Molecular Biology, Medical Faculty, University of Bonn, Nussallee 11, 53115, Bonn, Germany.
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27
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Faravelli I, Gagliardi D, Abati E, Meneri M, Ongaro J, Magri F, Parente V, Petrozzi L, Ricci G, Farè F, Garrone G, Fontana M, Caruso D, Siciliano G, Comi GP, Govoni A, Corti S, Ottoboni L. Multi-omics profiling of CSF from spinal muscular atrophy type 3 patients after nusinersen treatment: a 2-year follow-up multicenter retrospective study. Cell Mol Life Sci 2023; 80:241. [PMID: 37543540 PMCID: PMC10404194 DOI: 10.1007/s00018-023-04885-7] [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: 04/02/2023] [Revised: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 08/07/2023]
Abstract
Spinal muscular atrophy (SMA) is a neurodegenerative disorder caused by mutations in the SMN1 gene resulting in reduced levels of the SMN protein. Nusinersen, the first antisense oligonucleotide (ASO) approved for SMA treatment, binds to the SMN2 gene, paralogue to SMN1, and mediates the translation of a functional SMN protein. Here, we used longitudinal high-resolution mass spectrometry (MS) to assess both global proteome and metabolome in cerebrospinal fluid (CSF) from ten SMA type 3 patients, with the aim of identifying novel readouts of pharmacodynamic/response to treatment and predictive markers of treatment response. Patients had a median age of 33.5 [29.5; 38.25] years, and 80% of them were ambulant at time of the enrolment, with a median HFMSE score of 37.5 [25.75; 50.75]. Untargeted CSF proteome and metabolome were measured using high-resolution MS (nLC-HRMS) on CSF samples obtained before treatment (T0) and after 2 years of follow-up (T22). A total of 26 proteins were found to be differentially expressed between T0 and T22 upon VSN normalization and LIMMA differential analysis, accounting for paired replica. Notably, key markers of the insulin-growth factor signaling pathway were upregulated after treatment together with selective modulation of key transcription regulators. Using CombiROC multimarker signature analysis, we suggest that detecting a reduction of SEMA6A and an increase of COL1A2 and GRIA4 might reflect therapeutic efficacy of nusinersen. Longitudinal metabolome profiling, analyzed with paired t-Test, showed a significant shift for some aminoacid utilization induced by treatment, whereas other metabolites were largely unchanged. Together, these data suggest perturbation upon nusinersen treatment still sustained after 22 months of follow-up and confirm the utility of CSF multi-omic profiling as pharmacodynamic biomarker for SMA type 3. Nonetheless, validation studies are needed to confirm this evidence in a larger sample size and to further dissect combined markers of response to treatment.
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Affiliation(s)
- Irene Faravelli
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy.
| | - Delia Gagliardi
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Elena Abati
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Megi Meneri
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Jessica Ongaro
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Francesca Magri
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Valeria Parente
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Lucia Petrozzi
- Department of Clinical and Experimental Medicine, Neurological Clinics, University of Pisa, Pisa, Italy
| | - Giulia Ricci
- Department of Clinical and Experimental Medicine, Neurological Clinics, University of Pisa, Pisa, Italy
| | | | | | | | - Donatella Caruso
- Unitech OMICs, University of Milan, Milan, Italy
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Gabriele Siciliano
- Department of Clinical and Experimental Medicine, Neurological Clinics, University of Pisa, Pisa, Italy
| | - Giacomo Pietro Comi
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alessandra Govoni
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy
| | - Stefania Corti
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy.
- Neurology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Linda Ottoboni
- Department of Pathophysiology and Transplantation (DEPT), Dino Ferrari Centre, University of Milan, Milan, Italy.
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28
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Sychev ZE, Day A, Bergom HE, Larson G, Ali A, Ludwig M, Boytim E, Coleman I, Corey E, Plymate SR, Nelson PS, Hwang JH, Drake JM. Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.02.551697. [PMID: 37577653 PMCID: PMC10418188 DOI: 10.1101/2023.08.02.551697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.
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Affiliation(s)
- Zoi E. Sychev
- Department of Pharmacology, University of Minnesota, Minneapolis, MN, University of Minnesota, Minneapolis, MN
| | - Abderrahman Day
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN
| | - Hannah E. Bergom
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN
| | - Gabrianne Larson
- Department of Pharmacology, University of Minnesota, Minneapolis, MN, University of Minnesota, Minneapolis, MN
| | - Atef Ali
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN
| | - Megan Ludwig
- Department of Pharmacology, University of Minnesota, Minneapolis, MN, University of Minnesota, Minneapolis, MN
| | - Ella Boytim
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN
| | | | - Eva Corey
- Depart of Urology, University of Washington, Seattle, WA
| | - Stephen R. Plymate
- Division of gerontology and Geriatrics Medicine, University of Washington, Seattle, WA
| | | | - Justin H. Hwang
- Department of Medicine, University of Minnesota Masonic Cancer Center, Minneapolis, MN
- Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN
| | - Justin M. Drake
- Department of Pharmacology, University of Minnesota, Minneapolis, MN, University of Minnesota, Minneapolis, MN
- Department of Urology, University of Minnesota, Minneapolis, MN
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN
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29
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Serrano-Villar S, Tincati C, Raju SC, Sáenz JS, Moreno E, Bargiela R, Cabello-Ubeda A, Sendagorta E, Kurz A, Perez Molina JA, de Benito A, Hov JR, Fernandez-Lopez L, Muriel A, Del Campo R, Moreno S, Trøseid M, Seifert J, Ferrer M. Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer. Nat Med 2023; 29:1738-1749. [PMID: 37464040 DOI: 10.1038/s41591-023-02407-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/18/2023] [Indexed: 07/20/2023]
Abstract
Human papillomavirus can cause preinvasive, high-grade squamous intraepithelial lesions (HSILs) as precursors to cancer in the anogenital area, and the microbiome is suggested to be a contributing factor. Men who have sex with men (MSM) living with human immunodeficiency virus (HIV) have a high risk of anal cancer, but current screening strategies for HSIL detection lack specificity. Here, we investigated the anal microbiome to improve HSIL screening. We enrolled participants living with HIV, divided into a discovery (n = 167) and validation cohort (n = 46), and who were predominantly (93.9%) cisgender MSM undergoing HSIL screening with high-resolution anoscopy and anal biopsies. We identified no microbiome composition signatures associated with HSILs, but elevated levels of microbiome-encoded proteins producing succinyl coenzyme A and cobalamin were significantly associated with HSILs in both cohorts. Measurement of these candidate biomarkers alone in anal cytobrushes outperformed anal cytology as a diagnostic indicator for HSILs, increasing the sensitivity from 91.2% to 96.6%, the specificity from 34.1% to 81.8%, and reclassifying 82% of false-positive results as true negatives. We propose that these two microbiome-derived biomarkers may improve the current strategy of anal cancer screening.
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Affiliation(s)
- Sergio Serrano-Villar
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain.
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain.
| | - Camilla Tincati
- Department of Health Sciences, Clinic of Infectious Diseases, ASST Santi Paolo e Carlo, Presidio Ospedaliero San Paolo, University of Milan, Milan, Italy
| | - Sajan C Raju
- Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Johan S Sáenz
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Elena Moreno
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Bargiela
- Centre for Environmental Biotechnology, Bangor University, Bangor, UK
| | - Alfonso Cabello-Ubeda
- Department of Infectious Diseases, IIS-FJD, Hospital Universitario Fundación Jiménez Diaz, Madrid, Spain
| | - Elena Sendagorta
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
- Department of Dermatology, Hospital Universitario La Paz-IdiPaz, Madrid, Spain
| | - Alina Kurz
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Jose A Perez Molina
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Amparo de Benito
- Department of Pathology, IRYCIS, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Johannes R Hov
- Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Norwegian PSC Research Center and Section of Gastroenterology and Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Alfonso Muriel
- Biostatistics Unit, IRYCIS, Hospital Universitario Ramón y Cajal, CIBERESP, Universidad de Alcalá, Madrid, Spain
| | - Rosa Del Campo
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
- Department of Microbiology, IRYCIS, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Santiago Moreno
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain
- CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Marius Trøseid
- Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Section for Clinical Immunology and Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Jana Seifert
- Institute of Animal Science, University of Hohenheim, Stuttgart, Germany
| | - Manuel Ferrer
- Instituto de Catalisis y Petroleoquimica (ICP), CSIC, Madrid, Spain
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30
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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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31
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Maxwell CB, Sandhu JK, Cao TH, McCann GP, Ng LL, Jones DJL. The Edge Effect in High-Throughput Proteomics: A Cautionary Tale. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37155737 DOI: 10.1021/jasms.3c00035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.
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Affiliation(s)
- Colleen B Maxwell
- The Leicester van Geest MultiOmics Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Jatinderpal K Sandhu
- The Leicester van Geest MultiOmics Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Thong H Cao
- The Leicester van Geest MultiOmics Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Gerry P McCann
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Leong L Ng
- The Leicester van Geest MultiOmics Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Donald J L Jones
- The Leicester van Geest MultiOmics Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
- Leicester Cancer Research Centre, RKCSB, University of Leicester, Leicester LE2 7LX, United Kingdom
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32
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Fennell EMJ, Aponte-Collazo LJ, Pathmasiri W, Rushing BR, Barker NK, Partridge MC, Li YY, White CA, Greer YE, Herring LE, Lipkowitz S, Sumner SCJ, Iwanowicz EJ, Graves LM. Multi-omics analyses reveal ClpP activators disrupt essential mitochondrial pathways in triple-negative breast cancer. Front Pharmacol 2023; 14:1136317. [PMID: 37063293 PMCID: PMC10103842 DOI: 10.3389/fphar.2023.1136317] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/27/2023] [Indexed: 04/03/2023] Open
Abstract
ClpP activators ONC201 and related small molecules (TR compounds, Madera Therapeutics), have demonstrated significant anti-cancer potential in vitro and in vivo studies, including clinical trials for refractory solid tumors. Though progress has been made in identifying specific phenotypic outcomes following ClpP activation, the exact mechanism by which ClpP activation leads to broad anti-cancer activity has yet to be fully elucidated. In this study, we utilized a multi-omics approach to identify the ClpP-dependent proteomic, transcriptomic, and metabolomic changes resulting from ONC201 or the TR compound TR-57 in triple-negative breast cancer cells. Applying mass spectrometry-based methods of proteomics and metabolomics, we identified ∼8,000 proteins and 588 metabolites, respectively. From proteomics data, 113 (ONC201) and 191 (TR-57) proteins significantly increased and 572 (ONC201) and 686 (TR-57) proteins significantly decreased in this study. Gene ontological (GO) analysis revealed strong similarities between proteins up- or downregulated by ONC201 or TR-57 treatment. Notably, this included the downregulation of many mitochondrial processes and proteins, including mitochondrial translation and mitochondrial matrix proteins. We performed a large-scale transcriptomic analysis of WT SUM159 cells, identifying ∼7,700 transcripts (746 and 1,100 significantly increasing, 795 and 1,013 significantly decreasing in ONC201 and TR-57 treated cells, respectively). Less than 21% of these genes were affected by these compounds in ClpP null cells. GO analysis of these data demonstrated additional similarity of response to ONC201 and TR-57, including a decrease in transcripts related to the mitochondrial inner membrane and matrix, cell cycle, and nucleus, and increases in other nuclear transcripts and transcripts related to metal-ion binding. Comparison of response between both compounds demonstrated a highly similar response in all -omics datasets. Analysis of metabolites also revealed significant similarities between ONC201 and TR-57 with increases in α-ketoglutarate and 2-hydroxyglutaric acid and decreased ureidosuccinic acid, L-ascorbic acid, L-serine, and cytidine observed following ClpP activation in TNBC cells. Further analysis identified multiple pathways that were specifically impacted by ClpP activation, including ATF4 activation, heme biosynthesis, and the citrulline/urea cycle. In summary the results of our studies demonstrate that ONC201 and TR-57 induce highly similar and broad effects against multiple mitochondrial processes required for cell proliferation.
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Affiliation(s)
- Emily M. J. Fennell
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Lucas J. Aponte-Collazo
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Wimal Pathmasiri
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Blake R. Rushing
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Natalie K. Barker
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Michael Hooker Proteomics Core Facility, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Megan C. Partridge
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yuan-Yuan Li
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | - Cody A. White
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yoshimi E. Greer
- Women’s Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Laura E. Herring
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Michael Hooker Proteomics Core Facility, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stanley Lipkowitz
- Women’s Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Susan C. J. Sumner
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, United States
| | | | - Lee M. Graves
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- *Correspondence: Lee M. Graves,
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33
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Newman NK, Macovsky M, Rodrigues RR, Bruce AM, Pederson JW, Patil SS, Padiadpu J, Dzutsev AK, Shulzhenko N, Trinchieri G, Brown K, Morgun A. Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529449. [PMID: 36865280 PMCID: PMC9980039 DOI: 10.1101/2023.02.22.529449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.
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Affiliation(s)
- Nolan K Newman
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Matthew Macovsky
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Richard R Rodrigues
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Amanda M Bruce
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jacob W Pederson
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR
| | - Sankalp S Patil
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jyothi Padiadpu
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Amiran K Dzutsev
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Natalia Shulzhenko
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR
| | - Giorgio Trinchieri
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Kevin Brown
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
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34
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Wolski WE, Nanni P, Grossmann J, d'Errico M, Schlapbach R, Panse C. prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis. J Proteome Res 2023; 22:1092-1104. [PMID: 36939687 PMCID: PMC10088014 DOI: 10.1021/acs.jproteome.2c00441] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.
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Affiliation(s)
- Witold E Wolski
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Paolo Nanni
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Jonas Grossmann
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Maria d'Errico
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Ralph Schlapbach
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
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35
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Sundararaman N, Bhat A, Venkatraman V, Binek A, Dwight Z, Ariyasinghe NR, Escopete S, Joung SY, Cheng S, Parker SJ, Fert-Bober J, Van Eyk JE. BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data. J Proteome Res 2023; 22:471-481. [PMID: 36695565 DOI: 10.1021/acs.jproteome.2c00671] [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: 01/26/2023]
Abstract
Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.
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Affiliation(s)
- Niveda Sundararaman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Archana Bhat
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Vidya Venkatraman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Aleksandra Binek
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Zachary Dwight
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Nethika R Ariyasinghe
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sean Escopete
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sandy Y Joung
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sarah J Parker
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Justyna Fert-Bober
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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36
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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37
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Jung JW, Hwang YJ, Suh MW, Han D, Oh SH. Intratympanic steroid treatment can reduce ROS and immune response in human perilymph investigated by in-depth proteome analysis. Proteomics 2023; 23:e2200211. [PMID: 36259158 DOI: 10.1002/pmic.202200211] [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: 05/18/2022] [Revised: 10/11/2022] [Accepted: 10/11/2022] [Indexed: 01/05/2023]
Abstract
Intratympanic (IT) steroid treatment is one of the most widely used and effective treatments for inner ear disorders such as sudden sensorineural hearing loss (SNHL). However, a clear mechanism of IT steroids in inner ear recovery has not yet been revealed. Therefore, we investigated proteome changes in extracted human perilymph after steroid treatment. In this study, we applied a tandem mass spectrometry (MS/MS)-based proteomics approach to discover global proteome changes by comparing human perilymph after steroid treatment with non-treated perilymph group. Using liquid chromatography-MS/MS analysis, we selected 156 differentially expressed proteins (DEPs) that were statistically significant according to Student's t-test. Functional annotation analysis showed that upregulated proteins after steroid treatment are related to apoptosis signaling, as well as reactive oxygen species (ROS) and immune responses. The protein-protein interaction (PPI) clusters the proteins associated with these processes and attempts to observe signaling circuitry, which mediates cellular response after IT steroid treatments. Moreover, we also considered the interactome analysis of DEPs and observed that those with high interaction scores were categorized as having equivalent molecular functions (MFs). Collectively, we suggest that DEPs and interacting proteins in human perilymph after steroid treatment would inhibit the apoptotic and adaptive immune processes that may lead to anti-inflammatory effects.
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Affiliation(s)
- Jin Woo Jung
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Yu-Jung Hwang
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, South Korea
| | - Myung-Whan Suh
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, South Korea
| | - Dohyun Han
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea.,Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Korea
| | - Seung Ha Oh
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, South Korea
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38
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Abstract
The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.
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Affiliation(s)
- Zoe Welham
- Kolling Institute, St Leonards, NSW, Australia
| | - Sébastien Déjean
- Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS, Université Paul Sabatier, Toulouse, France
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia.
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39
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Koopmans F, Li KW, Klaassen RV, Smit AB. MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data. J Proteome Res 2022; 22:374-386. [PMID: 36541440 PMCID: PMC9903323 DOI: 10.1021/acs.jproteome.2c00513] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.
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Affiliation(s)
- Frank Koopmans
- Department
of Molecular and Cellular Neurobiology, Center for Neurogenomics and
Cognitive Research, Amsterdam Neuroscience, VU University, 1081 HV Amsterdam, The Netherlands,
| | - Ka Wan Li
- Department
of Molecular and Cellular Neurobiology, Center for Neurogenomics and
Cognitive Research, Amsterdam Neuroscience, VU University, 1081 HV Amsterdam, The Netherlands
| | - Remco V. Klaassen
- Department
of Molecular and Cellular Neurobiology, Center for Neurogenomics and
Cognitive Research, Amsterdam Neuroscience, VU University, 1081 HV Amsterdam, The Netherlands
| | - August B. Smit
- Department
of Molecular and Cellular Neurobiology, Center for Neurogenomics and
Cognitive Research, Amsterdam Neuroscience, VU University, 1081 HV Amsterdam, The Netherlands
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40
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Tan TCJ, Kelly V, Zou X, Wright D, Ly T, Zamoyska R. Translation factor eIF5a is essential for IFNγ production and cell cycle regulation in primary CD8 + T lymphocytes. Nat Commun 2022; 13:7796. [PMID: 36528626 PMCID: PMC9759561 DOI: 10.1038/s41467-022-35252-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
Control of mRNA translation adjusts protein production rapidly and facilitates local cellular responses to environmental conditions. Traditionally initiation of translation is considered to be a major translational control point, however, control of peptide elongation is also important. Here we show that the function of the elongation factor, eIF5a, is regulated dynamically in naïve CD8+ T cells upon activation by post-translational modification, whereupon it facilitates translation of specific subsets of proteins. eIF5a is essential for long-term survival of effector CD8+ T cells and sequencing of nascent polypeptides indicates that the production of proteins which regulate proliferation and key effector functions, particularly the production of IFNγ and less acutely TNF production and cytotoxicity, is dependent on the presence of functional eIF5a. Control of translation in multiple immune cell lineages is required to co-ordinate immune responses and these data illustrate that translational elongation contributes to post-transcriptional regulons important for the control of inflammation.
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Affiliation(s)
- Thomas C J Tan
- Institute of Immunology and Infection Research, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - Van Kelly
- Wellcome Trust Centre for Cell Biology, University of Edinburgh, Michael Swann Building, Max Born Crescent, Edinburgh, EH9 3BF, UK
| | - Xiaoyan Zou
- Institute of Immunology and Infection Research, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - David Wright
- Institute of Immunology and Infection Research, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - Tony Ly
- Wellcome Trust Centre for Cell Biology, University of Edinburgh, Michael Swann Building, Max Born Crescent, Edinburgh, EH9 3BF, UK
- Centre for Gene Regulation and Expression, Life Sciences Research Complex, University of Dundee, Dundee, DD1 5EH, UK
| | - Rose Zamoyska
- Institute of Immunology and Infection Research, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK.
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41
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Gavin PG, Kim KW, Craig ME, Hill MM, Hamilton-Williams EE. Multi-omic interactions in the gut of children at the onset of islet autoimmunity. MICROBIOME 2022; 10:230. [PMID: 36527134 PMCID: PMC9756488 DOI: 10.1186/s40168-022-01425-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The gastrointestinal ecosystem is a highly complex environment with a profound influence on human health. Inflammation in the gut, linked to an altered gut microbiome, has been associated with the development of multiple human conditions including type 1 diabetes (T1D). Viruses infecting the gastrointestinal tract, especially enteroviruses, are also thought to play an important role in T1D pathogenesis possibly via overlapping mechanisms. However, it is not known whether the microbiome and virome act together or which risk factor may be of greater importance at the time when islet autoimmunity is initiated. RESULTS Here, we apply an integrative approach to combine comprehensive fecal virome, microbiome, and metaproteome data sampled before and at the onset of islet autoimmunity in 40 children at increased risk of T1D. We show strong age-related effects, with microbial and metaproteome diversity increasing with age while host antibody number and abundance declined with age. Mastadenovirus, which has been associated with a reduced risk of T1D, was associated with profound changes in the metaproteome indicating a functional shift in the microbiota. Multi-omic factor analysis modeling revealed a cluster of proteins associated with carbohydrate transport from the genus Faecalibacterium were associated with islet autoimmunity. CONCLUSIONS These findings demonstrate the interrelatedness of the gut microbiota, metaproteome and virome in young children. We show a functional remodeling of the gut microbiota accompanies both islet autoimmunity and viral infection with a switch in function in Faecalibacterium occurring at the onset of islet autoimmunity. Video Abstract.
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Affiliation(s)
- Patrick G Gavin
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Present Address: Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Present Address: Harvard Medical School, Boston, MA, USA
| | - Ki Wook Kim
- Virology Research Laboratory, Prince of Wales Hospital Randwick, Sydney, Australia
- School of Clinical Medicine, Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia
| | - Maria E Craig
- Virology Research Laboratory, Prince of Wales Hospital Randwick, Sydney, Australia
- School of Clinical Medicine, Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia
- Institute of Endocrinology and Diabetes, Children's Hospital at Westmead, Sydney, Australia
- Discipline of Child and Adolescent Health, University of Sydney, Sydney, Australia
| | - Michelle M Hill
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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42
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Rushing BR, Fogle HM, Sharma J, You M, McCormac JP, Molina S, Sumner S, Krupenko NI, Krupenko SA. Exploratory Metabolomics Underscores the Folate Enzyme ALDH1L1 as a Regulator of Glycine and Methylation Reactions. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238394. [PMID: 36500483 PMCID: PMC9740053 DOI: 10.3390/molecules27238394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
Folate (vitamin B9) is involved in one-carbon transfer reactions and plays a significant role in nucleic acid synthesis and control of cellular proliferation, among other key cellular processes. It is now recognized that the role of folates in different stages of carcinogenesis is complex, and more research is needed to understand how folate reactions become dysregulated in cancers and the metabolic consequences that occur as a result. ALDH1L1 (cytosolic 10-formyltetrahydrofolate dehydrogenase), an enzyme of folate metabolism expressed in many tissues, is ubiquitously downregulated in cancers and is not expressed in cancer cell lines. The RT4 cell line (derived from papillary bladder cancer) which expresses high levels of ALDH1L1 represents an exception, providing an opportunity to explore the metabolic consequences of the loss of this enzyme. We have downregulated this protein in RT4 cells (shRNA driven knockdown or CRISPR driven knockout) and compared metabolomes of ALDH1L1-expressing and -deficient cells to determine if metabolic changes linked to the loss of this enzyme might provide proliferative and/or survival advantages for cancer cells. In this study, cell extracts were analyzed using Ultra High Performance Liquid Chromatography High Resolution Mass Spectrometry (UHPLC-HR-MS). A total of 13,339 signals were identified or annotated using an in-house library and public databases. Supervised and unsupervised multivariate analysis revealed metabolic differences between RT4 cells and ALDH1L1-deficient clones. Glycine (8-fold decrease) and metabolites derived from S-adenosylmethionine utilizing pathways were significantly decreased in the ALDH1L1-deficient clones, compared with RT4 cells. Other changes linked to ALDH1L1 downregulation include decreased levels of amino acids, Krebs cycle intermediates, and ribose-5-phosphate, and increased nicotinic acid. While the ALDH1L1-catalyzed reaction is directly linked to glycine biosynthesis and methyl group flux, its overall effect on cellular metabolism extends beyond immediate metabolic pathways controlled by this enzyme.
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Affiliation(s)
- Blake R. Rushing
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
- Department of Nutrition, UNC Chapel Hill, Chapel Hill, NC 27599, USA
| | - Halle M. Fogle
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
- Department of Nutrition, UNC Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jaspreet Sharma
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
| | - Mikyoung You
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
| | | | - Sabrina Molina
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
| | - Susan Sumner
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
- Department of Nutrition, UNC Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (S.S.); (S.A.K.)
| | - Natalia I. Krupenko
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
- Department of Nutrition, UNC Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sergey A. Krupenko
- Nutrition Research Institute, UNC Chapel Hill, Kannapolis, NC 28081, USA
- Department of Nutrition, UNC Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (S.S.); (S.A.K.)
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43
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Leonova T, Ihling C, Saoud M, Frolova N, Rennert R, Wessjohann LA, Frolov A. Does filter-aided sample preparation provide sufficient method linearity for quantitative plant shotgun proteomics? FRONTIERS IN PLANT SCIENCE 2022; 13:874761. [PMID: 36507396 PMCID: PMC9728026 DOI: 10.3389/fpls.2022.874761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Due to its outstanding throughput and analytical resolution, gel-free LC-based shotgun proteomics represents the gold standard of proteome analysis. Thereby, the efficiency of sample preparation dramatically affects the correctness and reliability of protein quantification. Thus, the steps of protein isolation, solubilization, and proteolysis represent the principal bottleneck of shotgun proteomics. The desired performance of the sample preparation protocols can be achieved by the application of detergents. However, these compounds ultimately compromise reverse-phase chromatographic separation and disrupt electrospray ionization. Filter-aided sample preparation (FASP) represents an elegant approach to overcome these limitations. Although this method is comprehensively validated for cell proteomics, its applicability to plants and compatibility with plant-specific protein isolation protocols remain to be confirmed. Thereby, the most important gap is the absence of the data on the linearity of underlying protein quantification methods for plant matrices. To fill this gap, we address here the potential of FASP in combination with two protein isolation protocols for quantitative analysis of pea (Pisum sativum) seed and Arabidopsis thaliana leaf proteomes by the shotgun approach. For this aim, in comprehensive spiking experiments with bovine serum albumin (BSA), we evaluated the linear dynamic range (LDR) of protein quantification in the presence of plant matrices. Furthermore, we addressed the interference of two different plant matrices in quantitative experiments, accomplished with two alternative sample preparation workflows in comparison to conventional FASP-based digestion of cell lysates, considered here as a reference. The spiking experiments revealed high sensitivities (LODs of up to 4 fmol) for spiked BSA and LDRs of at least 0.6 × 102. Thereby, phenol extraction yielded slightly better recoveries, whereas the detergent-based method showed better linearity. Thus, our results indicate the very good applicability of FASP to quantitative plant proteomics with only limited impact of the protein isolation technique on the method's overall performance.
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Affiliation(s)
- Tatiana Leonova
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- Department of Biochemistry, St Petersburg State University, St Petersburg, Russia
| | - Christian Ihling
- Institute of Pharmacy, Department of Pharmaceutical Chemistry and Bioanalytics, Martin-Luther Universität Halle-Wittenberg, Halle (Saale), Germany
| | - Mohamad Saoud
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Nadezhda Frolova
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- Department of Biochemistry, St Petersburg State University, St Petersburg, Russia
| | - Robert Rennert
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Ludger A. Wessjohann
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Andrej Frolov
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- Department of Biochemistry, St Petersburg State University, St Petersburg, Russia
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44
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Jensen-Kroll J, Demetrowitsch T, Clawin-Rädecker I, Klempt M, Waschina S, Schwarz K. Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis. Front Mol Biosci 2022; 9:968643. [PMID: 36353731 PMCID: PMC9638022 DOI: 10.3389/fmolb.2022.968643] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/03/2022] [Indexed: 08/07/2024] Open
Abstract
Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose. A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage. For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells.
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Affiliation(s)
- Julia Jensen-Kroll
- Institute of Human Nutrition and Food Science, Division of Food Technology, Kiel University, Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Division of Food Technology, Kiel University, Kiel, Germany
| | - Ingrid Clawin-Rädecker
- Federal Research Institute of Nutrition and Food, Department of Safety and Quality of Milk and Fish Products, Max Rubner-Institute, Kiel, Germany
| | - Martin Klempt
- Federal Research Institute of Nutrition and Food, Department of Microbiology and Biotechnology, Max Rubner-Institute, Kiel, Germany
| | - Silvio Waschina
- Institute of Human Nutrition and Food Science, Division of Nutriinformatics, Kiel University, Kiel, Germany
| | - Karin Schwarz
- Institute of Human Nutrition and Food Science, Division of Food Technology, Kiel University, Kiel, Germany
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Wen B, Jaehnig EJ, Zhang B. OmicsEV: a tool for comprehensive quality evaluation of omics data tables. Bioinformatics 2022; 38:5463-5465. [PMID: 36271853 PMCID: PMC9750102 DOI: 10.1093/bioinformatics/btac698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/20/2022] [Indexed: 12/25/2022] Open
Abstract
SUMMARY RNA-Seq and mass spectrometry-based studies generate omics data tables with measurements for tens of thousands of genes across all samples in a study. The success of a study relies on the quality of these data tables, which is determined by both experimental data generation and computational methods used to process raw experimental data into quantitative data tables. We present OmicsEV, an R package for the quality evaluation of omics data tables. For each data table, OmicsEV uses a series of methods to evaluate data depth, data normalization, batch effect, biological signal, platform reproducibility and multi-omics concordance, producing comprehensive visual and quantitative evaluation results that help assess the data quality of individual data tables and facilitate the identification of the optimal data processing method and parameters for the omics study under investigation. AVAILABILITY AND IMPLEMENTATION The source code and the user manual of OmicsEV are available at https://github.com/bzhanglab/OmicsEV, and the source code is released under the GPL-3 license.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bing Zhang
- To whom correspondence should be addressed.
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46
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Desaire H, Go EP, Hua D. Advances, obstacles, and opportunities for machine learning in proteomics. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:101069. [PMID: 36381226 PMCID: PMC9648337 DOI: 10.1016/j.xcrp.2022.101069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - Eden P. Go
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - David Hua
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
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Dmitrenko A, Reid M, Zamboni N. A system suitability testing platform for untargeted, high-resolution mass spectrometry. Front Mol Biosci 2022; 9:1026184. [PMID: 36304928 PMCID: PMC9592825 DOI: 10.3389/fmolb.2022.1026184] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/26/2022] [Indexed: 11/25/2022] Open
Abstract
The broad coverage of untargeted metabolomics poses fundamental challenges for the harmonization of measurements along time, even if they originate from the very same instrument. Internal isotopic standards can hardly cover the chemical complexity of study samples. Therefore, they are insufficient for normalizing data a posteriori as done for targeted metabolomics. Instead, it is crucial to verify instrument’s performance a priori, that is, before samples are injected. Here, we propose a system suitability testing platform for time-of-flight mass spectrometers independent of liquid chromatography. It includes a chemically defined quality control mixture, a fast acquisition method, software for extracting ca. 3,000 numerical features from profile data, and a simple web service for monitoring. We ran a pilot for 21 months and present illustrative results for anomaly detection or learning causal relationships between the spectral features and machine settings. Beyond mere detection of anomalies, our results highlight several future applications such as 1) recommending instrument retuning strategies to achieve desired values of quality indicators, 2) driving preventive maintenance, and 3) using the obtained, detailed spectral features for posterior data harmonization.
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Affiliation(s)
- Andrei Dmitrenko
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- Life Science Zurich PhD Program on Systems Biology, Zürich, Switzerland
| | - Michelle Reid
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- *Correspondence: Nicola Zamboni,
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Bracht T, Kleefisch D, Schork K, Witzke KE, Chen W, Bayer M, Hovanec J, Johnen G, Meier S, Ko YD, Behrens T, Brüning T, Fassunke J, Buettner R, Uszkoreit J, Adamzik M, Eisenacher M, Sitek B. Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD). Int J Mol Sci 2022; 23:ijms231911242. [PMID: 36232544 PMCID: PMC9569607 DOI: 10.3390/ijms231911242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.
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Affiliation(s)
- Thilo Bracht
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Correspondence: (T.B.); (B.S.); Tel.: +49-234-32-29985 (T.B.); +49-234-32-24362 (B.S.)
| | - Daniel Kleefisch
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Karin Schork
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Kathrin E. Witzke
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Weiqiang Chen
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Malte Bayer
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Jan Hovanec
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Georg Johnen
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Swetlana Meier
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, 53113 Bonn, Germany
| | - Thomas Behrens
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 Bochum, Germany
| | - Jana Fassunke
- Institute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany
| | - Reinhard Buettner
- Institute of Pathology, Medical Faculty and Center for Molecular Medicine (CMMC), University of Cologne, 50924 Cologne, Germany
| | - Julian Uszkoreit
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Michael Adamzik
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
| | - Martin Eisenacher
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Barbara Sitek
- Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, 44892 Bochum, Germany
- Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Correspondence: (T.B.); (B.S.); Tel.: +49-234-32-29985 (T.B.); +49-234-32-24362 (B.S.)
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Boronat-Toscano A, Monfort-Ferré D, Menacho M, Caro A, Bosch R, Espina B, Algaba-Chueca F, Saera-Vila A, Moliné A, Marti M, Espin E, Millan M, Serena C. Anti-TNF Therapies Suppress Adipose Tissue Inflammation in Crohn’s Disease. Int J Mol Sci 2022; 23:ijms231911170. [PMID: 36232469 PMCID: PMC9570367 DOI: 10.3390/ijms231911170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/19/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Anti-TNF biologics have been shown to markedly improve the quality of life for patients with Crohn’s disease (CD), yet one-third of patients fail to benefit from this treatment. Patients with CD develop a characteristic wrapping of visceral adipose tissue (VAT) in the inflamed intestinal area, termed creeping fat, and it is known that adipose tissue expansion influences the efficacy of anti-TNF drugs. We questioned whether anti-TNF therapies impact the creeping fat in CD, which might affect the outcome of the disease. Adipose tissue biopsies were obtained from a cohort of 14 patients with CD that received anti-TNF drugs and from 29 non-anti-TNF-treated patients (control group) matched by sex, age, and body mass index undergoing surgical interventions for symptomatic complications. We found that anti-TNF therapies restored adipose tissue morphology and suppressed immune cell infiltration in the creeping fat. Additionally, anti-TNF treatments appeared to markedly improve the pro-inflammatory phenotype of adipose-tissue macrophages and adipose-tissue-derived stem cells. Our study provides evidence that anti-TNF medications influence immune cells and progenitor cells in the creeping of patients with CD, suppressing inflammation. We propose that perilesional VAT should be considered when administering anti-TNF therapy in patients with CD.
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Affiliation(s)
- Albert Boronat-Toscano
- Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Diandra Monfort-Ferré
- Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Margarita Menacho
- Digestive Unit, Hospital Universitari Joan XXIII, 43007 Tarragona, Spain
| | - Aleidis Caro
- Colorectal Surgery Unit, Hospital Universitari Joan XXIII, 43007 Tarragona, Spain
| | - Ramon Bosch
- Department of Pathology, Oncological Pathology and Bioinformatics Research Group, Hospital de Tortosa Verge de la Cinta—IISPV, 43500 Tortosa, Spain
| | - Beatriz Espina
- Colorectal Surgery Unit, Hospital Universitari Joan XXIII, 43007 Tarragona, Spain
| | - Francisco Algaba-Chueca
- Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | | | - Alicia Moliné
- Digestive Unit, Hospital Universitari Joan XXIII, 43007 Tarragona, Spain
| | - Marc Marti
- Colorectal Surgery Unit, General Surgery Service, Hospital Valle de Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Eloy Espin
- Colorectal Surgery Unit, General Surgery Service, Hospital Valle de Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Mónica Millan
- Colorectal Surgery Unit, General Surgery Service, Hospital La Fe, 46026 Valencia, Spain
| | - Carolina Serena
- Institut d’Investigació Sanitària Pere Virgili, Hospital Universitari Joan XXIII, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Correspondence: or
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50
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Hsieh YC, Negri J, He A, Pearse RV, Liu L, Duong DM, Chibnik LB, Bennett DA, Seyfried NT, Young-Pearse TL. Elevated ganglioside GM2 activator (GM2A) in human brain tissue reduces neurite integrity and spontaneous neuronal activity. Mol Neurodegener 2022; 17:61. [PMID: 36131294 PMCID: PMC9494921 DOI: 10.1186/s13024-022-00558-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's Disease (AD) affects millions globally, but therapy development is lagging. New experimental systems that monitor neuronal functions in conditions approximating the AD brain may be beneficial for identifying new therapeutic strategies. METHODS We expose cultured neurons to aqueous-soluble human brain extract from 43 individuals across a spectrum of AD pathology. Multi-electrode arrays (MEAs) and live-cell imaging were used to assess neuronal firing and neurite integrity (NI), respectively, following treatments of rat cortical neurons (MEA) and human iPSC-derived neurons (iN) with human brain extracts. RESULTS We observe associations between spontaneous activity and Aβ42:40 levels, between neurite integrity and oligomeric Aβ, and between neurite integrity and tau levels present in the brain extracts. However, these associations with Aβ and tau do not fully account for the effects observed. Proteomic profiling of the brain extracts revealed additional candidates correlated with neuronal structure and activity. Neurotoxicity in MEA and NI assays was associated with proteins implicated in lysosomal storage disorders, while neuroprotection was associated with proteins of the WAVE regulatory complex controlling actin cytoskeleton dynamics. Elevated ganglioside GM2 activator (GM2A) associates with reductions in both NI and MEA activity, and cell-derived GM2A alone is sufficient to induce a loss of neurite integrity and a reduction in neuronal firing. CONCLUSIONS The techniques and data herein introduce a system for modeling neuronal vulnerability in response to factors in the human brain and provide insights into proteins potentially contributing to AD pathogenesis.
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Affiliation(s)
- Yi-Chen Hsieh
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
| | - Joseph Negri
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
| | - Amy He
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
| | - Richard V. Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
| | - Lei Liu
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
| | - Duc M. Duong
- Department of Biochemistry, Emory University School of Medicine, 1510 Clifton Rd NE, Atlanta, GA 30322 USA
| | - Lori B. Chibnik
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115 USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, 600 S. Paulina St, Chicago, IL 60612 USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, 1510 Clifton Rd NE, Atlanta, GA 30322 USA
- Department of Neurology, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322 USA
| | - Tracy L. Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital and Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 USA
- Harvard Stem Cell Institute, Harvard University, 7 Divinity Ave, Cambridge, MA 02138 USA
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