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Denisenko TV, Ivanova AE, Koval A, Silachev DN, Jia L, Sukhikh GT, Katanaev VL. Signalomics for molecular tumor boards and precision oncology of breast and gynecological cancers. Mol Syst Biol 2025:10.1038/s44320-025-00125-1. [PMID: 40490498 DOI: 10.1038/s44320-025-00125-1] [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: 03/16/2025] [Revised: 05/08/2025] [Accepted: 05/20/2025] [Indexed: 06/11/2025] Open
Abstract
Precision oncology led to the establishment and widespread application of molecular tumor boards (MTBs)-multidisciplinary units combining molecular and clinical assessment of individual cancer cases for swift selection of personalized treatments. Whole-exome or gene panel sequencing, combined with transcriptomic, immunohistochemical, and other molecular analyses, often permits dissection of molecular drivers of a tumor and identification of its potential targetable vulnerabilities, instructing clinical oncologists on sometimes unconventional treatment options. However, cancer drivers are often unleashed mutation-independently, especially in breast and gynecological cancers, and deleterious mutations are not always pathogenic. To complement the MTB arsenal, we chart here the molecular toolset we call Signalomics that permits fast and robust assessment of a panel of oncogenic signaling pathways in fresh tumor samples. Using transcriptional reporters introduced in primary tumor cells, this approach identifies the pathways overactivated in a given tumor and validates their sensitivity to targeted therapies, providing actionable insights for personalized treatment strategies. Integration of Signalomics into MTB workflows bridges the gap between molecular profiling and functional pathway analysis, refining clinical treatment decisions and advancing precision oncology.
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Affiliation(s)
- Tatiana V Denisenko
- Kulakov National Medical Research Center of Obstetrics, Gynecology and Perinatology, 4 Akademika Oparina Str., Moscow, 117997, Russia
| | - Anna E Ivanova
- Kulakov National Medical Research Center of Obstetrics, Gynecology and Perinatology, 4 Akademika Oparina Str., Moscow, 117997, Russia
| | - Alexey Koval
- Translational Research Centre in Oncohaematology, Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, CH-1211, Geneva, Switzerland
| | - Denis N Silachev
- Kulakov National Medical Research Center of Obstetrics, Gynecology and Perinatology, 4 Akademika Oparina Str., Moscow, 117997, Russia
- Translational Research Centre in Oncohaematology, Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, CH-1211, Geneva, Switzerland
- Department of Functional Biochemistry of Biopolymers, A.N. Belozersky Research Institute of Physico-Chemical Biology, Moscow State University, 119992, Moscow, Russia
| | - Lee Jia
- College of Materials and Chemical Engineering, Minjiang University, Fuzhou, Fujian, 350108, China
| | - Gennadiy T Sukhikh
- Kulakov National Medical Research Center of Obstetrics, Gynecology and Perinatology, 4 Akademika Oparina Str., Moscow, 117997, Russia
| | - Vladimir L Katanaev
- Translational Research Centre in Oncohaematology, Department of Cell Physiology and Metabolism, Faculty of Medicine, University of Geneva, CH-1211, Geneva, Switzerland.
- Translational Oncology Research Center, Qatar Biomedical Research Institute (QBRI), College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation, PO Box 34110, Doha, Qatar.
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Hostrup M, Deshmukh AS. Fiber Type-Specific Adaptations to Exercise Training in Human Skeletal Muscle: Lessons From Proteome Analyses and Future Directions. Scand J Med Sci Sports 2025; 35:e70059. [PMID: 40281372 PMCID: PMC12031692 DOI: 10.1111/sms.70059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Revised: 03/25/2025] [Accepted: 04/10/2025] [Indexed: 04/29/2025]
Abstract
Skeletal muscle is a key determinant of sports performance. It is a highly specialized, yet complex and heterogeneous tissue, comprising multiple cell types. Muscle fibers are the main functional cell type responsible for converting energy into mechanical work. They exhibit a remarkable ability to adapt in response to stressors, such as exercise training. But while it is recognized that human skeletal muscle fibers have distinct contractile and metabolic features, classified as slow/oxidative (type 1) or fast/glycolytic (type 2a/x), less attention has been directed to the adaptability of the different fiber types. Methodological advancements in mass spectrometry-based proteomics allow researchers to quantify thousands of proteins with only a small amount of muscle tissue-even in a single muscle fiber. By exploiting this technology, studies are emerging highlighting that muscle fiber subpopulations adapt differently to exercise training. This review provides a contemporary perspective on the fiber type-specific adaptability to exercise training in humans. A key aim of our review is to facilitate further advancements within exercise physiology by harnessing mass spectrometry proteomics.
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Affiliation(s)
- Morten Hostrup
- Clinical & Experimental Physiology Group, The August Krogh Section for Human and Molecular Physiology, Department of Nutrition, Exercise and SportsUniversity of CopenhagenCopenhagenDenmark
| | - Atul S. Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
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3
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Williams E, Echeverri Tribin F, Carreño JM, Krammer F, Hoffer M, Pallikkuth S, Pahwa S. Proteomic signatures of vaccine-induced and breakthrough infection-induced host responses to SARS-CoV-2. Vaccine 2025; 43:126484. [PMID: 39520894 PMCID: PMC12044548 DOI: 10.1016/j.vaccine.2024.126484] [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: 08/27/2024] [Revised: 10/22/2024] [Accepted: 10/23/2024] [Indexed: 11/16/2024]
Abstract
The severity of SARS-CoV-2 illness is influenced by factors including age, sex, pre-existing health conditions, and individual immune responses. However, the mechanisms conferring immunity following antigenic challenge have not been fully elucidated. There are currently no studies evaluating longitudinal proteomic changes in individuals following vaccination and breakthrough, limiting our understanding of the underlying mechanisms driving conferred immunity. In this work, we evaluated the differential protein expression in individuals with (CoV-P) or without (CoV-N) prior SARS-CoV-2 infection following primary vaccination and after breakthrough infection (CoV-BT). Overall, we found that individuals receiving primary vaccination relied on innate immune mechanisms, including complement and coagulation cascades, and natural killer cell-mediated cytotoxicity, while conversely, breakthrough infection immune mechanisms relied on T cell-mediated immunity. These mechanistic differences may help explain heterogeneity associated with vaccine-induced and breakthrough infection-related outcomes.
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Affiliation(s)
- Erin Williams
- Department of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, 33136, USA; Department of Biomedical Engineering, University of Miami, Miami, Florida, 33136, USA
| | | | - Juan Manuel Carreño
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, New York, 10029, USA; Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, New York, 10029, USA; Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Ignaz Semmelweis Institute, Interuniversity Institute for Infection Research, Medical University of Vienna, Vienna, Austria
| | - Michael Hoffer
- Department of Otolaryngology, University of Miami Miller School of Medicine, Miami, Florida, 33136, USA; Department of Neurological Surgery, University of Miami, Miller School of Medicine, Miami, Florida, 33136, USA
| | - Suresh Pallikkuth
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, Florida, 33146, USA
| | - Savita Pahwa
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, Florida, 33146, USA
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4
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Nayar G, Altman RB. Heterogeneous network approaches to protein pathway prediction. Comput Struct Biotechnol J 2024; 23:2727-2739. [PMID: 39035835 PMCID: PMC11260399 DOI: 10.1016/j.csbj.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.
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Affiliation(s)
- Gowri Nayar
- Department of Biomedical Data Science, Stanford University, United States
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, United States
- Department of Genetics, Stanford University, United States
- Department of Medicine, Stanford University, United States
- Department of Bioengineering, Stanford University, United States
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Nourisa J, Passemiers A, Shakeri F, Omidi M, Helmholz H, Raimondi D, Moreau Y, Tomforde S, Schlüter H, Luthringer-Feyerabend B, Cyron CJ, Aydin RC, Willumeit-Römer R, Zeller-Plumhoff B. Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg 2+ ions. Comput Struct Biotechnol J 2024; 23:1773-1785. [PMID: 38689715 PMCID: PMC11058716 DOI: 10.1016/j.csbj.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.
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Affiliation(s)
- Jalil Nourisa
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
| | | | - Farhad Shakeri
- Institute of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Maryam Omidi
- Institute of Clinical Chemistry/Central Laboratories, University Medical Center Hamburg, Hamburg, Germany
| | - Heike Helmholz
- Institute of Metallic Biomaterials, Helmholtz Zentrum Hereon, Geesthacht, Germany
| | | | | | - Sven Tomforde
- Department of Computer Science, Intelligent Systems, University of Kiel, Kiel, Germany
| | - Hartmuth Schlüter
- Institute of Clinical Chemistry and Laboratory Medicine Diagnostic Center, University of Hamburg, Hamburg, Germany
| | | | - Christian J. Cyron
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
| | - Roland C. Aydin
- Institute of Material Systems Modeling, Helmholtz Zentrum Hereon, Geesthacht, Germany
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
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Donders Z, Skorupska IJ, Willems E, Mussen F, Broeckhoven JV, Carlier A, Schepers M, Vanmierlo T. Beyond PDE4 inhibition: A comprehensive review on downstream cAMP signaling in the central nervous system. Biomed Pharmacother 2024; 177:117009. [PMID: 38908196 DOI: 10.1016/j.biopha.2024.117009] [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: 03/28/2024] [Revised: 05/27/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024] Open
Abstract
Cyclic adenosine monophosphate (cAMP) is a key second messenger that regulates signal transduction pathways pivotal for numerous biological functions. Intracellular cAMP levels are spatiotemporally regulated by their hydrolyzing enzymes called phosphodiesterases (PDEs). It has been shown that increased cAMP levels in the central nervous system (CNS) promote neuroplasticity, neurotransmission, neuronal survival, and myelination while suppressing neuroinflammation. Thus, elevating cAMP levels through PDE inhibition provides a therapeutic approach for multiple CNS disorders, including multiple sclerosis, stroke, spinal cord injury, amyotrophic lateral sclerosis, traumatic brain injury, and Alzheimer's disease. In particular, inhibition of the cAMP-specific PDE4 subfamily is widely studied because of its high expression in the CNS. So far, the clinical translation of full PDE4 inhibitors has been hampered because of dose-limiting side effects. Hence, focusing on signaling cascades downstream activated upon PDE4 inhibition presents a promising strategy, offering novel and pharmacologically safe targets for treating CNS disorders. Yet, the underlying downstream signaling pathways activated upon PDE(4) inhibition remain partially elusive. This review provides a comprehensive overview of the existing knowledge regarding downstream mediators of cAMP signaling induced by PDE4 inhibition or cAMP stimulators. Furthermore, we highlight existing gaps and future perspectives that may incentivize additional downstream research concerning PDE(4) inhibition, thereby providing novel therapeutic approaches for CNS disorders.
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Affiliation(s)
- Zoë Donders
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium
| | - Iga Joanna Skorupska
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium; Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht 6629ER, the Netherlands
| | - Emily Willems
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium
| | - Femke Mussen
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium; Department of Immunology and Infection, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium
| | - Jana Van Broeckhoven
- Department of Immunology and Infection, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium; University MS Centre (UMSC) Hasselt - Pelt, Belgium
| | - Aurélie Carlier
- Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht 6629ER, the Netherlands
| | - Melissa Schepers
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium; University MS Centre (UMSC) Hasselt - Pelt, Belgium
| | - Tim Vanmierlo
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht 6229ER, the Netherlands; Department of Neuroscience, Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt 3500, Belgium; University MS Centre (UMSC) Hasselt - Pelt, Belgium.
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7
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Swain IX, Kresak AM. Proteins Involved in Focal Cell Adhesion and Podosome Formation Are Differentially Expressed during Colorectal Tumorigenesis in AOM-Treated Rats. Cancers (Basel) 2024; 16:1678. [PMID: 38730628 PMCID: PMC11083089 DOI: 10.3390/cancers16091678] [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: 04/10/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Colorectal tumorigenesis involves the development of aberrant crypt foci (ACF) or preneoplastic lesions, representing the earliest morphological lesion visible in colon cancer. The purpose of this study was to determine changes in protein expression in carcinogen-induced ACF as they mature and transform into adenomas. Protein expression profiles of azoxymethane (AOM)-induced F344 rat colon ACF and adenomas were compared at four time points, 4 (control), 8, 16, and 24 weeks post AOM administration (n = 9/group), with time points correlating with induction and transformation events. At each time point, micro-dissected ACF and/or adenoma tissues were analyzed across multiple quantitative two-dimensional (2D-DIGE) gels using a Cy-dye labeling technique and a pooled internal standard to quantify expression changes with statistical confidence. Western blot and subsequent network pathway mapping were used to confirm and elucidate differentially expressed (p ≤ 0.05) proteins, including changes in vinculin (Vcl; p = 0.007), scinderin (Scin; p = 0.02), and profilin (Pfn1; p = 0.01), By determining protein expression changes in ACF as they mature and transform into adenomas, a "baseline" of altered regulatory proteins associated with adenocarcinoma development in this model has been elucidated. These data will enable future studies aimed at biomarker identification and understanding the molecular biology of intestinal tumorigenesis and adenocarcinoma maturation under varying intestinal conditions.
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Affiliation(s)
- Ian X. Swain
- Department of Pathology, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH 44106, USA;
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8
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Plouviez M, Dubreucq E. Key Proteomics Tools for Fundamental and Applied Microalgal Research. Proteomes 2024; 12:13. [PMID: 38651372 PMCID: PMC11036299 DOI: 10.3390/proteomes12020013] [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: 12/29/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.
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Affiliation(s)
- Maxence Plouviez
- School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
- The Cawthron Institute, Nelson 7010, New Zealand
| | - Eric Dubreucq
- Agropolymer Engineering and Emerging Technologies, L’Institut Agro Montpellier, 34060 Montpellier, France;
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9
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Miao X, Shen S, Koch G, Wang X, Li J, Shen X, Qu J, Straubinger RM, Jusko WJ. Systems Pharmacodynamic Model of Combined Gemcitabine and Trabectedin in Pancreatic Cancer Cells. Part I: Effects on Signal Transduction Pathways Related to Tumor Growth. J Pharm Sci 2024; 113:214-227. [PMID: 38498417 PMCID: PMC11017371 DOI: 10.1016/j.xphs.2023.10.030] [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: 08/30/2023] [Revised: 10/22/2023] [Accepted: 10/22/2023] [Indexed: 03/20/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is often chemotherapy-resistant, and novel drug combinations would fill an unmet clinical need. Previously we reported synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells, but underlying protein-level interaction mechanisms remained unclear. We employed a reliable, sensitive, comprehensive, quantitative, high-throughput IonStar proteomic workflow to investigate the time course of gemcitabine and trabectedin effects, alone and combined, upon pancreatic cancer cells. MiaPaCa-2 cells were incubated with vehicle (controls), gemcitabine, trabectedin, and their combinations over 72 hours. Samples were collected at intervals and analyzed using the label-free IonStar liquid chromatography-mass spectrometry (LC-MS/MS) workflow to provide temporal quantification of protein expression for 4,829 proteins in four experimental groups. To characterize diverse signal transduction pathways, a comprehensive systems pharmacodynamic (SPD) model was developed. The analysis is presented in two parts. Here, Part I describes drug responses in cancer cell growth and migration pathways included in the full model: receptor tyrosine kinase- (RTK), integrin-, G-protein coupled receptor- (GPCR), and calcium-signaling pathways. The developed model revealed multiple underlying mechanisms of drug actions, provides insight into the basis of drug interaction synergism, and offers a scientific rationale for potential drug combination strategies.
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Affiliation(s)
- Xin Miao
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States
| | - Shichen Shen
- Department of Biochemistry, School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States; New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States
| | - Gilbert Koch
- Pediatric Pharmacology and Pharmacometrics Research Center, University of Basel, Children's Hospital, Basel, Switzerland
| | - Xue Wang
- New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States; Department of Cell Stress Biology, Roswell Park Cancer Institute, Buffalo, NY, United States
| | - Jun Li
- New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States
| | - Xiaomeng Shen
- Department of Biochemistry, School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States; New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States
| | - Jun Qu
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States; New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States
| | - Robert M Straubinger
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States; New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, United States; Department of Cell Stress Biology, Roswell Park Cancer Institute, Buffalo, NY, United States
| | - William J Jusko
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, NY, United States.
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10
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Mousavian Z, Källenius G, Sundling C. From simple to complex: Protein-based biomarker discovery in tuberculosis. Eur J Immunol 2023; 53:e2350485. [PMID: 37740950 DOI: 10.1002/eji.202350485] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/15/2023] [Accepted: 09/22/2023] [Indexed: 09/25/2023]
Abstract
Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.
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Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
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11
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Zuccoli GS, Nascimento JM, Moraes-Vieira PM, Rehen SK, Martins-de-Souza D. Mitochondrial, cell cycle control and neuritogenesis alterations in an iPSC-based neurodevelopmental model for schizophrenia. Eur Arch Psychiatry Clin Neurosci 2023; 273:1649-1664. [PMID: 37039888 DOI: 10.1007/s00406-023-01605-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/29/2023] [Indexed: 04/12/2023]
Abstract
Schizophrenia is a severe psychiatric disorder of neurodevelopmental origin that affects around 1% of the world's population. Proteomic studies and other approaches have provided evidence of compromised cellular processes in the disorder, including mitochondrial function. Most of the studies so far have been conducted on postmortem brain tissue from patients, and therefore, do not allow the evaluation of the neurodevelopmental aspect of the disorder. To circumvent that, we studied the mitochondrial and nuclear proteomes of neural stem cells (NSCs) and neurons derived from induced pluripotent stem cells (iPSCs) from schizophrenia patients versus healthy controls to assess possible alterations related to energy metabolism and mitochondrial function during neurodevelopment in the disorder. Our results revealed differentially expressed proteins in pathways related to mitochondrial function, cell cycle control, DNA repair and neuritogenesis and their possible implication in key process of neurodevelopment, such as neuronal differentiation and axonal guidance signaling. Moreover, functional analysis of NSCs revealed alterations in mitochondrial oxygen consumption in schizophrenia-derived cells and a tendency of higher levels of intracellular reactive oxygen species (ROS). Hence, this study shows evidence that alterations in important cellular processes are present during neurodevelopment and could be involved with the establishment of schizophrenia, as well as the phenotypic traits observed in adult patients. Neural stem cells (NSCs) and neurons were derived from induced pluripotent stem cells (iPSCs) from schizophrenia patients and controls. Proteomic analyses were performed on the enriched mitochondrial and nuclear fractions of NSCs and neurons. Whole-cell proteomic analysis was also performed in neurons. Our results revealed alteration in proteins related to mitochondrial function, cell cycle control, among others. We also performed energy pathway analysis and reactive oxygen species (ROS) analysis of NSCs, which revealed alterations in mitochondrial oxygen consumption and a tendency of higher levels of intracellular ROS in schizophrenia-derived cells.
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Affiliation(s)
- Giuliana S Zuccoli
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Juliana M Nascimento
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
| | - Pedro M Moraes-Vieira
- Laboratory of Immunometabolism, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, 13083-862, Brazil
- Obesity and Comorbidities Research Center (OCRC), University of Campinas, São Paulo, Brazil
| | - Stevens K Rehen
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
- Department of Genetics, Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil.
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil.
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, 13083-862, Brazil.
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil.
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12
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Rainey A, McKay GJ, English J, Thakkinstian A, Maxwell AP, Corr M. Proteomic analysis investigating kidney transplantation outcomes- a scoping review. BMC Nephrol 2023; 24:346. [PMID: 37993798 PMCID: PMC10666386 DOI: 10.1186/s12882-023-03401-0] [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: 08/08/2023] [Accepted: 11/16/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Kidney transplantation is the optimal treatment option for most patients with end-stage kidney disease given the significantly lower morbidity and mortality rates compared to remaining on dialysis. Rejection and graft failure remain common in transplant recipients with limited improvement in long-term transplant outcomes despite therapeutic advances. There is an unmet need in the development of non-invasive biomarkers that specifically monitor graft function and predict transplant pathologies that affect outcomes. Despite the potential of proteomic investigatory approaches, up to now, no candidate biomarkers of sufficient sensitivity or specificity have translated into clinical use. The aim of this review was to collate and summarise protein findings and protein pathways implicated in the literature to date, and potentially flag putative biomarkers worth validating in independent patient cohorts. METHODS This review followed the Joanna Briggs' Institute Methodology for a scoping review. MedlineALL, Embase, Web of Science Core Collection, Scopus and Google Scholar databases were searched from inception until December 2022. Abstract and full text review were undertaken independently by two reviewers. Data was collated using a pre-designed data extraction tool. RESULTS One hundred one articles met the inclusion criteria. The majority were single-centre retrospective studies of small sample size. Mass spectrometry was the most used technique to evaluate differentially expressed proteins between diagnostic groups and studies identified various candidate biomarkers such as immune or structural proteins. DISCUSSION Putative immune or structural protein candidate biomarkers have been identified using proteomic techniques in multiple sample types including urine, serum and fluid used to perfuse donor kidneys. The most consistent findings implicated proteins associated with tubular dysfunction and immunological regulatory pathways such as leukocyte trafficking. However, clinical translation and adoption of candidate biomarkers is limited, and these will require comprehensive evaluation in larger prospective, multicentre trials.
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Affiliation(s)
- Anna Rainey
- Centre for Public Health- Queen's University Belfast, Belfast, UK
| | - Gareth J McKay
- Centre for Public Health- Queen's University Belfast, Belfast, UK
| | - Jane English
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Michael Corr
- Centre for Public Health- Queen's University Belfast, Belfast, UK.
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13
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Ghiasi H, Khaldari M, Taherkhani R. Identification of hub genes associated with somatic cell score in dairy cow. Trop Anim Health Prod 2023; 55:349. [PMID: 37796357 DOI: 10.1007/s11250-023-03766-2] [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: 10/26/2022] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
CONTEXT Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). AIM Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. METHODS This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. KEY RESULTS Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). CONCLUSION Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. IMPLICATIONS Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.
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Affiliation(s)
- Heydar Ghiasi
- Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran, 19395-4697, Iran.
| | - Majid Khaldari
- Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorram-Abad, Iran
| | - Reza Taherkhani
- Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran, 19395-4697, Iran
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14
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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 PMCID: PMC10246047 DOI: 10.1186/s12859-023-05360-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [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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15
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Gao K, Ayati M, Kaye NM, Koyuturk M, Calabrese JR, Ganocy SJ, Lazarus HM, Christian E, Kaplan D. Differences in intracellular protein levels in monocytes and CD4 + lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry. J Affect Disord 2023; 328:116-127. [PMID: 36806598 DOI: 10.1016/j.jad.2023.02.058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/30/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs). METHODS The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4+ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations. RESULTS Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4+ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs. LIMITATION The sample size of the study was small and 2 patients were on medications. CONCLUSION In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.
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Affiliation(s)
- Keming Gao
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America; Case Western Reserve University School of Medicine, Cleveland, OH, United States of America.
| | - Marzieh Ayati
- Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, United States of America
| | - Nicholas M Kaye
- CellPrint Biotechnology, Cleveland, OH, United States of America
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, United States of America
| | - Joseph R Calabrese
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America; Case Western Reserve University School of Medicine, Cleveland, OH, United States of America
| | - Stephen J Ganocy
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America; Case Western Reserve University School of Medicine, Cleveland, OH, United States of America
| | - Hillard M Lazarus
- Case Western Reserve University School of Medicine, Cleveland, OH, United States of America; CellPrint Biotechnology, Cleveland, OH, United States of America; Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America
| | - Eric Christian
- CellPrint Biotechnology, Cleveland, OH, United States of America
| | - David Kaplan
- CellPrint Biotechnology, Cleveland, OH, United States of America
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16
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Nguyen T, Wei Y, Nakada Y, Chen JY, Zhou Y, Walcott G, Zhang J. Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools. Sci Rep 2023; 13:6821. [PMID: 37100826 PMCID: PMC10133286 DOI: 10.1038/s41598-023-32293-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 04/28/2023] Open
Abstract
Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of individual cells. However, the effectiveness of the currently available tools for processing and interpreting these immense datasets is limited. We incorporated three Artificial Intelligence (AI) techniques into a toolkit for evaluating scRNAseq data: AI Autoencoding separates data from different cell types and subpopulations of cell types (cluster analysis); AI Sparse Modeling identifies genes and signaling mechanisms that are differentially activated between subpopulations (pathway/gene set enrichment analysis), and AI Semisupervised Learning tracks the transformation of cells from one subpopulation into another (trajectory analysis). Autoencoding was often used in data denoising; yet, in our pipeline, Autoencoding was exclusively used for cell embedding and clustering. The performance of our AI scRNAseq toolkit and other highly cited non-AI tools was evaluated with three scRNAseq datasets obtained from the Gene Expression Omnibus database. Autoencoder was the only tool to identify differences between the cardiomyocyte subpopulations found in mice that underwent MI or sham-MI surgery on postnatal day (P) 1. Statistically significant differences between cardiomyocytes from P1-MI mice and mice that underwent MI on P8 were identified for six cell-cycle phases and five signaling pathways when the data were analyzed via Sparse Modeling, compared to just one cell-cycle phase and one pathway when the data were analyzed with non-AI techniques. Only Semisupervised Learning detected trajectories between the predominant cardiomyocyte clusters in hearts collected on P28 from pigs that underwent apical resection (AR) on P1, and on P30 from pigs that underwent AR on P1 and MI on P28. In another dataset, the pig scRNAseq data were collected after the injection of CCND2-overexpression Human-induced Pluripotent Stem Cell-derived cardiomyocytes (CCND2hiPSC) into injured P28 pig heart; only the AI-based technique could demonstrate that the host cardiomyocytes increase proliferating by through the HIPPO/YAP and MAPK signaling pathways. For the cluster, pathway/gene set enrichment, and trajectory analysis of scRNAseq datasets generated from studies of myocardial regeneration in mice and pigs, our AI-based toolkit identified results that non-AI techniques did not discover. These different results were validated and were important in explaining myocardial regeneration.
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Affiliation(s)
- Thanh Nguyen
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Yuhua Wei
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Yuji Nakada
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Jake Y Chen
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Yang Zhou
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Gregory Walcott
- Department of Medicine, Cardiovascular Diseases, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Jianyi Zhang
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
- Department of Medicine, Cardiovascular Diseases, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
- Department of Biomedical Engineering, School of Medicine and School of Engineering, University of Alabama at Birmingham, 1670 University Blvd, Volker Hall G094J, Birmingham, AL, 35233, USA.
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17
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Balbisi M, Sugár S, Schlosser G, Szeitz B, Fillinger J, Moldvay J, Drahos L, Szász AM, Tóth G, Turiák L. Inter- and intratumoral proteomics and glycosaminoglycan characterization of ALK rearranged lung adenocarcinoma tissues: a pilot study. Sci Rep 2023; 13:6268. [PMID: 37069213 PMCID: PMC10110559 DOI: 10.1038/s41598-023-33435-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/12/2023] [Indexed: 04/19/2023] Open
Abstract
Lung cancer is one of the most common types of cancer with limited therapeutic options, therefore a detailed understanding of the underlying molecular changes is of utmost importance. In this pilot study, we investigated the proteomic and glycosaminoglycan (GAG) profile of ALK rearranged lung tumor tissue regions based on the morphological classification, mucin and stromal content. Principal component analysis and hierarchical clustering revealed that both the proteomic and GAG-omic profiles are highly dependent on mucin content and to a lesser extent on morphology. We found that differentially expressed proteins between morphologically different tumor types are primarily involved in the regulation of protein synthesis, whereas those between adjacent normal and different tumor regions take part in several other biological processes (e.g. extracellular matrix organization, oxidation-reduction processes, protein folding) as well. The total amount and the sulfation profile of heparan sulfate and chondroitin sulfate showed small differences based on morphology and larger differences based on mucin content of the tumor, while an increase was observed in both the total amount and the average rate of sulfation in tumors compared to adjacent normal regions.
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Affiliation(s)
- Mirjam Balbisi
- MS Proteomics Research Group, Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest, 1117, Hungary
- Doctoral School of Pharmaceutical Sciences, Semmelweis University, Üllői út 26., Budapest, 1085, Hungary
| | - Simon Sugár
- MS Proteomics Research Group, Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest, 1117, Hungary
- Doctoral School of Pharmaceutical Sciences, Semmelweis University, Üllői út 26., Budapest, 1085, Hungary
| | - Gitta Schlosser
- MTA-ELTE Lendület Ion Mobility Mass Spectrometry Research Group, Eötvös Loránd University, Pázmány Péter sétány 1, Budapest, 1117, Hungary
| | - Beáta Szeitz
- Division of Oncology, Department of Internal Medicine and Oncology, Semmelweis University, Üllői út 26., Budapest, 1085, Hungary
| | - János Fillinger
- Department of Pathology, National Korányi Institute of Pulmonology, Korányi Frigyes út 1., Budapest, 1121, Hungary
| | - Judit Moldvay
- 1st Department of Pulmonology, National Korányi Institute of Pulmonology, Korányi Frigyes út 1., Budapest, 1121, Hungary
| | - László Drahos
- MS Proteomics Research Group, Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest, 1117, Hungary
| | - A Marcell Szász
- Division of Oncology, Department of Internal Medicine and Oncology, Semmelweis University, Üllői út 26., Budapest, 1085, Hungary
| | - Gábor Tóth
- MS Proteomics Research Group, Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest, 1117, Hungary.
| | - Lilla Turiák
- MS Proteomics Research Group, Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest, 1117, Hungary.
- Doctoral School of Pharmaceutical Sciences, Semmelweis University, Üllői út 26., Budapest, 1085, Hungary.
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18
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Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet 2023; 39:308-319. [PMID: 36750393 DOI: 10.1016/j.tig.2023.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/24/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023]
Abstract
Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.
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Affiliation(s)
- Kangmei Zhao
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
| | - Seung Yon Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
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19
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Brown M, Zhu S, Taylor L, Tabrizian M, Li-Jessen NY. Unraveling the Relevance of Tissue-Specific Decellularized Extracellular Matrix Hydrogels for Vocal Fold Regenerative Biomaterials: A Comprehensive Proteomic and In Vitro Study. ADVANCED NANOBIOMED RESEARCH 2023; 3:2200095. [PMID: 37547672 PMCID: PMC10398787 DOI: 10.1002/anbr.202200095] [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] [Indexed: 02/04/2023] Open
Abstract
Decellularized extracellular matrix (dECM) is a promising material for tissue engineering applications. Tissue-specific dECM is often seen as a favorable material that recapitulates a native-like microenvironment for cellular remodeling. However, the minute quantity of dECM derivable from small organs like the vocal fold (VF) hampers manufacturing scalability. Small intestinal submucosa (SIS), a commercial product with proven regenerative capacity, may be a viable option for VF applications. This study aims to compare dECM hydrogels derived from SIS or VF tissue with respect to protein content and functionality using mass spectrometry-based proteomics and in vitro studies. Proteomic analysis reveals that VF and SIS dECM share 75% of core matrisome proteins. Although VF dECM proteins have greater overlap with native VF, SIS dECM shows less cross-sample variability. Following decellularization, significant reductions of soluble collagen (61%), elastin (81%), and hyaluronan (44%) are noted in VF dECM. SIS dECM contains comparable elastin and hyaluronan but 67% greater soluble collagen than VF dECM. Cells deposit more neo-collagen on SIS than VF-dECM hydrogels, whereas neo-elastin (~50 μg/scaffold) and neo-hyaluronan (~ 6 μg/scaffold) are comparable between the two hydrogels. Overall, SIS dECM possesses reasonably similar proteomic profile and regenerative capacity to VF dECM. SIS dECM is considered a promising alternative for dECM-derived biomaterials for VF regeneration.
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Affiliation(s)
- Mika Brown
- Department of Biomedical Engineering, McGill University 3655 Promenade Sir-William-Osler, Room 1003, Montreal, QC H3A 1A3, Canada
| | - Shirley Zhu
- Department of Microbiology and Immunology 2001 McGill College Ave, 8th Floor, Montreal, Quebec, H3A 1G1, Canada
| | - Lorne Taylor
- The Proteomics Platform, McGill University Health Center 1001 Decarie Boulevard Montreal Suite E01.5056 Montreal, Quebec, H4A 3J1, Canada
| | - Maryam Tabrizian
- Department of Biomedical Engineering, McGill University 3655 Promenade Sir-William-Osler, Room 1003, Montreal, QC H3A 1A3, Canada
- Department of Bioengineering, McGill University 740 Avenue Dr. Penfield, Room 4300, Montreal, QC H3A 0G1, Canada
- Faculty of Dentistry, McGill University 740 Avenue Dr. Penfield, Room 4300, Montreal, QC H3A 0G1, Canada
| | - Nicole Y.K. Li-Jessen
- Department of Biomedical Engineering, McGill University 3655 Promenade Sir-William-Osler, Room 1003, Montreal, QC H3A 1A3, Canada
- School of Communication Sciences and Disorders, McGill University 2001 McGill College Ave, 8th Floor, Montreal, Quebec, H3A 1G1, Canada
- Department of Otolaryngology - Head and Neck Surgery, McGill University 2001 McGill College Ave, 8th Floor, Montreal, Quebec, H3A 1G1, Canada
- Research Institute of McGill University Health Center, McGill University 2001 McGill College Ave, 8th Floor, Montreal, Quebec, H3A 1G1, Canada
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20
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Fan S, Weixuan W, Han H, Liansheng Z, Gang L, Jierui W, Yanshu Z. Role of NF-κB in lead exposure-induced activation of astrocytes based on bioinformatics analysis of hippocampal proteomics. Chem Biol Interact 2023; 370:110310. [PMID: 36539177 DOI: 10.1016/j.cbi.2022.110310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/05/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Lead (Pb), as a heavy metal, is used in batteries, ceramics, paint, pipes, certain ceramics, e-waste recycling, etc. Chronic Pb exposure can result in the inflammation of the central nervous system, as well as neurobehavioral changes. Both glial cells and neurons are involved in central nervous injury following Pb exposure. However, significant cellular events and their key regulators following Pb exposure remain to be elucidated. In this study, rats were randomly exposed to 250 or 500 mg/L PbAc for 9 weeks. Hippocampal proteomics was performed using isobaric tags for relative absolute quantification. Bioinformatics analysis was used to identify 301 and 267 differentially expressed proteins-which were involved in biological processes, including glial cell activation, neural nucleus development, and mRNA processing-in the low and high Pb exposure groups, respectively. Gene Set Enrichment Analysis showed that astrocyte activation was identified as a significant cellular event occurring in the low- or high-dose Pb exposure group. Subsequently, in vivo and in vitro models of Pb exposure were established to confirm astrocyte activation. As a result, glial fibrillary acidic protein expression in astrocytes was much higher in the Pb exposure group. Moreover, the mRNA expression of neurotoxic reactive astrocyte genes was much higher than that of the control group. The analysis of transcription factors indicated that NF-κB was screened as the top transcription factor, which might regulate astrocyte activation following Pb exposure in the rat hippocampus. The data also showed that the inhibition of NF-κB transcription suppressed astrocyte activation following Pb exposure. Overall, astrocyte activation was one of the significant cellular events following Pb exposure in the rat hippocampus, which was regulated by the NF-κB transcription factor, suggesting that inhibiting astrocyte activation may be a potential target for the prevention of Pb neurotoxicity.
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Affiliation(s)
- Shi Fan
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Wang Weixuan
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Hao Han
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Zhang Liansheng
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Liu Gang
- Department of Medicine, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Wang Jierui
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China.
| | - Zhang Yanshu
- School of Public Health, North China University of Science of Technology, Tangshan, 062310, Hebei, China; Laboratory Animal Center, North China University of Science and Technology, Tangshan Hebei, 063210, People's Republic of China.
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21
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Hapke R, Venton L, Rose KL, Sheng Q, Reddy A, Prather R, Jones A, Rathmell WK, Haake SM. SETD2 regulates the methylation of translation elongation factor eEF1A1 in clear cell renal cell carcinoma. KIDNEY CANCER JOURNAL : OFFICIAL JOURNAL OF THE KIDNEY CANCER ASSOCIATION 2022; 6:179-193. [PMID: 36684483 PMCID: PMC9851421 DOI: 10.3233/kca-220009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND SET domain-containing protein 2 (SETD2) is commonly mutated in renal cell carcinoma. SETD2 methylates histone H3 as well as a growing list of non-histone proteins. OBJECTIVE Initially, we sought to explore SETD2-dependent changes in lysine methylation of proteins in proximal renal tubule cells. Subsequently, we focused on changes in lysine methylation of the translation elongation factor eEF1A1. METHODS To accomplish these objectives, we initially performed a systems-wide analysis of protein lysine-methylation and expression in wild type (WT) and SETD2-knock out (KO) kidney cells and later focused our studies on eEF1A1 as well as the expression of lysine methyltransferases that regulate its lysine methylation. RESULTS We observed decreased lysine methylation of the translation elongation factor eEF1A1. EEF1AKMT2 and EEF1AKMT3 are known to methylate eEF1A1, and we show here that their expression is dependent on SET-domain function of SETD2. Globally, we observe differential expression of hundreds of proteins in WT versus SETD2-KO cells, including increased expression of many involved in protein translation. Finally, we observe decreased progression free survival and loss of EEF1AKMT2 gene expression in SETD2-mutated tumors predicted to have loss of function of the SET domain. CONCLUSION Overall, these data suggest that SETD2-mutated ccRCC, via loss of enzymatic function of the SET domain, displays dysregulation of protein translation as a potentially important component of the transformed phenotype.
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Affiliation(s)
- Robert Hapke
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Venton
- Department of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kristie Lindsay Rose
- Mass Spectrometry Research Center, Proteomics Core Laboratory, Vanderbilt University, Nashville, TN, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Rebecca Prather
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela Jones
- Vanderbilt Technologies for Advanced Genomics (VANTAGE), Vanderbilt University Medical Center, Nashville, TN, USA
| | - W. Kimryn Rathmell
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott M. Haake
- Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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22
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Ooka T, Raita Y, Fujiogi M, Freishtat RJ, Gerszten RE, Mansbach JM, Zhu Z, Camargo CA, Hasegawa K. Proteomics endotyping of infants with severe bronchiolitis and risk of childhood asthma. Allergy 2022; 77:3350-3361. [PMID: 35620861 PMCID: PMC9617778 DOI: 10.1111/all.15390] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/10/2022] [Accepted: 05/18/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND Bronchiolitis is the leading cause of hospitalization in U.S. infants and a major risk factor for childhood asthma. Growing evidence supports clinical heterogeneity within bronchiolitis. We aimed to identify endotypes of infant bronchiolitis by integrating clinical, virus, and serum proteome data, and examine their relationships with asthma development. METHODS This is a multicenter prospective cohort study of infants hospitalized for physician-diagnosis of bronchiolitis. We identified bronchiolitis endotypes by applying unsupervised machine learning (clustering) approaches to integrated clinical, virus (respiratory syncytial virus [RSV], rhinovirus [RV]), and serum proteome data measured at hospitalization. We then examined their longitudinal association with the risk for developing asthma by age 6 years. RESULTS In 140 infants hospitalized with bronchiolitis, we identified three endotypes: (1) clinicalatopic virusRV proteomeNFκB-dysregulated , (2) clinicalnon-atopic virusRSV/RV proteomeTNF-dysregulated , and (3) clinicalclassic virusRSV proteomeNFκB/TNF-regulated endotypes. Endotype 1 infants were characterized by high proportion of IgE sensitization and RV infection. These endotype 1 infants also had dysregulated NFκB pathways (FDR < 0.001) and significantly higher risks for developing asthma (53% vs. 22%; adjOR 4.04; 95% CI, 1.49-11.0; p = 0.006), compared with endotype 3 (clinically resembling "classic" bronchiolitis). Likewise, endotype 2 infants were characterized by low proportion of IgE sensitization and high proportion of RSV or RV infection. These endotype 2 infants had dysregulated tumor necrosis factor (TNF)-mediated signaling pathway (FDR <0.001) and significantly higher risks for developing asthma (44% vs. 22%; adjOR 2.71; 95% CI, 1.03-7.11, p = 0.04). CONCLUSION In this multicenter cohort, integrated clustering of clinical, virus, and proteome data identified biologically distinct endotypes of bronchiolitis that have differential risks of asthma development.
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Affiliation(s)
- Tadao Ooka
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Science, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michimasa Fujiogi
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert J. Freishtat
- Center for Genetic Medicine Research and Division of Emergency Medicine Children’s National Hospital. Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Jonathan M. Mansbach
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Zhaozhong Zhu
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Carlos A. Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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23
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Zanjani LS, Vafaei S, Abolhasani M, Fattahi F, Madjd Z. Prognostic value of Talin-1 in renal cell carcinoma and its association with B7-H3. Cancer Biomark 2022; 35:269-292. [DOI: 10.3233/cbm-220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
METHODS: Talin-1 protein was demonstrated as a potential prognostic marker in renal cell carcinoma (RCC) using bioinformatics analysis. We, therefore, examined the protein expression levels and prognostic significance of Talin-1 with a clinical follow-up in a total of 269 tissue specimens from three important subtypes of RCC and 30 adjacent normal samples using immunohistochemistry. Then, we used combined analysis with B7-H3 to investigate higher prognostic values. RESULTS: The results showed that high membranous and cytoplasmic expression of Talin-1 was significantly associated with advanced nucleolar grade, microvascular invasion, histological tumor necrosis, and invasion to Gerota’s fascia in clear cell RCC (ccRCC). In addition, high membranous and cytoplasmic expression of Talin-1 was found to be associated with significantly poorer disease-specific survival (DSS) and progression-free survival (PFS). Moreover, increased cytoplasmic expression of Talin-1High/B7-H3High compared to the other phenotypes was associated with tumor aggressiveness and progression of the disease, and predicted a worse clinical outcome, which may be an effective biomarker to identify ccRCC patients at high risk of recurrence and metastasis. CONCLUSIONS: Collectively, these observations indicate that Talin-1 is an important molecule involved in the spread and progression of ccRCC when expressed particularly in the cytoplasm and may serve as a novel prognostic biomarker in this subtype. Furthermore, a combined analysis of Talin-1/B7-H3 indicated an effective biomarker to predict the progression of disease and prognosis in ccRCC.
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Affiliation(s)
- Leili Saeednejad Zanjani
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
- Department of Pathology, Anatomy and Cell Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Somayeh Vafaei
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Maryam Abolhasani
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
- Hasheminejad Kidney Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Fahimeh Fattahi
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Madjd
- Oncopathology Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
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24
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Unravelling the neuroprotective mechanisms of carotenes in differentiated human neural cells: Biochemical and proteomic approaches. FOOD CHEMISTRY. MOLECULAR SCIENCES 2022; 4:100088. [PMID: 35415676 PMCID: PMC8991711 DOI: 10.1016/j.fochms.2022.100088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 12/20/2022]
Abstract
Total mixed carotenes (TMC) protect differentiated human neural cells against 6-hydroxydopamine-induced toxicity. TMC elevated the antioxidant enzymes activities and suppressed generation of reactive oxygen species. TMC augmented the dopamine and tyrosine hydroxylase levels. TMC exerted differential protein expression in human neural cells. Carotenoids, fat-soluble pigments found ubiquitously in plants and fruits, have been reported to exert significant neuroprotective effects against free radicals. However, the neuroprotective effects of total mixed carotenes complex (TMC) derived from virgin crude palm oil have not been studied extensively. Therefore, the present study was designed to establish the neuroprotective role of TMC on differentiated human neural cells against 6-hydroxydopamine (6-OHDA)-induced cytotoxicity. The human neural cells were differentiated using retinoic acid for six days. Then, the differentiated neural cells were pre-treated for 24 hr with TMC before exposure to 6-OHDA. TMC pre-treated neurons showed significant alleviation of 6-OHDA-induced cytotoxicity as evidenced by enhanced activity of the superoxide dismutase (SOD) and catalase (CAT) enzymes. Furthermore, TMC elevated the levels of intra-neuronal dopamine and tyrosine hydroxylase (TH) in differentiated neural cells. The 6-OHDA induced overexpression of α-synuclein was significantly hindered in neural cells pre-treated with TMC. In proteomic analysis, TMC altered the expression of ribosomal proteins, α/β isotypes of tubulins, protein disulphide isomerases (PDI) and heat shock proteins (HSP) in differentiated human neural cells. The natural palm phytonutrient TMC is a potent antioxidant with significant neuroprotective effects against free radical-induced oxidative stress.
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Key Words
- 6-OHDA, 6-hydroxydopamine
- 6-hydroxydopamine
- AD, Alzheimer’s disease
- BCM, beta-carotene-15,15′-monooxygenase
- CAT, catalase
- DRD2, dopamine receptor D2
- Dopamine
- ER, endoplasmic reticulum
- GO, gene ontology
- HSP, Heat shock protein
- HSPA9, Heat shock protein family A (HSP70) member 9
- HSPD1, Heat shock protein family D (HSP60) member 1
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- LC-MS/MS, liquid chromatography-double mass spectrometry
- LDH, lactate dehydrogenase
- MCODE, minimal common oncology data elements
- MS, mass spectrometry
- Mixed carotene
- PD, Parkinson's disease
- PDI, protein disulphide isomerases
- PHB2, prohibitin 2
- PPI, protein–protein interaction
- RAN, Ras-related nuclear protein
- ROS, reactive oxygen species
- RPs, ribosomal proteins
- SH-SY5Y neuroblastoma cells
- SOD, superoxide dismutase
- TH, tyrosine hydroxylase
- TMC, total mixed carotene complex
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25
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Yue Z, Slominski R, Bharti S, Chen JY. PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics. Front Genet 2022; 13:820361. [PMID: 35495152 PMCID: PMC9039620 DOI: 10.3389/fgene.2022.820361] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/17/2022] [Indexed: 12/30/2022] Open
Abstract
Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.
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Affiliation(s)
- Zongliang Yue
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Radomir Slominski
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
- Graduate Biomedical Sciences Program, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Samuel Bharti
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jake Y. Chen
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
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26
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Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers. Transl Psychiatry 2022; 12:142. [PMID: 35383147 PMCID: PMC8983647 DOI: 10.1038/s41398-022-01896-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/05/2022] [Accepted: 03/11/2022] [Indexed: 11/30/2022] Open
Abstract
Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.
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27
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Carr AV, Frey BL, Scalf M, Cesnik AJ, Rolfs Z, Pike KA, Yang B, Keller MP, Jarrard DF, Shortreed MR, Smith LM. MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses. J Proteome Res 2022; 21:410-419. [PMID: 35073098 PMCID: PMC9150505 DOI: 10.1021/acs.jproteome.1c00756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.
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Affiliation(s)
- Austin V Carr
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Brian L Frey
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Mark Scalf
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Anthony J Cesnik
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Zach Rolfs
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Kyndal A Pike
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Bing Yang
- Department of Urology, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Mark P. Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - David F Jarrard
- Department of Urology, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States,Corresponding Author: Telephone: 608-263-2594
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28
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Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021; 49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Citation(s) in RCA: 383] [Impact Index Per Article: 95.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/01/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
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Affiliation(s)
- Parminder S Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Ewan Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
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29
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Abstract
The abundance, localization, modifications, and protein-protein interactions of many host cell and virus proteins can change dynamically throughout the course of any viral infection. Studying these changes is critical for a comprehensive understanding of how viruses replicate and cause disease, as well as for the development of antiviral therapeutics and vaccines. Previously, we developed a mass spectrometry-based technique called quantitative temporal viromics (QTV), which employs isobaric tandem mass tags (TMTs) to allow precise comparative quantification of host and virus proteomes through a whole time course of infection. In this review, we discuss the utility and applications of QTV, exemplified by numerous studies that have since used proteomics with a variety of quantitative techniques to study virus infection through time. Expected final online publication date for the Annual Review of Virology, Volume 8 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
| | - Michael P Weekes
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, United Kingdom;
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30
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Glycoproteomic analysis of the changes in protein N-glycosylation during neuronal differentiation in human-induced pluripotent stem cells and derived neuronal cells. Sci Rep 2021; 11:11169. [PMID: 34045517 PMCID: PMC8160270 DOI: 10.1038/s41598-021-90102-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/04/2021] [Indexed: 11/09/2022] Open
Abstract
N-glycosylation of glycoproteins, a major post-translational modification, plays a crucial role in various biological phenomena. In central nervous systems, N-glycosylation is thought to be associated with differentiation and regeneration; however, the state and role of N-glycosylation in neuronal differentiation remain unclear. Here, we conducted sequential LC/MS/MS analyses of tryptic digest, enriched glycopeptides, and deglycosylated peptides of proteins derived from human-induced pluripotent stem cells (iPSCs) and iPSC-derived neuronal cells, which were used as a model of neuronal differentiation. We demonstrate that the production profiles of many glycoproteins and their glycoforms were altered during neuronal differentiation. Particularly, the levels of glycoproteins modified with an N-glycan, consisting of five N-acetylhexosamines, three hexoses, and a fucose (HN5H3F), increased in dopaminergic neuron-rich cells (DAs). The N-glycan was deduced to be a fucosylated and bisected biantennary glycan based on product ion spectra. Interestingly, the HN5H3F-modified proteins were predicted to be functionally involved in neural cell adhesion, axon guidance, and the semaphorin-plexin signaling pathway, and protein modifications were site-selective and DA-selective regardless of protein production levels. Our integrated method for glycoproteome analysis and resultant profiles of glycoproteins and their glycoforms provide valuable information for further understanding the role of N-glycosylation in neuronal differentiation and neural regeneration.
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Braconi D, Bernardini G, Spiga O, Santucci A. Leveraging proteomics in orphan disease research: pitfalls and potential. Expert Rev Proteomics 2021; 18:315-327. [PMID: 33861161 DOI: 10.1080/14789450.2021.1918549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: The term 'orphan diseases' includes conditions meeting prevalence-based or commercial viability criteria: they affect a small number of individuals and are considered an unviable market for drug development. Proteomics is an important technology to study them, providing information on mechanisms and evolution, biomarkers, and effects of therapeutic interventions.Areas covered: Herein, we review how proteomics and bioinformatic tools could be applied to the study of rare diseases and discuss pitfalls and potential.Expert opinion: Research in the field of rare diseases has to face many challenges, and implementation plans should foresee highly specialized collaborative consortia to create multidisciplinary frameworks for data sharing, advancing research, supporting clinical studies, and accelerating drug development. The integration of different technologies will allow better knowledge of disease pathophysiology, and the inclusion of proteomics and other omics technologies in this context will be pivotal to this aim.Several aspects of rare diseases, often perceived as limiting factors, might actually be advantages for a precision medicine approach: the limited number of patients, the collaboration with patient societies, and the availability of curated clinical registries could allow the development of homogeneous clinical databases and ultimately a better control over the data to be analyzed.
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Affiliation(s)
- Daniela Braconi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Giulia Bernardini
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
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32
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Madeira C, Costa PM. Proteomics in systems toxicology. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:55-91. [PMID: 34340774 DOI: 10.1016/bs.apcsb.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Proteins are the ultimate product of gene expression. As they hinge between gene transcription and phenotype, they offer a more realistic perspective of toxicopathic effects, responses and even susceptibility to insult than targeting genes and mRNAs while dodging some inter-individual variability that hinders measuring downstream endpoints like metabolites or enzyme activity. Toxicologists have long focused on proteins as biomarkers but the advent of proteomics shifted risk assessment from narrow single-endpoint analyses to whole-proteome screening, enabling deriving protein-centric adverse outcome pathways (AOPs), which are pivotal for the derivation of Systems Biology informally named Systems Toxicology. Especially if coupled pathology, the identification of molecular initiating events (MIEs) and AOPs allow predictive modeling of toxicological pathways, which now stands as the frontier for the next generation of toxicologists. Advances in mass spectrometry, bioinformatics, protein databases and top-down proteomics create new opportunities for mechanistic and effects-oriented research in all fields, from ecotoxicology to pharmacotoxicology.
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Affiliation(s)
- Carolina Madeira
- UCIBIO-Applied Molecular Biosciences Unit, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Caparica, Portugal
| | - Pedro M Costa
- UCIBIO-Applied Molecular Biosciences Unit, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Caparica, Portugal.
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Fingleton E, Li Y, Roche KW. Advances in Proteomics Allow Insights Into Neuronal Proteomes. Front Mol Neurosci 2021; 14:647451. [PMID: 33935646 PMCID: PMC8084103 DOI: 10.3389/fnmol.2021.647451] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/25/2021] [Indexed: 11/29/2022] Open
Abstract
Protein–protein interaction networks and signaling complexes are essential for normal brain function and are often dysregulated in neurological disorders. Nevertheless, unraveling neuron- and synapse-specific proteins interaction networks has remained a technical challenge. New techniques, however, have allowed for high-resolution and high-throughput analyses, enabling quantification and characterization of various neuronal protein populations. Over the last decade, mass spectrometry (MS) has surfaced as the primary method for analyzing multiple protein samples in tandem, allowing for the precise quantification of proteomic data. Moreover, the development of sophisticated protein-labeling techniques has given MS a high temporal and spatial resolution, facilitating the analysis of various neuronal substructures, cell types, and subcellular compartments. Recent studies have leveraged these novel techniques to reveal the proteomic underpinnings of well-characterized neuronal processes, such as axon guidance, long-term potentiation, and homeostatic plasticity. Translational MS studies have facilitated a better understanding of complex neurological disorders, such as Alzheimer’s disease (AD), Schizophrenia (SCZ), and Autism Spectrum Disorder (ASD). Proteomic investigation of these diseases has not only given researchers new insight into disease mechanisms but has also been used to validate disease models and identify new targets for research.
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Affiliation(s)
- Erin Fingleton
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, United States
| | - Yan Li
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, United States
| | - Katherine W Roche
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, United States
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Li M, Shang H, Wang T, Yang SQ, Li L. Huanglian decoction suppresses the growth of hepatocellular carcinoma cells by reducing CCNB1 expression. World J Gastroenterol 2021; 27:939-958. [PMID: 33776365 PMCID: PMC7968131 DOI: 10.3748/wjg.v27.i10.939] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/03/2021] [Accepted: 02/01/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Huanglian decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer. AIM To investigate the role and mechanism of Huanglian decoction on HCC cells. METHODS To identify differentially expressed genes (DEGs), we downloaded gene expression profile data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma and Gene Expression Omnibus (GSE45436) databases. We obtained phytochemicals of the four herbs of Huanglian decoction from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. We also established a regulatory network of DEGs and drug target genes and subsequently analyzed key genes using bioinformatics approaches. Furthermore, we conducted in vitro experiments to explore the effect of Huanglian decoction and to verify the predictions. In particular, the CCNB1 gene was knocked down to verify the primary target of this decoction. Through the identification of the expression levels of key proteins, we determined the primary mechanism of Huanglian decoction in HCC. RESULTS Based on the results of the network pharmacological analysis, we revealed 5 bioactive compounds in Huanglian decoction that act on HCC. In addition, a protein-protein interaction network analysis of the target genes of these five compounds as well as expression and prognosis analyses were performed in tumors. CCNB1 was confirmed to be the primary gene that may be highly expressed in tumors and was significantly associated with a worse prognosis. We also noted that CCNB1 may serve as an independent prognostic indicator in HCC. Moreover, in vitro experiments demonstrated that Huanglian decoction significantly inhibited the growth, migration, and invasiveness of HCC cells and induced cell apoptosis and G2/M phase arrest. Further analysis showed that the decoction may inhibit the growth of HCC cells by downregulating the CCNB1 expression level. After Huanglian decoction treatment, the expression levels of Bax, caspase 3, caspase 9, p21 and p53 in HCC cells were increased, while the expression of CDK1 and CCNB1 was significantly decreased. The p53 signaling pathway was also found to play an important role in this process. CONCLUSION Huanglian decoction has a significant inhibitory effect on HCC cells. CCNB1 is a potential therapeutic target in HCC. Further analysis showed that Huanglian decoction can inhibit HCC cell growth by downregulating the expression of CCNB1 to activate the p53 signaling pathway.
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Affiliation(s)
- Min Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo 255036, Shandong Province, China
| | - Hua Shang
- Department of Gastroenterology, Zibo Central Hospital, Zibo 255036, Shandong Province, China
| | - Tao Wang
- Department of General Surgery, Zibo Central Hospital, Zibo 255036, Shandong Province, China
| | - Shui-Qing Yang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Lei Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Department of Pathology, University of Otago, Dunedin px806, New Zealand
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Zeng Y, He H, Zhang L, Zhu W, Shen H, Yan YJ, Deng HW. GWA-based pleiotropic analysis identified potential SNPs and genes related to type 2 diabetes and obesity. J Hum Genet 2021; 66:297-306. [PMID: 32948839 PMCID: PMC7884093 DOI: 10.1038/s10038-020-00843-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/26/2020] [Accepted: 09/06/2020] [Indexed: 01/02/2023]
Abstract
Metabolic syndrome is a cluster of symptoms including excessive body fat and insulin resistance which may lead to obesity and type 2 diabetes (T2D). The physiological and pathological cross-talk between T2D and obesity is crucial and complex, meanwhile, the genetic connection between T2D and obesity is largely unknown. The purpose of this study is to identify pleiotropic SNPs and genes between these two associated conditions by applying genetic analysis incorporating pleiotropy and annotation (GPA) on two large genome-wide association studies (GWAS) data sets: a body mass index (BMI) data set containing 339,224 subjects and a T2D data set containing 110,452 subjects. In all, 5182 SNPs showed pleiotropy in both T2D and obesity. After further prioritization based on suggested local false discovery rates (FDR) by the GPA model, 2146 SNPs corresponding to 217 unique genes are significantly associated with both traits (FDR < 0.2), among which 187 are newly identified pleiotropic genes compare with original GWAS in individual traits. Subsequently, gene enrichment and pathway analyses highlighted several pleiotropic SNPs including rs849135 (FDR = 0.0002), rs2119812 (FDR = 0.0018), rs4506565 (FDR = 1.23E-08), rs1558902 (7.23E-10) and corresponding genes JAZF1, SYN2, TCF7L2, FTO which may play crucial rol5es in the etiology of both T2D and obesity. Additional evidences from expression data analysis of pleiotropic genes strongly supports that the pleiotropic genes including JAZF1 (p = 1.39E-05 and p = 2.13E-05), SYN2 (p = 5.49E-03 and p = 5.27E-04), CDKN2C (p = 1.99E-12 and p = 6.27E-11), RABGAP1 (p = 3.08E-03 and p = 7.46E-03), and UBE2E2 (p = 1.83E-04 and p = 8.22E-03) play crucial roles in both obesity and T2D pathogenesis. Pleiotropic analysis integrated with functional network identified several novel and causal SNPs and genes involved in both BMI and T2D which may be ignored in single GWAS.
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Affiliation(s)
- Yong Zeng
- National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
| | - Hao He
- Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Lan Zhang
- Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Wei Zhu
- Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Hui Shen
- Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA
| | - Yu-Jie Yan
- National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
| | - Hong-Wen Deng
- National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
- Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
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Čanadi Jurešić G, Ćurko-Cofek B, Barbarić M, Mumiši N, Blagović B, Jamnik P. Response of Saccharomyces cerevisiae W303 to Iron and Lead Toxicity in Overloaded Conditions. Curr Microbiol 2021; 78:1188-1201. [PMID: 33624192 DOI: 10.1007/s00284-021-02390-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/07/2021] [Indexed: 11/29/2022]
Abstract
Yeast Saccharomyces cerevisiae is an ideal model organism for studying molecular mechanisms of the stress response provoked by metals. In this work, yeast cells response to iron (Fe3+) or lead (Pb2+) exposure was tested and compared. Survival test was used to determine testing doses of metal ions-for Fe3+ it was 4 mM and for Pb2+ 8 mM. These (high, over-loaded) doses provoked comparable values of growth inhibition, but different values in vitality measurement. The percentage of metabolically active cells, determined by fluorescent FUN-1 dye, was lower in Pb2+ than in Fe3+ treated cells. Besides, endogenous antioxidant defence systems in the cells treated with Pb2+ were less efficient compared to Fe3+. At the mitochondrial level, the effects of metal ions were in correlation with the results of cell metabolic activity. The mitochondrial proteome of Pb2+ treated cells showed the domination of protein downregulation. Yeast cells treated either with Fe3+ or Pb2+ shared 19 common significantly changed proteins. The affected proteins were involved in different cellular process and amongst them only five proteins belong to energy and carbohydrate metabolism, and protein biosynthesis. Based on all obtained results, it is possible to conclude that the effects of Fe3+ and Pb2+ on yeast cells show rather specific patterns of toxicity and stress response.
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Affiliation(s)
- Gordana Čanadi Jurešić
- Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, University of Rijeka, B. Branchetta 20, 51000, Rijeka, Croatia.
| | - Božena Ćurko-Cofek
- Department of Physiology and Immunology, Faculty of Medicine, University of Rijeka, B. Branchetta 20, 51000, Rijeka, Croatia
| | - Martina Barbarić
- Faculty of Medicine, University of Rijeka, B. Branchetta 20, 51000, Rijeka, Croatia
| | - Nermina Mumiši
- Faculty of Medicine, University of Rijeka, B. Branchetta 20, 51000, Rijeka, Croatia
| | - Branka Blagović
- Department of Medical Chemistry, Biochemistry and Clinical Chemistry, Faculty of Medicine, University of Rijeka, B. Branchetta 20, 51000, Rijeka, Croatia
| | - Polona Jamnik
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000, Ljubljana, Slovenia
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Yoo YJ. Coevolution of Mathematics, Statistics, and Genetics. HANDBOOK OF THE MATHEMATICS OF THE ARTS AND SCIENCES 2021:2039-2071. [DOI: 10.1007/978-3-319-57072-3_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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Tahery N, Khodadost M, Jahani Sherafat S, Rezaei Tavirani M, Ahmadi N, Montazer F, Rezaei Tavirani M, Naderi N. C-reactive protein as a possible marker for severity and mortality of COVID-19 infection. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2021; 14:S118-S122. [PMID: 35154611 PMCID: PMC8817756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/29/2021] [Indexed: 11/24/2022]
Abstract
AIM The present study aimed to introduce a possible biomarker to differentiate between severe and fatal conditions of COVID-19. BACKGROUND The COVID-19 pandemic, appearing as a complicated health problem, has changed the lifestyle of people in recent years. Clinical findings indicate mild, severe, and fatal conditions of this disease. Prediction of disease severity is a significant point in managing COVID-19 infection. METHODS In this study, 195 differentially expressed genes (DEGs) that discriminate between fatal and severe conditions in patients were extracted from the literature and screened to determine the significant ones. The significant DEGs plus the 90 first neighbors added from the STRING database were included in the interactome using Cytoscape software v 3.7.2. The central nodes of the analyzed network were identified and assessed. RESULTS Ten significant DEGs were candidates for assessment, of which 9 were recognized by the STRING database. IL6, ALB, TNF, CRP, INS, MPO, C3, CXCL8, TTR, and TLR4 were determined as central nodes; IL6, CRP, and TTR were highlighted as the critical genes related to the severity of COVID-19 infection. CONCLUSION CRP was identified as the best possible biomarker with levels related to the severity and fatality of COVID-19 infection.
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Affiliation(s)
| | - Mahmood Khodadost
- School of Traditional Medicine Shahid, Beheshti University of Medical Sciences, Tehran, Iran
| | - Somayeh Jahani Sherafat
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nayebali Ahmadi
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Montazer
- Firoozabadi Clinical Research Development Unit, Iran University of Medical Sciences, Tehran, Iran
| | | | - Nosratollah Naderi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Zuo H, Chen L, Li N, Song Q. Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer. Front Genet 2020; 11:612196. [PMID: 33414811 PMCID: PMC7782244 DOI: 10.3389/fgene.2020.612196] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 11/30/2020] [Indexed: 12/13/2022] Open
Abstract
Pancreatic cancer is known as "the king of cancer," and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.
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Affiliation(s)
- Hao Zuo
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Research Center for Precision Medicine of Cancer, Wuhan, China
| | - Luojun Chen
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Research Center for Precision Medicine of Cancer, Wuhan, China
| | - Na Li
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Research Center for Precision Medicine of Cancer, Wuhan, China
| | - Qibin Song
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.,Hubei Provincial Research Center for Precision Medicine of Cancer, Wuhan, China
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Guerrero CR, Maier LA, Griffin TJ, Higgins L, Najt CP, Perlman DM, Bhargava M. Application of Proteomics in Sarcoidosis. Am J Respir Cell Mol Biol 2020; 63:727-738. [PMID: 32804537 PMCID: PMC12036602 DOI: 10.1165/rcmb.2020-0070ps] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/17/2020] [Indexed: 02/03/2023] Open
Abstract
Sarcoidosis is a multisystem disease with heterogeneity in manifestations and outcomes. System-level studies leveraging "omics" technologies are expected to define mechanisms contributing to sarcoidosis heterogeneous manifestations and course. With improvements in mass spectrometry (MS) and bioinformatics, it is possible to study protein abundance for a large number of proteins simultaneously. Contemporary fast-scanning MS enables the acquisition of spectral data for deep coverage of the proteins with data-dependent or data-independent acquisition MS modes. Studies leveraging MS-based proteomics in sarcoidosis have characterized BAL fluid (BALF), alveolar macrophages, plasma, and exosomes. These studies identified several differentially expressed proteins, including protocadherin-2 precursor, annexin A2, pulmonary surfactant A2, complement factors C3, vitamin-D-binding protein, cystatin B, and amyloid P, comparing subjects with sarcoidosis with control subjects. Other studies identified ceruloplasmin, complement factors B, C3, and 1, and others with differential abundance in sarcoidosis compared with other interstitial lung diseases. Using quantitative proteomics, most recent studies found differences in PI3K/Akt/mTOR, MAP kinase, pluripotency-associated transcriptional factor, and hypoxia response pathways. Other studies identified increased clathrin-mediated endocytosis and Fcγ receptor-mediated phagocytosis pathways in sarcoidosis alveolar macrophages. Although studies in mixed BAL and blood cells or plasma are limited, some of the changes in lung compartment are detected in the blood cells and plasma. We review proteomics for sarcoidosis with a focus on the existing MS data acquisition strategies, bioinformatics for spectral data analysis to infer protein identity and quantity, unique aspects about biospecimen collection and processing for lung-related proteomics, and proteomics studies conducted to date in sarcoidosis.
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Affiliation(s)
- Candance R Guerrero
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Lisa A Maier
- Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, Colorado
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - Charles P Najt
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Sciences and
| | - David M Perlman
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
| | - Maneesh Bhargava
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota; and
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Wu Y, Xu Y. Bioinformatics for The Prognostic Value and Function of Cubilin (CUBN) in Colorectal Cancer. Med Sci Monit 2020; 26:e922447. [PMID: 33235183 PMCID: PMC7702664 DOI: 10.12659/msm.922447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background Cubilin (CUBN) gene was reported to be a novel risk variant for colorectal cancer (CRC). Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed CRC to identify CUBN that can predict patient outcome and function by using bioinformatics. Material/Methods The association in expression, clinicopathological parameters, and survival were analyzed by using Oncomine, UNCLA, and GEPIA, while CUBN alterations and related functional networks were identified using cBioPortal. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) of CUBN in CRC were explored by using LinkOmics. Gene set enrichment analysis (GSEA) examined target networks of kinases, miRNAs, and transcription factors. Results We found that CUBN was overexpressed in CRC. Patients who were in advanced TNM stage tended to express higher CUBN mRNA levels, while those who received radiotherapy tended to express relatively lower CUBN mRNA levels. Higher expression of CUBN was found to be associated with shorter overall survival (OS) and disease-free survival (DFS). Moreover, functional networks analysis suggested that CUBN can regulate mismatch repair, terpenoid backbone biosynthesis, base excision repair, and proteasome via vitamin digestion and absorption pathway to influence CRC occurrence. Conclusions These findings suggested that CUBN could serve as a prognostic and therapeutic biomarker of CRC in the future.
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Affiliation(s)
- Yibin Wu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China (mainland)
| | - Ye Xu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China (mainland)
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Liu J, Yan Y, Yan J, Wang J, Wei J, Xiao J, Zeng Y, Feng H. Multi-omics analysis revealed crucial genes and pathways associated with black carp antiviral innate immunity. FISH & SHELLFISH IMMUNOLOGY 2020; 106:724-732. [PMID: 32871249 DOI: 10.1016/j.fsi.2020.08.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
Multi-omics strategy contributes as an indispensable and efficient approach for the investigation of the innate immunity in vertebrates. To explore the crucial genes and pathways involved in the antiviral innate immunity of black carp (Mylopharyngodon piceus), the comparative phosphoproteomics and transcriptomics of Mylopharyngodon piceus kidney (MPK) cells with/without GCRV infection were performed in this manuscript. In phosphoproteomics analysis, 2637 phosphosites corresponding to 1532 proteins were identified and quantified, in which 1372 proteins were identified as differentially expressed proteins (DEPs) with 683 upregulated and 689 downregulated in GCRV infected cells. Functional annotation, enrichment analysis and pathway analysis highlighted that a large number of DEPs were enriched in immune related pathways including TLR pathway and NLR pathway. In transcriptomics analysis, a total of 2936 genes were identified as differentially expressed genes (DEGs), in which 2290 and 646 genes were upregulated and downregulated respectively after GCRV infection. As expected, pathway analysis based on DEGs also showed that a large proportion of DEGs were enriched in immune related pathways including TLR and RLR pathway. A combined list of DEPs and DEGs that enriched in above pathways were imported in Cytoscape for network analysis, reconstruction and visualization. The integrative study suggested that several significant DEPs and DEGs, such as MAP3K7 (TAK1), JUN, MAP2K2, CASP8, IL8 and IRF7 might be functionally crucial in host antiviral innate immunity. Thus, this study contributes as an indispensable reference map for the further investigation of the innate immune system of black carp.
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Affiliation(s)
- Ji Liu
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Yujie Yan
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
| | - Jun Yan
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Junting Wang
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China
| | - Jing Wei
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Jun Xiao
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; College of Fisheries, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yong Zeng
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; National & Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
| | - Hao Feng
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, 410081, China.
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Parthasarathy A, Kalesh K. Defeating the trypanosomatid trio: proteomics of the protozoan parasites causing neglected tropical diseases. RSC Med Chem 2020; 11:625-645. [PMID: 33479664 PMCID: PMC7549140 DOI: 10.1039/d0md00122h] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/12/2020] [Indexed: 12/20/2022] Open
Abstract
Mass spectrometry-based proteomics enables accurate measurement of the modulations of proteins on a large scale upon perturbation and facilitates the understanding of the functional roles of proteins in biological systems. It is a particularly relevant methodology for studying Leishmania spp., Trypanosoma cruzi and Trypanosoma brucei, as the gene expression in these parasites is primarily regulated by posttranscriptional mechanisms. Large-scale proteomics studies have revealed a plethora of information regarding modulated proteins and their molecular interactions during various life processes of the protozoans, including stress adaptation, life cycle changes and interactions with the host. Important molecular processes within the parasite that regulate the activity and subcellular localisation of its proteins, including several co- and post-translational modifications, are also accurately captured by modern proteomics mass spectrometry techniques. Finally, in combination with synthetic chemistry, proteomic techniques facilitate unbiased profiling of targets and off-targets of pharmacologically active compounds in the parasites. This provides important data sets for their mechanism of action studies, thereby aiding drug development programmes.
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Affiliation(s)
- Anutthaman Parthasarathy
- Rochester Institute of Technology , Thomas H. Gosnell School of Life Sciences , 85 Lomb Memorial Dr , Rochester , NY 14623 , USA
| | - Karunakaran Kalesh
- Department of Chemistry , Durham University , Lower Mount Joy, South Road , Durham DH1 3LE , UK .
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Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges. ENTROPY 2020; 22:e22040427. [PMID: 33286201 PMCID: PMC7516904 DOI: 10.3390/e22040427] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/18/2020] [Accepted: 04/03/2020] [Indexed: 12/22/2022]
Abstract
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
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Seo DY, Yoon CS, Dizon LA, Lee SR, Youm JB, Yang WS, Kwak HB, Ko TH, Kim HK, Han J, McGregor RA. Circadian modulation of the cardiac proteome underpins differential adaptation to morning and evening exercise training: an LC-MS/MS analysis. Pflugers Arch 2020; 472:259-269. [PMID: 32025886 DOI: 10.1007/s00424-020-02350-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/09/2020] [Accepted: 01/16/2020] [Indexed: 12/11/2022]
Abstract
All living beings on earth are influenced by the circadian rhythm, the rising and the setting of the sun. The ubiquitous effect of exercise is widely believed to maximize health benefits but has not been formally investigated for cardiac responses in the exercise-induced circadian rhythms. We hypothesized that the exercise-related proteome is differentially influenced by circadian rhythm and analyzed the differences between the effects of morning and evening exercise. Twenty-four Sprague-Dawley rats were randomly divided into four groups (n = 6 per group): morning control, morning exercise, evening control, and evening exercise groups. The exercise groups were subjected to 12-week treadmill exercise (5 days/week) performed either during daytime or nighttime. After 12 weeks, the physiological characteristics (e.g., body weight, heart weight, visceral fat, and blood metabolites), cardiovascular capacity (ejection fraction (%) and fractional shortening (%)), circadian gene expression levels (clock, ball1, per1, per2, cry1, and cry2), and the proteomic data were obtained and subjected to univariate and multivariate analysis. The mRNA levels of per1 and cry2 increased in the evening group compared with those in the morning group. We also found that per2 decreased and cry2 increased in the evening exercise groups. The evening exercise groups showed more decreased triacylglycerides and increased blood insulin levels than the morning exercise group. The principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis indicated that the circadian rhythm differently influenced the protein networks of the exercise groups. In the morning exercise group, the transcription-translation feedback loop (TTFL) (clock, per1, per2, cry1, and cry2) formed a protein-protein interaction network with Nme2, Hint1, Ddt, Ndufb8, Ldha, and Eef1a2. In contrast, the TTFL group appeared close to Maoa, Hist2h4, and Macrod1 in the evening exercise group. Interestingly, the evening exercise group decreased the mRNA level of per2 but not per1. Per1 and Per2 are known to transport Cry1 and Cry2 into the nucleus. Taken together, we summarized the characteristics of enriched proteins in the aspect of their molecular function, cellular component, and biological process. Our results might provide a better understanding of the circadian effect on exercise-related proteins.
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Affiliation(s)
- Dae Yun Seo
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Chang Shin Yoon
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Louise Anne Dizon
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Sung Ryul Lee
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Jae Boum Youm
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Won Suk Yang
- Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, 151-742, South Korea
| | - Hyo-Bum Kwak
- Department of Kinesiology, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, South Korea
| | - Tae Hee Ko
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Hyoung Kyu Kim
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
| | - Jin Han
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea.
| | - Robin A McGregor
- National Research Laboratory for Mitochondrial Signaling, Department of Physiology, Department of Health Sciences and Technology, BK21 Project Team, College of Medicine, Cardiovascular and Metabolic Disease Center, Inje University, Bokji-ro 75, Busanjin-gu, Busan, 47392, South Korea
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Ulgen E, Ozisik O, Sezerman OU. pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front Genet 2019; 10:858. [PMID: 31608109 PMCID: PMC6773876 DOI: 10.3389/fgene.2019.00858] [Citation(s) in RCA: 278] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.
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Affiliation(s)
- Ege Ulgen
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Ozan Ozisik
- Department of Computer Engineering, Electrical & Electronics Faculty, Yildiz Technical University, Istanbul, Turkey
| | - Osman Ugur Sezerman
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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Glazko G, Zybailov B, Emmert-Streib F, Baranova A, Rahmatallah Y. Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study. PLoS One 2019; 14:e0221444. [PMID: 31437237 PMCID: PMC6705791 DOI: 10.1371/journal.pone.0221444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 08/06/2019] [Indexed: 01/10/2023] Open
Abstract
Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.
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Affiliation(s)
- Galina Glazko
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Frank Emmert-Streib
- Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu, Tampere, Finland FI
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Manassas VA, United States of America
- Research Center for Medical Genetics, Moscow, Russia
| | - Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
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Malatras A, Duguez S, Duddy W. Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field. Skelet Muscle 2019; 9:10. [PMID: 31053169 PMCID: PMC6498474 DOI: 10.1186/s13395-019-0196-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 04/09/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology. METHODS Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline. Two hundred eighty-two samples were discarded based on a battery of rigorous global quality controls. From among the remaining studies, 578 comparisons of interest were identified by a combination of text mining and manual curation of the study meta-data. For each comparison, significantly dysregulated genes (FDR adjusted p < 0.05) were identified. RESULTS Lists of dysregulated genes were divided between upregulated and downregulated to give 1156 Muscle Gene Sets (MGS). This resource is available for download ( www.sys-myo.com/muscle_gene_sets ) and is accessible through three commonly used functional genomics platforms (GSEA, EnrichR, and WebGestalt). Basic guidance and recommendations are provided for the use of MGS through these platforms. In addition, consensus muscle gene sets were created to capture the overlap between the results of similar studies, and analysis of these highlighted the potential for novel disease-relevant findings. CONCLUSIONS The MGS resource can be used to investigate the behaviour of any list of genes across previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the Gene Ontology. Its major intended use is in enrichment testing for functional genomics analysis.
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Affiliation(s)
- Apostolos Malatras
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, 1 University Avenue, 2109 Nicosia, Cyprus
| | - Stephanie Duguez
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
| | - William Duddy
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
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Montoya A, López MC, Vélez ID, Robledo SM. Label-free quantitative proteomic analysis reveals potential biomarkers for early healing in cutaneous leishmaniasis. PeerJ 2019; 6:e6228. [PMID: 30648003 PMCID: PMC6330957 DOI: 10.7717/peerj.6228] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 12/06/2018] [Indexed: 01/08/2023] Open
Abstract
Background Leishmaniasis is a parasitic disease caused by more than 20 species of the Leishmania genus. The disease is globally distributed and is endemic in 97 countries and three territories in the tropical and subtropical regions. The efficacy of the current treatments is becoming increasingly low either due to incomplete treatment or resistant parasites. Failure of treatment is frequent, and therefore, the search for early biomarkers of therapeutic response in cutaneous leishmaniasis (CL) is urgently needed. Objective The aim of this study was to compare the proteomic profiles in patients with CL before and after 7 days of treatment and identify early biomarkers of curative response. Methods Four patients with a parasitological diagnosis of leishmaniasis with confirmation of species by PCR-RFLP were recruited. All patients had a single lesion, and a protein from the middle of the ulcer was quantified by liquid chromatography and mass spectrometry. Results A total of 12 proteins showed differential expression in the comparative LC-electrospray ionization MS/MS (LC-ESI-MS/MS) triplicate analysis. Seven of them were up-regulated and five of them were down-regulated. Calcium binding proteins A2, A8, and A9 and hemoglobin subunits alpha-2 and delta showed high correlation with epidermis development and immune response. Conclusion We identified changes in the profiles of proteins that had a positive therapeutic response to the treatment. The proteins identified with differential expression are related to the reduction of inflammation and increased tissue repair. These proteins can be useful as biomarkers for early monitoring of therapeutic response in CL.
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Affiliation(s)
- Andrés Montoya
- PECET, Facultad de Medicina, Universidad de Antioquia, Medellin, Antioquia, Colombia
| | - Manuel Carlos López
- Molecular Biology Department Consejo Superior de Investigaciones Científicas, Instituto de Parasitología y Biomedicina "López Neyra", Granade, Spain
| | - Ivan D Vélez
- PECET, Facultad de Medicina, Universidad de Antioquia, Medellin, Antioquia, Colombia
| | - Sara M Robledo
- PECET, Facultad de Medicina, Universidad de Antioquia, Medellin, Antioquia, Colombia
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Pascovici D, Wu JX, McKay MJ, Joseph C, Noor Z, Kamath K, Wu Y, Ranganathan S, Gupta V, Mirzaei M. Clinically Relevant Post-Translational Modification Analyses-Maturing Workflows and Bioinformatics Tools. Int J Mol Sci 2018; 20:E16. [PMID: 30577541 PMCID: PMC6337699 DOI: 10.3390/ijms20010016] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 12/09/2018] [Accepted: 12/17/2018] [Indexed: 01/04/2023] Open
Abstract
Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.
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Affiliation(s)
- Dana Pascovici
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
| | - Jemma X Wu
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
| | - Matthew J McKay
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
| | - Chitra Joseph
- Department of Clinical Medicine, Macquarie University, Sydney, NSW 2109, Australia.
| | - Zainab Noor
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
| | - Karthik Kamath
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
| | - Yunqi Wu
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
| | - Vivek Gupta
- Department of Clinical Medicine, Macquarie University, Sydney, NSW 2109, Australia.
| | - Mehdi Mirzaei
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia.
- Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia.
- Department of Clinical Medicine, Macquarie University, Sydney, NSW 2109, Australia.
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