1
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Sprang M, Möllmann J, Andrade-Navarro MA, Fontaine JF. Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets. Genome Biol 2024; 25:222. [PMID: 39152483 PMCID: PMC11328481 DOI: 10.1186/s13059-024-03331-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/08/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Reproducibility is a major concern in biomedical studies, and existing publication guidelines do not solve the problem. Batch effects and quality imbalances between groups of biological samples are major factors hampering reproducibility. Yet, the latter is rarely considered in the scientific literature. RESULTS Our analysis uses 40 clinically relevant RNA-seq datasets to quantify the impact of quality imbalance between groups of samples on the reproducibility of gene expression studies. High-quality imbalance is frequent (14 datasets; 35%), and hundreds of quality markers are present in more than 50% of the datasets. Enrichment analysis suggests common stress-driven effects among the low-quality samples and highlights a complementary role of transcription factors and miRNAs to regulate stress response. Preliminary ChIP-seq results show similar trends. Quality imbalance has an impact on the number of differential genes derived by comparing control to disease samples (the higher the imbalance, the higher the number of genes), on the proportion of quality markers in top differential genes (the higher the imbalance, the higher the proportion; up to 22%) and on the proportion of known disease genes in top differential genes (the higher the imbalance, the lower the proportion). We show that removing outliers based on their quality score improves the resulting downstream analysis. CONCLUSIONS Thanks to a stringent selection of well-designed datasets, we demonstrate that quality imbalance between groups of samples can significantly reduce the relevance of differential genes, consequently reducing reproducibility between studies. Appropriate experimental design and analysis methods can substantially reduce the problem.
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Affiliation(s)
- Maximilian Sprang
- Faculty of Biology, Johannes Gutenberg-Universität Mainz, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Mainz, 55128, Germany
| | - Jannik Möllmann
- Faculty of Biology, Johannes Gutenberg-Universität Mainz, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Mainz, 55128, Germany
| | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg-Universität Mainz, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Mainz, 55128, Germany.
| | - Jean-Fred Fontaine
- Faculty of Biology, Johannes Gutenberg-Universität Mainz, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Mainz, 55128, Germany
- Central Institute for Decision Support Systems in Crop Protection (ZEPP), Rüdesheimer Str. 60-68, Bad Kreuznach, 55545, Germany
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2
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Bontempi D, Nuernberg L, Pai S, Krishnaswamy D, Thiriveedhi V, Hosny A, Mak RH, Farahani K, Kikinis R, Fedorov A, Aerts HJWL. End-to-end reproducible AI pipelines in radiology using the cloud. Nat Commun 2024; 15:6931. [PMID: 39138215 PMCID: PMC11322541 DOI: 10.1038/s41467-024-51202-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 07/30/2024] [Indexed: 08/15/2024] Open
Abstract
Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.
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Affiliation(s)
- Dennis Bontempi
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Leonard Nuernberg
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Suraj Pai
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Deepa Krishnaswamy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Vamsi Thiriveedhi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
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3
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Ogrodnik M, Carlos Acosta J, Adams PD, d'Adda di Fagagna F, Baker DJ, Bishop CL, Chandra T, Collado M, Gil J, Gorgoulis V, Gruber F, Hara E, Jansen-Dürr P, Jurk D, Khosla S, Kirkland JL, Krizhanovsky V, Minamino T, Niedernhofer LJ, Passos JF, Ring NAR, Redl H, Robbins PD, Rodier F, Scharffetter-Kochanek K, Sedivy JM, Sikora E, Witwer K, von Zglinicki T, Yun MH, Grillari J, Demaria M. Guidelines for minimal information on cellular senescence experimentation in vivo. Cell 2024; 187:4150-4175. [PMID: 39121846 DOI: 10.1016/j.cell.2024.05.059] [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: 11/29/2023] [Revised: 05/27/2024] [Accepted: 05/31/2024] [Indexed: 08/12/2024]
Abstract
Cellular senescence is a cell fate triggered in response to stress and is characterized by stable cell-cycle arrest and a hypersecretory state. It has diverse biological roles, ranging from tissue repair to chronic disease. The development of new tools to study senescence in vivo has paved the way for uncovering its physiological and pathological roles and testing senescent cells as a therapeutic target. However, the lack of specific and broadly applicable markers makes it difficult to identify and characterize senescent cells in tissues and living organisms. To address this, we provide practical guidelines called "minimum information for cellular senescence experimentation in vivo" (MICSE). It presents an overview of senescence markers in rodent tissues, transgenic models, non-mammalian systems, human tissues, and tumors and their use in the identification and specification of senescent cells. These guidelines provide a uniform, state-of-the-art, and accessible toolset to improve our understanding of cellular senescence in vivo.
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Affiliation(s)
- Mikolaj Ogrodnik
- Ludwig Boltzmann Research Group Senescence and Healing of Wounds, 1200 Vienna, Austria; Ludwig Boltzmann Institute for Traumatology, The Research Centre in Cooperation with AUVA, 1200 Vienna, Austria; Austrian Cluster for Tissue Regeneration, Vienna, Austria.
| | - Juan Carlos Acosta
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK; Instituto de Biomedicina y Biotecnología de Cantabria, IBBTEC (CSIC, Universidad de Cantabria), C/ Albert Einstein 22, 39011 Santander, Spain
| | - Peter D Adams
- Cancer Genome and Epigenetics Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Fabrizio d'Adda di Fagagna
- IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Institute of Molecular Genetics IGM-CNR "Luigi Luca Cavalli-Sforza," Pavia, Italy
| | - Darren J Baker
- Department of Biochemistry and Molecular Biology, Department of Pediatric and Adolescent Medicine, Robert and Arlene Kogod Center on Aging, Mayo Clinic, 200 First Steet SW, Rochester, MN 55905, USA
| | - Cleo L Bishop
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London E1 2AT, UK
| | - Tamir Chandra
- MRC Human Generics Unit, University of Edinburgh, Edinburgh, UK
| | - Manuel Collado
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Department of Immunology and Oncology (DIO), Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
| | - Jesus Gil
- MRC Laboratory of Medical Sciences (LMS), Du Cane Road, London W12 0NN, UK; Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Vassilis Gorgoulis
- Molecular Carcinogenesis Group, Department of Histology and Embryology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece; Ninewells Hospital and Medical School, University of Dundee, Dundee DD19SY, UK; Faculty Institute for Cancer Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M20 4GJ, UK; Faculty of Health and Medical Sciences, University of Surrey, Surrey GU2 7YH, UK
| | - Florian Gruber
- Department of Dermatology, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Skin Multimodal Imaging of Aging and Senescence - SKINMAGINE, Vienna, Austria
| | - Eiji Hara
- Research Institute for Microbial Diseases (RIMD), Osaka University, Suita 565-0871, Japan; Immunology Frontier Research Center (IFReC), Osaka University, Suita 565-0871, Japan
| | - Pidder Jansen-Dürr
- Institute for Biomedical Aging Research, University of Innsbruck, and Center for Molecular Biosciences Innsbruck (CMBI), Innsbruck, Austria
| | - Diana Jurk
- Mayo Clinic, Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Sundeep Khosla
- Kogod Center on Aging and Division of Endocrinology, Mayo Clinic, Rochester, MN, USA
| | - James L Kirkland
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Valery Krizhanovsky
- Department of Molecular Cell Biology, The Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Tohru Minamino
- Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan; Japan Agency for Medical Research and Development-Core Research for Evolutionary Medical Science and Technology (AMED-CREST), Japan Agency for Medical Research and Development, Tokyo 100-0004, Japan
| | - Laura J Niedernhofer
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6-155 Jackson Hall, 321 Church Street, SE, Minneapolis, MN 55455, USA
| | - João F Passos
- Mayo Clinic, Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Rochester, MN, USA
| | - Nadja A R Ring
- Ludwig Boltzmann Research Group Senescence and Healing of Wounds, 1200 Vienna, Austria; Ludwig Boltzmann Institute for Traumatology, The Research Centre in Cooperation with AUVA, 1200 Vienna, Austria; Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Heinz Redl
- Ludwig Boltzmann Research Group Senescence and Healing of Wounds, 1200 Vienna, Austria; Ludwig Boltzmann Institute for Traumatology, The Research Centre in Cooperation with AUVA, 1200 Vienna, Austria; Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Paul D Robbins
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 6-155 Jackson Hall, 321 Church Street, SE, Minneapolis, MN 55455, USA
| | - Francis Rodier
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada; Institut du cancer de Montréal, Montreal, QC, Canada; Université de Montréal, Département de radiologie, radio-oncologie et médicine nucléaire, Montreal, QC, Canada
| | - Karin Scharffetter-Kochanek
- Department f Dermatology and Allergic Diseases, Ulm University Hospital, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - John M Sedivy
- Department of Molecular, Cellular Biology and Biochemistry, Center on the Biology of Aging, Brown University, Providence, RI, USA
| | - Ewa Sikora
- Laboratory of Molecular Bases of Aging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Kenneth Witwer
- The Johns Hopkins University School of Medicine, Department of Molecular and Comparative Pathobiology, Baltimore, MD, USA; The Johns Hopkins University School of Medicine, Department of Neurology, Baltimore, MD, USA
| | - Thomas von Zglinicki
- Newcastle University Biosciences Institute, Ageing Biology Laboratories, Newcastle upon Tyne, UK
| | - Maximina H Yun
- Technische Universität Dresden, CRTD/Center for Regenerative Therapies Dresden, Dresden, Germany; Max Planck Institute of Molecular Cellular Biology and Genetics, Dresden, Germany; Physics of Life Excellence Cluster, Dresden, Germany
| | - Johannes Grillari
- Ludwig Boltzmann Institute for Traumatology, The Research Centre in Cooperation with AUVA, 1200 Vienna, Austria; Austrian Cluster for Tissue Regeneration, Vienna, Austria; Institute of Molecular Biotechnology, BOKU University, Vienna, Austria.
| | - Marco Demaria
- European Research Institute for the Biology of Ageing (ERIBA), University Medical Center Groningen (UMCG), University of Groningen (RUG), Groningen, the Netherlands.
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4
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Chen J, Wang C, Tu W, Zhang K, Fent K, Dai J, Hecker M, Giesy JP, Zhao Y. AOP-Anchored Transcriptome Analysis Catalogue Accelerates the Discovery of Environmental Toxicants in Zebrafish. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39018385 DOI: 10.1021/acs.est.4c03100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
Current toxicity screening approaches to evaluate the vast number of environmental chemicals that require assessment are hampered due to their significant costs, time requirements, and reliance on live animal testing. The aim of the present study was to develop an adverse outcome pathway (AOP)-anchored transcriptome analysis (AATA) catalogue to expedite the discovery of environmental toxicants. 437 AOPs from the AOPwiki (https://aopwiki.org/) and 2280 transcriptomics data sets from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress (AE) repositories were comprehensively reviewed and analyzed. By using the differentially expressed molecular key event (mKE) genes as connection nodes, we created a large-scale environmental substance─target gene (mKE)─predicted adverse outcomes (SGAs) network that included 78 substances, 1099 genes, and 354 adverse outcomes (AOs). To validate the reliability of the network, comprehensive literature verification was conducted. We demonstrated that 164 of the 354 AOs identified have been previously characterized in the literature. The results for 136 of these AOs were consistent with the predictions of the AATA catalogue, representing an accuracy rate of 82.9%. Besides, distinct patterns in molecular KEs and AOs among categories of substances, such as biocides and metals, were demonstrated. Some representative substances, including atrazine and copper, pose significant risks to fish at various levels of biological organization. Moreover, experimental verification of the AATA predictions was conducted, including exposures of zebrafish to perfluorooctanesulfonate, cresyl diphenyl phosphate, and lanthanum. Results demonstrated consistency with predictions of the AATA catalogue, with an accuracy rate of 92.3%. Collectively, the present findings support the AATA catalogue as an efficient and promising platform for identifying environmental toxicants to fish and thereby provide novel insights into the understanding of potential risks of environmental contaminants.
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Affiliation(s)
- Jierong Chen
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Congcong Wang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Wenqing Tu
- School of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
| | - Kun Zhang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Karl Fent
- Department of Environmental Systems Science, ETH Zürich, Institute of Biogeochemistry and Pollution Dynamics, CH-8092 Zürich, Switzerland
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Markus Hecker
- School of the Environment and Sustainability and Toxicology Centre, University of Saskatchewan, Saskatoon, Canada SK S7N 5B4
| | - John P Giesy
- Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Canada SK S7N 5B4
- Department of Integrative Biology and Center for Integrative Toxicology, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Environmental Sciences, Baylor University, Waco, Texas 76706, United States
| | - Yanbin Zhao
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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5
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Browne DJ, Miller CM, Doolan DL. Technical pitfalls when collecting, cryopreserving, thawing, and stimulating human T-cells. Front Immunol 2024; 15:1382192. [PMID: 38812513 PMCID: PMC11133553 DOI: 10.3389/fimmu.2024.1382192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
The collection, cryopreservation, thawing, and culture of peripheral blood mononuclear cells (PBMCs) can profoundly influence T cell viability and immunogenicity. Gold-standard PBMC processing protocols have been developed by the Office of HIV/AIDS Network Coordination (HANC); however, these protocols are not universally observed. Herein, we have explored the current literature assessing how technical variation during PBMC processing can influence cellular viability and T cell immunogenicity, noting inconsistent findings between many of these studies. Amid the mounting concerns over scientific replicability, there is growing acknowledgement that improved methodological rigour and transparent reporting is required to facilitate independent reproducibility. This review highlights that in human T cell studies, this entails adopting stringent standardised operating procedures (SOPs) for PBMC processing. We specifically propose the use of HANC's Cross-Network PBMC Processing SOP, when collecting and cryopreserving PBMCs, and the HANC member network International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) PBMC Thawing SOP when thawing PBMCs. These stringent and detailed protocols include comprehensive reporting procedures to document unavoidable technical variations, such as delayed processing times. Additionally, we make further standardisation and reporting recommendations to minimise and document variability during this critical experimental period. This review provides a detailed overview of the challenges inherent to a procedure often considered routine, highlighting the importance of carefully considering each aspect of SOPs for PBMC collection, cryopreservation, thawing, and culture to ensure accurate interpretation and comparison between studies.
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Affiliation(s)
- Daniel J. Browne
- Centre for Molecular Therapeutics, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia
| | - Catherine M. Miller
- College of Medicine and Dentistry, James Cook University, Cairns, QLD, Australia
| | - Denise L. Doolan
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia
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6
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Claussnitzer M, Parikh VN, Wagner AH, Arbesfeld JA, Bult CJ, Firth HV, Muffley LA, Nguyen Ba AN, Riehle K, Roth FP, Tabet D, Bolognesi B, Glazer AM, Rubin AF. Minimum information and guidelines for reporting a multiplexed assay of variant effect. Genome Biol 2024; 25:100. [PMID: 38641812 PMCID: PMC11027375 DOI: 10.1186/s13059-024-03223-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 03/25/2024] [Indexed: 04/21/2024] Open
Abstract
Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
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Affiliation(s)
- Melina Claussnitzer
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, 02142, USA
| | - Victoria N Parikh
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA
| | - Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43215, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | - Helen V Firth
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Dept of Medical Genetics, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Lara A Muffley
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alex N Nguyen Ba
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Daniel Tabet
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Benedetta Bolognesi
- Institute for Bioengineering of Catalunya (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Andrew M Glazer
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.
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7
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Wright A, Wilkinson MD, Mungall C, Cain S, Richards S, Sternberg P, Provin E, Jacobs JL, Geib S, Raciti D, Yook K, Stein L, Molik DC. FAIR Header Reference genome: a TRUSTworthy standard. Brief Bioinform 2024; 25:bbae122. [PMID: 38555475 PMCID: PMC10981671 DOI: 10.1093/bib/bbae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/16/2024] [Accepted: 02/22/2024] [Indexed: 04/02/2024] Open
Abstract
The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.
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Affiliation(s)
- Adam Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Mark D Wilkinson
- Departamento de Biotecnolog’ıa-Biolog’ıa Vegetal, Escuela T’ecnica Superior de Ingenier’ıa Agron’omica, Alimentaria y de Biosistemas,Centro de Biotecnolog’ıa y Gen’omica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Polit’ecnica de Madrid (UPM) - Instituto Nacional de Investigaci’on y Tecnolog’ıa Agraria y Alimentaria (INIA/CSIC), Pozuelo de Alarc’on, Madrid, ES, Spain
| | - Christopher Mungall
- Biosystems Data Science, Lawrence Berkeley National Laboratory, Building: 977, 1 Cyclotron Rd, Berkeley, CA 94720, USA
| | - Scott Cain
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Stephen Richards
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, MS: BCM226, Houston, TX 77030, USA
| | - Paul Sternberg
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ellen Provin
- Department of Horticultural Studies, Texas A&M University, HFSB 204, TAMU 2133, College Station, TX 77848, USA
| | - Jonathan L Jacobs
- American Type Culture Collection, 10801 University Blvd, Manassas, VA 20110, USA
| | - Scott Geib
- Tropical Pest Genetics and Molecular Biology Research Unit, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, United States Department of Agriculture, Agricultural Research Service, 64 Nowelo St, Hilo HI 96720, USA
| | - Daniela Raciti
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Karen Yook
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lincoln Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - David C Molik
- Arthropod-borne Animal Diseases Research Unit, Center for Grain and Animal Health Research United States Department of Agriculture, Agricultural Research Service, 1515 College Ave, Manhattan, KS 66502 USA
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8
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Lenoci D, Resteghini C, Serafini MS, Pistore F, Canevari S, Ma B, Cavalieri S, Alfieri S, Trama A, Licitra L, De Cecco L. Tumor molecular landscape of Epstein-Barr virus (EBV) related nasopharyngeal carcinoma in EBV-endemic and non-endemic areas: Implications for improving treatment modalities. Transl Res 2024; 265:1-16. [PMID: 37949350 DOI: 10.1016/j.trsl.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 10/14/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023]
Abstract
Epstein-Barr virus (EBV) related- nasopharyngeal carcinoma (NPC) is a squamous carcinoma of the nasopharyngeal mucosal lining. Endemic areas (EA) are east and Southeast Asia, were NPC was recorded with higher incidence and longer estimated survival than in non-endemic area (NEA) such as Europe, We analyzed the gene expression and microenvironment properties of NPC in both areas to identify molecular subtypes and assess biological and clinical correlates that might explain the differences in incidence and outcome between EA- and NEA-NPCs. Six EA-NPC transcriptomic datasets, including tumor and normal samples, were integrated in a meta-analysis to identify molecular subtypes using a ConsensusClusterPlus bioinformatic approach. Based on the biological/functional characterization of four identified clusters were identified: Cl1, Immune-active; Cl2, defense-response; Cl3, proliferation; Cl4, perineural-interaction/EBV-exhaustion. Kaplan-Meier survival analysis, applied to the single dataset with available disease-free survival indicated Cl3 as the cluster with the worst prognosis (P = 0.0476), confirmed when applying four previously published prognostic signatures. A Cl3 classifier signature was generated and its prognostic performance was confirmed (P = 0.0368) on a validation dataset. Prediction of treatment response suggested better responses to: radiotherapy and immune checkpoint inhibitors immune-active and defense-response clusters; chemotherapy proliferation cluster; cisplatin perineural-interaction/EBV-exhaustion cluster. RNA sequencing for gene expression profiling was performed on 50 NEA-NPC Italian samples. In the NEA cohort, Cl1, Cl2 and Cl3 were represented, while perineural-interaction/EBV-exhaustion was almost absent. The immune/biological characterization and treatment-response prediction analyses of NEA-NPC partially replicated the EA-NPC results. Well characterized EA- and NEA-NPC retrospective and prospective cohorts are needed to validate the obtained results and can help designing future clinical studies.
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Affiliation(s)
- Deborah Lenoci
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy
| | - Carlo Resteghini
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Mara S Serafini
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Silvana Canevari
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Brigette Ma
- Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Salvatore Alfieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Annalisa Trama
- Evaluative Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Loris De Cecco
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy.
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9
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Mecham A, Stephenson A, Quinteros BI, Brown GS, Piccolo SR. TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus. J Integr Bioinform 2024; 21:jib-2023-0021. [PMID: 38047898 DOI: 10.1515/jib-2023-0021] [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: 06/13/2023] [Accepted: 11/20/2023] [Indexed: 12/05/2023] Open
Abstract
TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 6 million biological samples across more than 4000 organisms, GEO provides diverse opportunities for secondary research. Although scientists may find assay data relevant to a given research question, most analyses require sample-level annotations. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps.
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Affiliation(s)
- Avery Mecham
- Department of Biology, Brigham Young University, Provo, UT, 84602, USA
| | - Ashlie Stephenson
- Department of Biology, Brigham Young University, Provo, UT, 84602, USA
| | - Badi I Quinteros
- Department of Biology, Brigham Young University, Provo, UT, 84602, USA
| | - Grace S Brown
- Department of Biology, Brigham Young University, Provo, UT, 84602, USA
| | - Stephen R Piccolo
- Department of Biology, Brigham Young University, Provo, UT, 84602, USA
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10
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Korir PK, Iudin A, Somasundharam S, Weyand S, Salih O, Hartley M, Sarkans U, Patwardhan A, Kleywegt GJ. Ten recommendations for organising bioimaging data for archival. F1000Res 2024; 12:ELIXIR-1391. [PMID: 38486614 PMCID: PMC10938051 DOI: 10.12688/f1000research.129720.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data). The recommendations that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues, such as redundant directories or invalid characters in file or folder names, that should be addressed before archival. We offer these recommendations as a starting point and hope to engender more substantial conversations across and between the various data-rich communities.
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Affiliation(s)
- Paul K. Korir
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Andrii Iudin
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Simone Weyand
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Osman Salih
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Matthew Hartley
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ugis Sarkans
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ardan Patwardhan
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
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11
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Voogdt CGP, Tripathi S, Bassler SO, McKeithen-Mead SA, Guiberson ER, Koumoutsi A, Bravo AM, Buie C, Zimmermann M, Sonnenburg JL, Typas A, Deutschbauer AM, Shiver AL, Huang KC. Randomly barcoded transposon mutant libraries for gut commensals II: Applying libraries for functional genetics. Cell Rep 2024; 43:113519. [PMID: 38142398 DOI: 10.1016/j.celrep.2023.113519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/22/2023] [Accepted: 11/14/2023] [Indexed: 12/26/2023] Open
Abstract
The critical role of the intestinal microbiota in human health and disease is well recognized. Nevertheless, there are still large gaps in our understanding of the functions and mechanisms encoded in the genomes of most members of the gut microbiota. Genome-scale libraries of transposon mutants are a powerful tool to help us address this gap. Recent advances in barcoded transposon mutagenesis have dramatically lowered the cost of mutant fitness determination in hundreds of in vitro and in vivo experimental conditions. In an accompanying review, we discuss recent advances and caveats for the construction of pooled and arrayed barcoded transposon mutant libraries in human gut commensals. In this review, we discuss how these libraries can be used across a wide range of applications, the technical aspects involved, and expectations for such screens.
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Affiliation(s)
- Carlos Geert Pieter Voogdt
- Genome Biology Unit, EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany; Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Surya Tripathi
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Stefan Oliver Bassler
- Genome Biology Unit, EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Grabengasse 1, 69117 Heidelberg, Germany
| | - Saria A McKeithen-Mead
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Emma R Guiberson
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexandra Koumoutsi
- Genome Biology Unit, EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Afonso Martins Bravo
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Cullen Buie
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Justin L Sonnenburg
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Athanasios Typas
- Genome Biology Unit, EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany; Structural and Computational Biology Unit, EMBL, Heidelberg, Germany.
| | - Adam M Deutschbauer
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| | - Anthony L Shiver
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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12
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Clough E, Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillippy K, Sherman P, Lee H, Zhang N, Serova N, Wagner L, Zalunin V, Kochergin A, Soboleva A. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res 2024; 52:D138-D144. [PMID: 37933855 PMCID: PMC10767856 DOI: 10.1093/nar/gkad965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data files and descriptive metadata for over 200 000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.
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Affiliation(s)
- Emily Clough
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen E Wilhite
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre Ledoux
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Carlos Evangelista
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Irene F Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Maxim Tomashevsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kimberly A Marshall
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine H Phillippy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patti M Sherman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hyeseung Lee
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Naigong Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nadezhda Serova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lukas Wagner
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vadim Zalunin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrey Kochergin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alexandra Soboleva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
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13
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Rayamajhi S, Sipes J, Tetlow AL, Saha S, Bansal A, Godwin AK. Extracellular Vesicles as Liquid Biopsy Biomarkers across the Cancer Journey: From Early Detection to Recurrence. Clin Chem 2024; 70:206-219. [PMID: 38175602 DOI: 10.1093/clinchem/hvad176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 09/26/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Cancer is a dynamic process and thus requires highly informative and reliable biomarkers to help guide patient care. Liquid-based biopsies have emerged as a clinical tool for tracking cancer dynamics. Extracellular vesicles (EVs), lipid bilayer delimited particles secreted by cells, are a new class of liquid-based biomarkers. EVs are rich in selectively sorted biomolecule cargos, which provide a spatiotemporal fingerprint of the cell of origin, including cancer cells. CONTENT This review summarizes the performance characteristics of EV-based biomarkers at different stages of cancer progression, from early malignancy to recurrence, while emphasizing their potential as diagnostic, prognostic, and screening biomarkers. We discuss the characteristics of effective biomarkers, consider challenges associated with the EV biomarker field, and report guidelines based on the biomarker discovery pipeline. SUMMARY Basic science and clinical trial studies have shown the potential of EVs as precision-based biomarkers for tracking cancer status, with promising applications for diagnosing disease, predicting response to therapy, and tracking disease burden. The multi-analyte cargos of EVs enhance the performance characteristics of biomarkers. Recent technological advances in ultrasensitive detection of EVs have shown promise with high specificity and sensitivity to differentiate early-cancer cases vs healthy individuals, potentially outperforming current gold-standard imaging-based cancer diagnosis. Ultimately, clinical translation will be dictated by how these new EV biomarker-based platforms perform in larger sample cohorts. Applying ultrasensitive, scalable, and reproducible EV detection platforms with better design considerations based upon the biomarker discovery pipeline should guide the field towards clinically useful liquid biopsy biomarkers.
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Affiliation(s)
- Sagar Rayamajhi
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | - Jared Sipes
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | - Ashley L Tetlow
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | - Souvik Saha
- Division of Gastroenterology and Hepatology, University of Kansas Health System, Kansas City, KS, United States
| | - Ajay Bansal
- Division of Gastroenterology and Hepatology, University of Kansas Health System, Kansas City, KS, United States
- The University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City, KS, United States
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, United States
- The University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City, KS, United States
- Division of Genomic Diagnostics, University of Kansas Health System, Kansas City, KS, United States
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, KS, United States
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14
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Wright A, Wilkinson MD, Mungall C, Cain S, Richards S, Sternberg P, Provin E, Jacobs JL, Geib S, Raciti D, Yook K, Stein L, Molik DC. DATA RESOURCES AND ANALYSES FAIR Header Reference genome: A TRUSTworthy standard. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569306. [PMID: 38076838 PMCID: PMC10705436 DOI: 10.1101/2023.11.29.569306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR-bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability, and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability, and Technology (TRUST). The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility, and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.
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Affiliation(s)
- Adam Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Mark D Wilkinson
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas,Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Pozuelo de Alarcón, Madrid, ES, Spain
| | - Chris Mungall
- Biosystems Data Science, Lawrence Berkeley National Laboratory, Building: 977, 1 Cyclotron Rd, Berkeley, CA 94720 USA
| | - Scott Cain
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Stephen Richards
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, MS: BCM226, Houston, TX 77030, USA
| | - Paul Sternberg
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ellen Provin
- Department of Horticultural Studies, Texas A&M University, HFSB 204, TAMU 2133, College Station, TX 77848, USA
| | - Jonathan L Jacobs
- American Type Culture Collection, 10801 University Blvd, Manassas, VA 20110, USA
| | - Scott Geib
- Tropical Pest Genetics and Molecular Biology Research Unit, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, United States Department of Agriculture, Agricultural Research Service, 64 Nowelo St, Hilo HI 96720 USA
| | - Daniela Raciti
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Karen Yook
- Division of Biology and Biological Engineering 140-18, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lincoln Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - David C Molik
- Arthropod-borne Animal Diseases Research Unit, Center for Grain and Animal Health Research United States Department of Agriculture, Agricultural Research Service, 1515 College Ave, Manhattan, KS 66502 USA
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15
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Mukhin AM, Kazantsev FV, Lashin SA. Laboratory information systems for research management in biology. Vavilovskii Zhurnal Genet Selektsii 2023; 27:898-905. [PMID: 38213703 PMCID: PMC10777299 DOI: 10.18699/vjgb-23-104] [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: 07/13/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 01/13/2024] Open
Abstract
Modern investigations in biology often require the efforts of one or more groups of researchers. Often these are groups of specialists from various scientific fields who generate and share data of different formats and sizes. Without modern approaches to work automation and data versioning (where data from different collaborators are stored at different points in time), teamwork quickly devolves into unmanageable confusion. In this review, we present a number of information systems designed to solve these problems. Their application to the organization of scientific activity helps to manage the flow of actions and data, allowing all participants to work with relevant information and solving the issue of reproducibility of both experimental and computational results. The article describes methods for organizing data flows within a team, principles for organizing metadata and ontologies. The information systems Trello, Git, Redmine, SEEK, OpenBIS and Galaxy are considered. Their functionality and scope of use are described. Before using any tools, it is important to understand the purpose of implementation, to define the set of tasks they should solve, and, based on this, to formulate requirements and finally to monitor the application of recommendations in the field. The tasks of creating a framework of ontologies, metadata, data warehousing schemas and software systems are key for a team that has decided to undertake work to automate data circulation. It is not always possible to implement such systems in their entirety, but one should still strive to do so through a step-by-step introduction of principles for organizing data and tasks with the mastery of individual software tools. It is worth noting that Trello, Git, and Redmine are easier to use, customize, and support for small research groups. At the same time, SEEK, OpenBIS, and Galaxy are more specific and their use is advisable if the capabilities of simple systems are no longer sufficient.
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Affiliation(s)
- A M Mukhin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - F V Kazantsev
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - S A Lashin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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16
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Ghosh M, Broothaerts K, Ronsmans S, Roig IB, Scheepers J, Dikmen M, Ciscato ER, Blanch C, Plusquin M, Nygaard UC, Sejbæk CS, Hougaard KS, Hoet PH. Data management and protection in occupational and environmental exposome research - A case study from the EU-funded EXIMIOUS project. ENVIRONMENTAL RESEARCH 2023; 237:116886. [PMID: 37597835 DOI: 10.1016/j.envres.2023.116886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/25/2023] [Accepted: 08/12/2023] [Indexed: 08/21/2023]
Abstract
Within collaborative projects, such as the EU-funded Horizon 2020 EXIMIOUS project (Mapping Exposure-Induced Immune Effects: Connecting the Exposome and the Immunome), collection and analysis of large volumes of data pose challenges in the domain of data management, with regards to both ethical and legal aspects. However, researchers often lack the right tools and/or accurate understanding of the ethical/legal framework to independently address such challenges. With the guidance and support within and between the partner institutes (the researchers and the ethical and legal teams) in the EXIMIOUS project, we have been able to understand and solve most challenges during the first two project years. This has fed into the development of a Data Management Plan and the establishment of data management platforms in accordance with the ethical and legal framework laid down by the EU and the different national regulations of the partners involved. Through this elaborate exercise, we have acquired tools which allow us to make our research data FAIR (Findable, Accessible, Interoperable, and Reusable), while at the same time ensuring data privacy and security (GDPR compliant). Herein we share our experience of creating and managing the data workflow through an open research communication, with the aim of helping other researchers build their data management framework in their own projects. Based on the measures adopted in EXIMIOUS to ensure FAIR data management, we also put together a checklist "DMP CHECK" containing a series of recommendations based on our experience.
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Affiliation(s)
- Manosij Ghosh
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
| | - Katrijn Broothaerts
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Steven Ronsmans
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | | | - Jef Scheepers
- Support for Research Data Management (RDM), KU Leuven, Leuven, Belgium
| | - Mustafa Dikmen
- Support for Research Data Management (RDM), KU Leuven, Leuven, Belgium
| | | | - Carolina Blanch
- Imec (Interuniversitair Microelectronica Centrum), Leuven, Belgium
| | - Michelle Plusquin
- Centre for Environmental Sciences, University of Hasselt, Hasselt, Belgium
| | - Unni C Nygaard
- Section for Immunology, Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
| | - Camilla Sandal Sejbæk
- Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital, Department of Occupational and Environmental Medicine, Copenhagen, Denmark
| | - Karin S Hougaard
- National Research Centre for the Working Environment, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Peter Hm Hoet
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
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17
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Lupancu TJ, Lee KM, Eivazitork M, Hor C, Fleetwood AJ, Cook AD, Olshansky M, Turner SJ, de Steiger R, Lim K, Hamilton JA, Achuthan AA. Epigenetic and transcriptional regulation of CCL17 production by glucocorticoids in arthritis. iScience 2023; 26:108079. [PMID: 37860753 PMCID: PMC10583050 DOI: 10.1016/j.isci.2023.108079] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 08/17/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023] Open
Abstract
Glucocorticoids (GCs) are potent anti-inflammatory agents and are broadly used in treating rheumatoid arthritis (RA) patients, albeit with adverse side effects associated with long-term usage. The negative consequences of GC therapy provide an impetus for research into gaining insights into the molecular mechanisms of GC action. We have previously reported that granulocyte-macrophage colony-stimulating factor (GM-CSF)-induced CCL17 has a non-redundant role in inflammatory arthritis. Here, we provide molecular evidence that GCs can suppress GM-CSF-mediated upregulation of IRF4 and CCL17 expression via downregulating JMJD3 expression and activity. In mouse models of inflammatory arthritis, GC treatment inhibited CCL17 expression and ameliorated arthritic pain-like behavior and disease. Significantly, GC treatment of RA patient peripheral blood mononuclear cells ex vivo resulted in decreased CCL17 production. This delineated pathway potentially provides new therapeutic options for the treatment of many inflammatory conditions, where GCs are used as an anti-inflammatory drug but without the associated adverse side effects.
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Affiliation(s)
- Tanya J. Lupancu
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Kevin M.C. Lee
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Mahtab Eivazitork
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Cecil Hor
- Department of Medicine, Western Health, The University of Melbourne, St Albans, VIC 3021, Australia
| | - Andrew J. Fleetwood
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Haematopoiesis and Leukocyte Biology, Baker IDI Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Andrew D. Cook
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Moshe Olshansky
- Department of Microbiology, Monash University, Clayton, VIC 3800, Australia
| | - Stephen J. Turner
- Department of Microbiology, Monash University, Clayton, VIC 3800, Australia
| | - Richard de Steiger
- Department of Surgery, Epworth HealthCare, The University of Melbourne, Richmond, VIC 3121, Australia
| | - Keith Lim
- Department of Medicine, Western Health, The University of Melbourne, St Albans, VIC 3021, Australia
| | - John A. Hamilton
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Adrian A. Achuthan
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
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18
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Holý P, Brynychová V, Šeborová K, Haničinec V, Koževnikovová R, Trnková M, Vrána D, Gatěk J, Kopečková K, Mrhalová M, Souček P. Integrative analysis of mRNA and miRNA expression profiles and somatic variants in oxysterol signaling in early-stage luminal breast cancer. Mol Oncol 2023; 17:2074-2089. [PMID: 37491786 PMCID: PMC10552891 DOI: 10.1002/1878-0261.13495] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/19/2023] [Accepted: 07/24/2023] [Indexed: 07/27/2023] Open
Abstract
Oxysterols, oxidized derivatives of cholesterol, act in breast cancer (BC) as selective estrogen receptor modulators and affect cholesterol homeostasis, drug transport, nuclear and cell receptors, and other signaling proteins. Using data from three highly overlapping sets of patients (N = 162 in total) with early-stage estrogen-receptor-positive luminal BC-high-coverage targeted DNA sequencing (113 genes), mRNA sequencing, and full micro-RNA (miRNA) transcriptome microarrays-we describe complex oxysterol-related interaction (correlation) networks, with validation in public datasets (n = 538) and 11 databases. The ESR1-CH25H-INSIG1-ABCA9 axis was the most prominent, interconnected through miR-125b-5p, miR-99a-5p, miR-100-5p, miR-143-3p, miR-199b-5p, miR-376a-3p, and miR-376c-3p. Mutations in SC5D, CYP46A1, and its functionally linked gene set were associated with multiple differentially expressed oxysterol-related genes. STARD5 was upregulated in patients with positive lymph node status. High expression of hsa-miR-19b-3p was weakly associated with poor survival. This is the first study of oxysterol-related genes in BC that combines DNA, mRNA, and miRNA multiomics with detailed clinical data. Future studies should provide links between intratumoral oxysterol signaling depicted here, circulating oxysterol levels, and therapy outcomes, enabling eventual clinical exploitation of present findings.
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Affiliation(s)
- Petr Holý
- Third Faculty of MedicineCharles UniversityPragueCzech Republic
- Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
- Toxicogenomics UnitNational Institute of Public HealthPragueCzech Republic
| | - Veronika Brynychová
- Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
- Toxicogenomics UnitNational Institute of Public HealthPragueCzech Republic
| | - Karolína Šeborová
- Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
- Toxicogenomics UnitNational Institute of Public HealthPragueCzech Republic
| | - Vojtěch Haničinec
- Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
| | | | | | - David Vrána
- Comprehensive Cancer Center Novy JicinHospital Novy JicinCzech Republic
| | - Jiří Gatěk
- Department of SurgeryEUC Hospital Zlin and Tomas Bata University in ZlinCzech Republic
| | - Kateřina Kopečková
- Department of Oncology, Second Faculty of MedicineCharles University and Motol University HospitalPragueCzech Republic
| | - Marcela Mrhalová
- Department of Pathology, Second Faculty of MedicineCharles University and Motol University HospitalPragueCzech Republic
| | - Pavel Souček
- Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
- Toxicogenomics UnitNational Institute of Public HealthPragueCzech Republic
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19
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Nizamoglu M, Joglekar MM, Almeida CR, Larsson Callerfelt AK, Dupin I, Guenat OT, Henrot P, van Os L, Otero J, Elowsson L, Farre R, Burgess JK. Innovative three-dimensional models for understanding mechanisms underlying lung diseases: powerful tools for translational research. Eur Respir Rev 2023; 32:230042. [PMID: 37495250 PMCID: PMC10369168 DOI: 10.1183/16000617.0042-2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/04/2023] [Indexed: 07/28/2023] Open
Abstract
Chronic lung diseases result from alteration and/or destruction of lung tissue, inevitably causing decreased breathing capacity and quality of life for patients. While animal models have paved the way for our understanding of pathobiology and the development of therapeutic strategies for disease management, their translational capacity is limited. There is, therefore, a well-recognised need for innovative in vitro models to reflect chronic lung diseases, which will facilitate mechanism investigation and the advancement of new treatment strategies. In the last decades, lungs have been modelled in healthy and diseased conditions using precision-cut lung slices, organoids, extracellular matrix-derived hydrogels and lung-on-chip systems. These three-dimensional models together provide a wide spectrum of applicability and mimicry of the lung microenvironment. While each system has its own limitations, their advantages over traditional two-dimensional culture systems, or even over animal models, increases the value of in vitro models. Generating new and advanced models with increased translational capacity will not only benefit our understanding of the pathobiology of lung diseases but should also shorten the timelines required for discovery and generation of new therapeutics. This article summarises and provides an outline of the European Respiratory Society research seminar "Innovative 3D models for understanding mechanisms underlying lung diseases: powerful tools for translational research", held in Lisbon, Portugal, in April 2022. Current in vitro models developed for recapitulating healthy and diseased lungs are outlined and discussed with respect to the challenges associated with them, efforts to develop best practices for model generation, characterisation and utilisation of models and state-of-the-art translational potential.
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Affiliation(s)
- Mehmet Nizamoglu
- University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
- Both authors contributed equally
| | - Mugdha M Joglekar
- University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
- Both authors contributed equally
| | - Catarina R Almeida
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | | | - Isabelle Dupin
- Centre de Recherche Cardio-thoracique de Bordeaux, Université de Bordeaux, Pessac, France
- INSERM, Centre de Recherche Cardio-thoracique de Bordeaux, Pessac, France
| | - Olivier T Guenat
- Organs-on-Chip Technologies, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Pulmonary Medicine, University Hospital of Bern, Bern, Switzerland
- Department of General Thoracic Surgery, University Hospital of Bern, Bern, Switzerland
| | - Pauline Henrot
- Centre de Recherche Cardio-thoracique de Bordeaux, Université de Bordeaux, Pessac, France
- INSERM, Centre de Recherche Cardio-thoracique de Bordeaux, Pessac, France
- Service d'exploration fonctionnelle respiratoire, CHU de Bordeaux, Pessac, France
| | - Lisette van Os
- Organs-on-Chip Technologies, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Jorge Otero
- Unit of Biophysics and Bioengineering, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades Respiratorias, Madrid, Spain
| | - Linda Elowsson
- Lung Biology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Ramon Farre
- Unit of Biophysics and Bioengineering, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades Respiratorias, Madrid, Spain
- Institut Investigacions Biomediques August Pi Sunyer, Barcelona, Spain
| | - Janette K Burgess
- University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science-FB41, Groningen, The Netherlands
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20
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Ziemann M, Poulain P, Bora A. The five pillars of computational reproducibility: bioinformatics and beyond. Brief Bioinform 2023; 24:bbad375. [PMID: 37870287 PMCID: PMC10591307 DOI: 10.1093/bib/bbad375] [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: 06/16/2023] [Revised: 09/26/2023] [Accepted: 09/30/2023] [Indexed: 10/24/2023] Open
Abstract
Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.
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Affiliation(s)
- Mark Ziemann
- Deakin University, School of Life and Environmental Sciences, Geelong, Australia
- Burnet Institute, Melbourne, Australia
| | - Pierre Poulain
- Université Paris Cité, CNRS, Institut Jacques Monod, Paris, France
| | - Anusuiya Bora
- Deakin University, School of Life and Environmental Sciences, Geelong, Australia
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21
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Sakalli-Tecim E, Gur-Dedeoglu B, Guray NT. Systems biology based miRNA-mRNA expression pattern analysis of Emodin in breast cancer cell lines. Pathol Res Pract 2023; 249:154780. [PMID: 37633004 DOI: 10.1016/j.prp.2023.154780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/20/2023] [Accepted: 08/21/2023] [Indexed: 08/28/2023]
Abstract
Breast cancer has been among the most prominent cancers with high mortality. Currently most of the offered therapeutics are toxic; hence, less toxic therapeutic intervention is required. Here, we studied the molecular mechanisms of the effect of a phytoestrogen Emodin on estrogen receptor positive MCF-7 and negative MDA-MB-231 cells by carrying out a comprehensive network assessment. Differentially expressed microRNAs along with their previously identified differentially expressed mRNAs were analyzed through microarrays by using integrative systems biology approach. For each cell line miRNA-target gene networks were built, gene ontology and pathway enrichment analyses were performed, enrichment maps were constructed and the potential key genes, miRNAs and miRNA-gene interactions were studied.
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Affiliation(s)
- Elif Sakalli-Tecim
- Department of Biotechnology, Middle East Technical University, Ankara, Turkiye
| | | | - N Tulin Guray
- Department of Biotechnology, Middle East Technical University, Ankara, Turkiye; Department of Biological Sciences, Middle East Technical University, Ankara, Turkiye.
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22
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Khaipho-Burch M, Cooper M, Crossa J, de Leon N, Holland J, Lewis R, McCouch S, Murray SC, Rabbi I, Ronald P, Ross-Ibarra J, Weigel D, Buckler ES. Genetic modification can improve crop yields - but stop overselling it. Nature 2023; 621:470-473. [PMID: 37773222 DOI: 10.1038/d41586-023-02895-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2023]
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23
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Hartley M, Iudin A, Padwardhan A, Sarkans U, Yoldaş AK, Kleywegt GJ. Providing open imaging data at scale: An EMBL-EBI perspective. Histochem Cell Biol 2023; 160:211-221. [PMID: 37537341 PMCID: PMC10492673 DOI: 10.1007/s00418-023-02216-2] [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] [Accepted: 05/30/2023] [Indexed: 08/05/2023]
Abstract
Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.
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Affiliation(s)
- Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ardan Padwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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24
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Abdelkader Y, Perez-Davalos L, LeDuc R, Zahedi RP, Labouta HI. Omics approaches for the assessment of biological responses to nanoparticles. Adv Drug Deliv Rev 2023; 200:114992. [PMID: 37414362 DOI: 10.1016/j.addr.2023.114992] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/08/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.
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Affiliation(s)
- Yasmin Abdelkader
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada; Department of Cell Biology, Biotechnology Research Institute, National Research Centre, 33 El Buhouth St., Cairo 12622, Egypt
| | - Luis Perez-Davalos
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada
| | - Richard LeDuc
- Children's Hospital Research Institute of Manitoba, 513 - 715 McDermot Av. W, Winnipeg, Manitoba R3E 3P4, Canada; Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Av., Winnipeg, Manitoba R3E 0J9, Canada
| | - Rene P Zahedi
- Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Av., Winnipeg, Manitoba R3E 0J9, Canada; Department of Internal Medicine, 715 McDermot Av., Winnipeg, Manitoba R3E 3P4, Canada; Manitoba Centre for Proteomics and Systems Biology, 799 JBRC, 715 McDermot Av., Winnipeg, Manitoba R3E 3P4, Canada; CancerCare Manitoba Research Institute, 675 McDermot Av., Manitoba R3E 0V9, Canada
| | - Hagar I Labouta
- Unity Health Toronto - St. Michael's Hospital, University of Toronto, 209 Victoria St., Toronto, Ontario M5B 1T8, Canada; College of Pharmacy, Apotex Centre, University of Manitoba, 750 McDermot Av. W, Winnipeg, Manitoba R3E 0T5, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St., Toronto, Ontario M5S 3M2, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, M5S 3G9, Canada; Faculty of Pharmacy, Alexandria University, 1 Khartoum Square, Azarita, Alexandria, Egypt, 21521.
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25
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Zieliński T, Hodge JJL, Millar AJ. Keep It Simple: Using README Files to Advance Standardization in Chronobiology. Clocks Sleep 2023; 5:499-506. [PMID: 37754351 PMCID: PMC10529918 DOI: 10.3390/clockssleep5030033] [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: 07/19/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/28/2023] Open
Abstract
Standardization plays a crucial role in ensuring the reliability, reproducibility, and interoperability of research data in the biomedical sciences. Metadata standards are one foundation for the FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. They facilitate data discovery, understanding, and reuse. However, the adoption of metadata standards in biological research lags in practice. Barriers such as complexity, lack of incentives, technical challenges, resource constraints, and resistance to change hinder widespread adoption. In the field of chronobiology, standardization is essential but faces particular challenges due to the longitudinal nature of experimental data, diverse model organisms, and varied measurement techniques. To address these challenges, we propose an approach that emphasizes simplicity and practicality: the development of README templates tailored for particular data types and species. Through this opinion article, our intention is to initiate a dialogue and commence a community-driven standardization process by engaging potential contributors and collaborators.
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Affiliation(s)
- Tomasz Zieliński
- Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JD, UK;
| | - James J. L. Hodge
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol BS8 1TD, UK;
| | - Andrew J. Millar
- Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JD, UK;
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26
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Jackson KC, Pachter L. A standard for sharing spatial transcriptomics data. CELL GENOMICS 2023; 3:100374. [PMID: 37601972 PMCID: PMC10435375 DOI: 10.1016/j.xgen.2023.100374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.
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Affiliation(s)
- Kayla C. Jackson
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
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27
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Hosseini R, Vlasveld M, Willemse J, van de Water B, Le Dévédec SE, Wolstencroft KJ. FAIR High Content Screening in Bioimaging. Sci Data 2023; 10:462. [PMID: 37460560 PMCID: PMC10352356 DOI: 10.1038/s41597-023-02367-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Affiliation(s)
- Rohola Hosseini
- Life Science Semantics, Leiden Institute of Advanced Computer Science, Leiden, The Netherlands
| | - Matthijs Vlasveld
- Drug Discovery and Safety, Cell Observatory, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Joost Willemse
- Cell Observatory, Institute of Biology Leiden, Leiden, The Netherlands
| | - Bob van de Water
- Drug Discovery and Safety, Cell Observatory, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Sylvia E Le Dévédec
- Drug Discovery and Safety, Cell Observatory, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
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28
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Bahraini M, Fazeli A, Dorgalaleh A. Laboratory Diagnosis of Activated Protein C Resistance and Factor V Leiden. Semin Thromb Hemost 2023. [PMID: 37429328 DOI: 10.1055/s-0043-1770773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
The factor V Leiden (FVL) polymorphism is known as the most common inherited risk factor for venous thrombosis. In turn, FVL is the leading cause of an activated protein C resistance (APCR) phenotype, in which the addition of exogenous activated protein C to plasma does not result in the expected anticoagulant effect. In the routine laboratory approach to the formal diagnosis of FVL, an initial positive screening plasma-based method for APCR is often performed, and only if needed, this is followed by a confirmatory DNA-based assay for FVL. Multiple methods with accepted sensitivity and specificity for determining an APCR/FVL phenotype are commonly categorized into two separate groups: (1) screening plasma-based assays, including qualitative functional clot-based assays, for APCR, and (2) confirmatory DNA-based molecular assays, entailing several tests and platforms, including polymerase chain reaction-based and non-PCR-based techniques, for FVL. This review will describe the methodological aspects of each laboratory test and prepare suggestions on the indication of APCR and FVL testing and method selection.
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Affiliation(s)
- Mehran Bahraini
- Department of Hematology and Blood Transfusion, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Alieh Fazeli
- Diagnostic Laboratory Sciences and Technology Research Center, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Akbar Dorgalaleh
- Department of Hematology and Blood Transfusion, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran
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29
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Thompson PT, Ojha S, Powell CD, Pennell KG, Moseley HNB. A proposed FAIR approach for disseminating geospatial information system maps. Sci Data 2023; 10:389. [PMID: 37328607 PMCID: PMC10275873 DOI: 10.1038/s41597-023-02281-1] [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: 01/04/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023] Open
Abstract
We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.
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Affiliation(s)
- P Travis Thompson
- University of Kentucky Superfund Research Center (UKSRC), Lexington, KY, USA
| | - Sweta Ojha
- University of Kentucky Superfund Research Center (UKSRC), Lexington, KY, USA
- University of Kentucky, College of Engineering, Department of Civil Engineering, Lexington, KY, USA
| | - Christian D Powell
- University of Kentucky Superfund Research Center (UKSRC), Lexington, KY, USA
- University of Kentucky, Department of Computer Science (Data Science Program), Lexington, KY, USA
| | - Kelly G Pennell
- University of Kentucky Superfund Research Center (UKSRC), Lexington, KY, USA
- University of Kentucky, College of Engineering, Department of Civil Engineering, Lexington, KY, USA
| | - Hunter N B Moseley
- University of Kentucky Superfund Research Center (UKSRC), Lexington, KY, USA.
- University of Kentucky, Department of Molecular and Cellular Biochemistry, Lexington, KY, USA.
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30
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Kundalia PH, Pažitná L, Kianičková K, Jáné E, Lorencová L, Katrlík J. A Holistic 4D Approach to Optimize Intrinsic and Extrinsic Factors Contributing to Variability in Microarray Biosensing in Glycomics. SENSORS (BASEL, SWITZERLAND) 2023; 23:5362. [PMID: 37420529 DOI: 10.3390/s23125362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/27/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
Protein-carbohydrate interactions happen to be a crucial facet of biology, discharging a myriad of functions. Microarrays have become a premier choice to discern the selectivity, sensitivity and breadth of these interactions in a high-throughput manner. The precise recognition of target glycan ligands among the plethora of others is central for any glycan-targeting probe being tested by microarray analyses. Ever since the introduction of the microarray as an elemental tool for high-throughput glycoprofiling, numerous distinct array platforms possessing different customizations and assemblies have been developed. Accompanying these customizations are various factors ushering variances across array platforms. In this primer, we investigate the influence of various extrinsic factors, namely printing parameters, incubation procedures, analyses and array storage conditions on the protein-carbohydrate interactions and evaluate these factors for the optimal performance of microarray glycomics analysis. We hereby propose a 4D approach (Design-Dispense-Detect-Deduce) to minimize the effect of these extrinsic factors on glycomics microarray analyses and thereby streamline cross-platform analyses and comparisons. This work will aid in optimizing microarray analyses for glycomics, minimize cross-platform disparities and bolster the further development of this technology.
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Affiliation(s)
- Paras H Kundalia
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
| | - Lucia Pažitná
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
| | - Kristína Kianičková
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
| | - Eduard Jáné
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
| | - Lenka Lorencová
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
| | - Jaroslav Katrlík
- Department of Glycobiotechnology, Institute of Chemistry, Slovak Academy of Sciences, SK-84538 Bratislava, Slovakia
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31
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Nault R, Cave MC, Ludewig G, Moseley HN, Pennell KG, Zacharewski T. A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:65001. [PMID: 37352010 PMCID: PMC10289218 DOI: 10.1289/ehp11484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise. OBJECTIVE In this commentary, we examine the current state of managing data and metadata-referred to collectively as (meta)data-in the experimental environmental health sciences. We introduce new tools and resources based on in vivo experiments to serve as examples for the broader field. METHODS We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of in vivo data deposited in the Gene Expression Omnibus that demonstrates the current state of in vivo environmental health data and highlights the value of using the tools we propose to support data deposition. DISCUSSION The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484.
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Affiliation(s)
- Rance Nault
- Biochemistry & Molecular Biology Department, Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan, USA
| | - Matthew C. Cave
- Division of Gastroenterology, Hepatology, and Nutrition, University of Louisville, Louisville, Kentucky, USA
| | - Gabriele Ludewig
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, Iowa, USA
| | - Hunter N.B. Moseley
- Molecular and Cellular Biochemistry Department, University of Kentucky, Lexington, Kentucky, USA
| | - Kelly G. Pennell
- Department of Civil Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Tim Zacharewski
- Biochemistry & Molecular Biology Department, Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan, USA
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32
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Sahar A, Nicorescu I, Barran G, Paterson M, Hilkens CM, Lord P. Tolerogenic dendritic cell reporting: Has a minimum information model made a difference? PeerJ 2023; 11:e15352. [PMID: 37273539 PMCID: PMC10239229 DOI: 10.7717/peerj.15352] [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: 12/23/2022] [Accepted: 04/13/2023] [Indexed: 06/06/2023] Open
Abstract
Minimum information models are reporting frameworks that describe the essential information that needs to be provided in a publication, so that the work can be repeated or compared to other work. In 2016, Minimum Information about Tolerogenic Antigen-Presenting cells (MITAP) was created to standardize the reporting on tolerogenic antigen-presenting cells, including tolerogenic dendritic cells (tolDCs). tolDCs is a generic term for dendritic cells that have the ability to (re-)establish immune tolerance; they have been developed as a cell therapy for autoimmune diseases or for the prevention of transplant rejection. Because protocols to generate these therapeutic cells vary widely, MITAP was deemed to be a pivotal reporting tool by and for the tolDC community. In this paper, we explored the impact that MITAP has had on the tolDC field. We did this by examining a subset of the available literature on tolDCs. Our analysis shows that MITAP is used in only the minority of relevant papers (14%), but where it is used the amount of metadata available is slightly increased over where it is not. From this, we conclude that MITAP has been a partial success, but that much more needs to be done if standardized reporting is to become common within the discipline.
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Affiliation(s)
- Ayesha Sahar
- School of Computing Science, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ioana Nicorescu
- Translational & Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Gabrielle Barran
- Translational & Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Megan Paterson
- Translational & Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Catharien M.U. Hilkens
- Translational & Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Phillip Lord
- School of Computing Science, Newcastle University, Newcastle Upon Tyne, United Kingdom
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33
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Ohshima K, Nagashima T, Fujiya K, Hatakeyama K, Watanabe Y, Morimoto K, Kamada F, Shimoda Y, Ohnami S, Naruoka A, Serizawa M, Ohnami S, Kenmotsu H, Shiomi A, Tsubosa Y, Bando E, Sugiura T, Sugino T, Terashima M, Uesaka K, Urakami K, Akiyama Y, Yamaguchi K. Whole-genome and Epigenomic Landscapes of Malignant Gastrointestinal Stromal Tumors Harboring KIT Exon 11 557-558 Deletion Mutations. CANCER RESEARCH COMMUNICATIONS 2023; 3:684-696. [PMID: 37377752 PMCID: PMC10124575 DOI: 10.1158/2767-9764.crc-22-0364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/12/2022] [Accepted: 03/17/2023] [Indexed: 06/29/2023]
Abstract
Gastrointestinal stromal tumors (GIST) with KIT exon 11 deletions involving in codons 557-558 (KIT Δ557-558) exhibit higher proliferation rates and shorter disease-free survival times compared with GISTs with other KIT exon 11 mutations. We analyzed 30 GIST cases and observed genomic instability and global DNA hypomethylation only in high-risk malignant GISTs with KIT Δ557-558. Whole-genome sequencing revealed that the high-risk malignant GISTs with KIT Δ557-558 (12 cases) had more structural variations (SV), single-nucleotide variants, and insertions and deletions compared with the low-risk, less malignant GISTs with KIT Δ557-558 (six cases) and the high-risk (six cases) or low-risk (6 cases) GISTs with other KIT exon 11 mutations. The malignant GISTs with KIT Δ557-558 showed higher frequency and significance in copy number (CN) reduction on chromosome arms 9p and 22q, and 50% of them had LOH or CN-dependent expression reduction in CDKN2A. In addition, SVs with driver potential were detected in 75% of them, in which AKT3 and MGMT were recurrently identified. Genome-wide DNA methylation and gene expression analyses showed global intergenic DNA hypomethylation, SNAI2 upregulation, and higher expression signatures, including p53 inactivation and chromosomal instability, as characteristics of malignant GISTs with KIT Δ557-558 that distinguished them from other GISTs. These genomic and epigenomic profiling results revealed that KIT Δ557-558 mutations are associated with increased genomic instability in malignant GISTs. Significance We present genomic and epigenomic insights into the malignant progression of GISTs with KIT exon 11 deletions involving in 557-558, demonstrating their unique chromosomal instability and global intergenic DNA hypomethylation.
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Affiliation(s)
- Keiichi Ohshima
- Medical Genetics Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Takeshi Nagashima
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
- SRL, Inc., Tokyo, Japan
| | - Keiichi Fujiya
- Division of Gastric Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Keiichi Hatakeyama
- Cancer Multiomics Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Yuko Watanabe
- Medical Genetics Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Kimiko Morimoto
- Medical Genetics Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Fukumi Kamada
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Yuji Shimoda
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Sumiko Ohnami
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Akane Naruoka
- Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Masakuni Serizawa
- Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Shumpei Ohnami
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Hirotsugu Kenmotsu
- Division of Genetic Medicine Promotion, Shizuoka Cancer Center Hospital, Shizuoka, Japan
- Division of Thoracic Oncology, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Akio Shiomi
- Division of Colon and Rectal Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Yasuhiro Tsubosa
- Division of Esophageal Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Etsuro Bando
- Division of Gastric Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Teiichi Sugiura
- Division of Hepato-Biliary-Pancreatic Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Masanori Terashima
- Division of Gastric Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Katsuhiko Uesaka
- Division of Hepato-Biliary-Pancreatic Surgery, Shizuoka Cancer Center Hospital, Shizuoka, Japan
| | - Kenichi Urakami
- Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Yasuto Akiyama
- Immunotherapy Division, Shizuoka Cancer Center Research Institute, Shizuoka, Japan
| | - Ken Yamaguchi
- Shizuoka Cancer Center Hospital and Research Institute, Shizuoka, Japan
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34
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Lee B, Hwang S, Kim PG, Ko G, Jang K, Kim S, Kim JH, Jeon J, Kim H, Jung J, Yoon BH, Byeon I, Jang I, Song W, Choi J, Kim SY. Introduction of the Korea BioData Station (K-BDS) for sharing biological data. Genomics Inform 2023; 21:e12. [PMID: 37037470 PMCID: PMC10085736 DOI: 10.5808/gi.22073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/06/2023] [Indexed: 04/03/2023] Open
Abstract
A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.
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35
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Shi H, Li M, Meng H, Zheng X, Zhang K, Fent K, Dai J, Zhao Y. Reduced Transcriptome Analysis of Zebrafish Embryos Prioritizes Environmental Compounds with Adverse Cardiovascular Activities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4959-4970. [PMID: 36935584 DOI: 10.1021/acs.est.2c08920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Cardiovascular diseases are the leading cause of premature death in humans and remain a global public health challenge. While age, sex, family history, and false nutrition make a contribution, our understanding of compounds acting as cardiovascular disruptors is far from complete. Here, we aim to identify cardiovascular disruptors via a reduced transcriptome atlas (RTA) approach, which integrates large-scale transcriptome data sets of zebrafish and compiles a specific gene panel related to cardiovascular diseases. Among 767 gene expression profiles covering 81 environmental compounds, 11 priority compounds are identified with the greatest effects on the cardiovascular system at the transcriptional level. Among them, metals (AgNO3, Ag nanoparticles, arsenic) and pesticides/biocides (linuron, methylparaben, triclosan, and trimethylchlorotin) are identified with the most significant effects. Distinct transcriptional signatures are further identified by the percentage values, indicating that different physiological endpoints exist among prioritized compounds. In addition, cardiovascular dysregulations are experimentally confirmed for the prioritized compounds via alterations of cardiovascular physiology and lipid profiles of zebrafish. The accuracy rate of experimental verification reaches up to 62.9%. The web-based RTA analysis tool, Cardionet, for rapid cardiovascular disruptor discovery was further provided at http://www.envh.sjtu.edu.cn/cardionet.jsp. Our integrative approach yields an efficient platform to discover novel cardiovascular-disrupting chemicals in the environment.
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Affiliation(s)
- Haochun Shi
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Meng Li
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Haoyu Meng
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Xuehan Zheng
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Kun Zhang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Karl Fent
- Institute of Biogeochemistry and Pollution Dynamics, Department of Environmental Systems Science, ETH Zürich, CH-8092 Zürich, Switzerland
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yanbin Zhao
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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36
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Hasel P, Aisenberg WH, Bennett FC, Liddelow SA. Molecular and metabolic heterogeneity of astrocytes and microglia. Cell Metab 2023; 35:555-570. [PMID: 36958329 DOI: 10.1016/j.cmet.2023.03.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/26/2023] [Accepted: 03/08/2023] [Indexed: 03/25/2023]
Abstract
Astrocytes and microglia are central players in a myriad of processes in the healthy and diseased brain, ranging from metabolism to immunity. The crosstalk between these two cell types contributes to pathology in many if not all neuroinflammatory and neurodegenerative diseases. Recent advancements in integrative multimodal sequencing techniques have begun to highlight how heterogeneous both cell types are and the importance of metabolism to their regulation. We discuss here the transcriptomic, metabolic, and functional heterogeneity of astrocytes and microglia and highlight their interaction in health and disease.
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Affiliation(s)
- Philip Hasel
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY 10016, USA.
| | - William H Aisenberg
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
| | - F Chris Bennett
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
| | - Shane A Liddelow
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY 10016, USA; Parekh Center for Interdisciplinary Neurology, NYU Grossman School of Medicine, New York, NY 10016, USA.
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37
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Parmagnani AS, Kanchiswamy CN, Paponov IA, Bossi S, Malnoy M, Maffei ME. Bacterial Volatiles (mVOC) Emitted by the Phytopathogen Erwinia amylovora Promote Arabidopsis thaliana Growth and Oxidative Stress. Antioxidants (Basel) 2023; 12:antiox12030600. [PMID: 36978848 PMCID: PMC10045578 DOI: 10.3390/antiox12030600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/21/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
Phytopathogens are well known for their devastating activity that causes worldwide significant crop losses. However, their exploitation for crop welfare is relatively unknown. Here, we show that the microbial volatile organic compound (mVOC) profile of the bacterial phytopathogen, Erwinia amylovora, enhances Arabidopsis thaliana shoot and root growth. GC-MS head-space analyses revealed the presence of typical microbial volatiles, including 1-nonanol and 1-dodecanol. E. amylovora mVOCs triggered early signaling events including plasma transmembrane potential Vm depolarization, cytosolic Ca2+ fluctuation, K+-gated channel activity, and reactive oxygen species (ROS) and nitric oxide (NO) burst from few minutes to 16 h upon exposure. These early events were followed by the modulation of the expression of genes involved in plant growth and defense responses and responsive to phytohormones, including abscisic acid, gibberellin, and auxin (including the efflux carriers PIN1 and PIN3). When tested, synthetic 1-nonanol and 1-dodecanol induced root growth and modulated genes coding for ROS. Our results show that E. amylovora mVOCs affect A. thaliana growth through a cascade of early and late signaling events that involve phytohormones and ROS.
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Affiliation(s)
- Ambra S. Parmagnani
- Department of Life Sciences and Systems Biology, University of Turin, Via Quarello 15/a, 10135 Turin, Italy
| | | | - Ivan A. Paponov
- Department of Food Science, Aarhus University, 8200 Aarhus, Denmark
| | - Simone Bossi
- Department of Life Sciences and Systems Biology, University of Turin, Via Quarello 15/a, 10135 Turin, Italy
| | - Mickael Malnoy
- Research and Innovation Centre, Edmund Mach Foundation, Via Edmund Mach 1, 38098 San Michele all’Adige, Italy
| | - Massimo E. Maffei
- Department of Life Sciences and Systems Biology, University of Turin, Via Quarello 15/a, 10135 Turin, Italy
- Correspondence: ; Tel.: +39-011-670-5967
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38
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Deutsch EW, Vizcaíno JA, Jones AR, Binz PA, Lam H, Klein J, Bittremieux W, Perez-Riverol Y, Tabb DL, Walzer M, Ricard-Blum S, Hermjakob H, Neumann S, Mak TD, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Bandeira N, Carver J, Pullman B, Sun Z, Hoffmann N, Shofstahl J, Zhu Y, Licata L, Quaglia F, Tosatto SCE, Orchard SE. Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work. J Proteome Res 2023; 22:287-301. [PMID: 36626722 PMCID: PMC9903322 DOI: 10.1021/acs.jproteome.2c00637] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Indexed: 01/11/2023]
Abstract
The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.
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Affiliation(s)
- Eric W. Deutsch
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Juan Antonio Vizcaíno
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R. Jones
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Pierre-Alain Binz
- Clinical
Chemistry Service, Lausanne University Hospital, 1011 976 Lausanne, Switzerland
| | - Henry Lam
- Department
of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, P. R. China.
| | - Joshua Klein
- Program for
Bioinformatics, Boston University, Boston, Massachusetts 02215, United States
| | - Wout Bittremieux
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Department
of Computer Science, University of Antwerp, 2020 Antwerpen, Belgium
| | - Yasset Perez-Riverol
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - David L. Tabb
- SA MRC
Centre for TB Research, DST/NRF Centre of Excellence for Biomedical
TB Research, Division of Molecular Biology and Human Genetics, Faculty
of Medicine and Health Sciences, Stellenbosch
University, Cape Town 7602, South Africa
| | - Mathias Walzer
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Sylvie Ricard-Blum
- Univ.
Lyon, Université Lyon 1, ICBMS, UMR 5246, 69622 Villeurbanne, France
| | - Henning Hermjakob
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Bioinformatics
and Scientific Data, Leibniz Institute of
Plant Biochemistry, 06120 Halle, Germany
- German
Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Jena-Leipzig, Germany
| | - Tytus D. Mak
- Mass Spectrometry
Data Center, National Institute of Standards
and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United
States
| | - Shin Kawano
- Database
Center for Life Science, Joint Support Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- Faculty
of Contemporary Society, Toyama University
of International Studies, Toyama 930-1292, Japan
- School
of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Luis Mendoza
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Tim Van Den Bossche
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Nuno Bandeira
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Jeremy Carver
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Benjamin Pullman
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Zhi Sun
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Nils Hoffmann
- Institute
for Bio- and Geosciences (IBG-5), Forschungszentrum
Jülich GmbH, 52428 Jülich, Germany
| | - Jim Shofstahl
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Yunping Zhu
- National
Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, #38, Life Science Park, Changping District, Beijing 102206, China
| | - Luana Licata
- Fondazione
Human Technopole, 20157 Milan, Italy
- Department
of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Federica Quaglia
- Institute
of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), 70126 Bari, Italy
- Department
of Biomedical Sciences, University of Padova, 35131 Padova, Italy
| | | | - Sandra E. Orchard
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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Rouchka EC, de Almeida C, House RB, Daneshmand JC, Chariker JH, Saraswat-Ohri S, Gomes C, Sharp M, Shum-Siu A, Cesarz GM, Petruska JC, Magnuson DS. Construction of a searchable database for gene expression changes in spinal cord injury experiments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.01.526630. [PMID: 36778366 PMCID: PMC9915599 DOI: 10.1101/2023.02.01.526630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Spinal cord injury (SCI) is a debilitating disease resulting in an estimated 18,000 new cases in the United States on an annual basis. Significant behavioral research on animal models has led to a large amount of data, some of which has been catalogued in the Open Data Commons for Spinal Cord Injury (ODC-SCI). More recently, high throughput sequencing experiments have been utilized to understand molecular mechanisms associated with SCI, with nearly 6,000 samples from over 90 studies available in the Sequence Read Archive. However, to date, no resource is available for efficiently mining high throughput sequencing data from SCI experiments. Therefore, we have developed a protocol for processing RNA-Seq samples from high-throughput sequencing experiments related to SCI resulting in both raw and normalized data that can be efficiently mined for comparisons across studies as well as homologous discovery across species. We have processed 1,196 publicly available RNA-seq samples from 50 bulk RNA-Seq studies across nine different species, resulting in an SQLite database that can be used by the SCI research community for further discovery. We provide both the database as well as a web-based front-end that can be used to query the database for genes of interest, differential gene expression, genes with high variance, and gene set enrichments.
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Affiliation(s)
- Eric C. Rouchka
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, University of Louisville, Louisville, KY USA
- Kentucky IDeA Networks of Biomedical Research Excellence (KY INBRE) Bioinformatics Core, University of Louisville School of Medicine, 522 East Gray Street, Louisville, KY USA 40202
- Bioinformatics Program, School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY
| | - Carlos de Almeida
- Translational Neuroscience Program, School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
| | - Randi B. House
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY
| | - Jonah C. Daneshmand
- Bioinformatics Program, School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY
| | - Julia H. Chariker
- Kentucky IDeA Networks of Biomedical Research Excellence (KY INBRE) Bioinformatics Core, University of Louisville School of Medicine, 522 East Gray Street, Louisville, KY USA 40202
- Department of Neuroscience Training, School of Medicine, University of Louisville, Louisville, KY
| | - Sujata Saraswat-Ohri
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY USA
| | - Cynthia Gomes
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY
| | - Morgan Sharp
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY USA
| | - Alice Shum-Siu
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY USA
| | - Greta M. Cesarz
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
| | - Jeffrey C. Petruska
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY USA
- Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY
| | - David S.K. Magnuson
- Translational Neuroscience Program, School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY
- Kentucky Spinal Cord Injury Research Center, School of Medicine, University of Louisville, Louisville, KY
- Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY USA
- Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY
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40
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Zhu H, Leung SW. MicroRNA biomarkers of type 2 diabetes: evidence synthesis from meta-analyses and pathway modelling. Diabetologia 2023; 66:288-299. [PMID: 36269347 PMCID: PMC9807484 DOI: 10.1007/s00125-022-05809-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/10/2022] [Indexed: 01/07/2023]
Abstract
AIMS/HYPOTHESIS MicroRNAs are being sought as biomarkers for the early identification of type 2 diabetes. This study aimed to synthesise the evidence from microRNA-type 2 diabetes association studies and microRNA-regulated type 2 diabetes pathway delineation studies that met stringent quality criteria to identify and validate microRNAs of both statistical and biological significance as type 2 diabetes biomarkers. METHODS Eligible controlled studies on microRNA expression profiling of type 2 diabetes were retrieved from PubMed, ScienceDirect and Web of Science. MicroRNA-regulated type 2 diabetes pathway delineation studies were conducted by integrating and cross-verifying the data from miRTarBase, TransmiR, miRecords, TargetScanHuman, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Retraction Watch database. Before meta-analysis, quality assessment was performed according to the corresponding reporting guidelines for evidence-based medicine. To select the most statistically significant microRNAs, we conducted extensive meta-analyses according to the latest methodology. Subgroup and sensitivity analyses were carried out to further examine the microRNA candidates for their tissue specificity and blood fraction specificity and the robustness of the evidence. Signalling pathway impact analysis of dysregulated microRNAs identified from meta-analyses was performed to select biologically significant microRNAs that were enriched in our newly built microRNA-regulated pathways. RESULTS Of the 404 differentially expressed microRNAs identified in the 156 controlled profiling studies with a combined sample size of >15,000, only 60 were both consistently and significantly dysregulated in human type 2 diabetes. No microRNAs were both consistently and significantly dysregulated in multiple tissues according to subgroup analyses. In total, 58 microRNAs were found to be robust in sensitivity analyses. A total of 1966 pathway delineation studies were identified, including 3290 microRNA-target interactions, which were further combined with KEGG pathways, producing 225 microRNA-regulated pathways. Impact analysis found that 16 dysregulated microRNAs identified from extensive meta-analyses were statistically significantly enriched in the augmented KEGG type 2 diabetes pathway. CONCLUSIONS/INTERPRETATION Sixteen microRNAs met the criteria for biomarker selection. In terms of both significance and relevance, the order of priority for verification of these microRNAs is as follows: miR-29a-3p, miR-221-3p, miR-126-3p, miR-26a-5p, miR-503-5p, miR-100-5p, miR-101-3p, mIR-103a-3p, miR-122-5p, miR-199a-3p, miR-30b-5p, miR-130a-3p, miR-143-3p, miR-145-5p, miR-19a-3p and miR-311-3p. REGISTRATION PROSPERO registration number CRD42017081659.
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Affiliation(s)
- Hongmei Zhu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
- Centre of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, Third People's Hospital of Chengdu, Chengdu, China
- Medical Research Centre, Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China
| | - Siu-Wai Leung
- Edinburgh Bayes Centre for AI Research in Shenzhen, College of Science and Engineering, University of Edinburgh, Edinburgh, UK.
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Viasus D, Simonetti AF, Nonell L, Vidal O, Meije Y, Ortega L, Arnal M, Bódalo-Torruella M, Sierra M, Rombauts A, Abelenda-Alonso G, Blanchart G, Gudiol C, Carratalà J. Whole-Blood Gene Expression Profiles Associated with Mortality in Community-Acquired Pneumonia. Biomedicines 2023; 11:biomedicines11020429. [PMID: 36830965 PMCID: PMC9953679 DOI: 10.3390/biomedicines11020429] [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: 12/05/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Abstract
(1) Background: Information regarding gene expression profiles and the prognosis of community-acquired pneumonia (CAP) is scarce. We aimed to examine the differences in the gene expression profiles in peripheral blood at hospital admission between patients with CAP who died during hospitalization and those who survived. (2) Methods: This is a multicenter study of nonimmunosuppressed adult patients who required hospitalization for CAP. Whole blood samples were obtained within 24 h of admission for genome-expression-profile analysis. Gene expression profiling identified both differentially expressed genes and enriched gene sets. (3) Results: A total of 198 samples from adult patients who required hospitalization for CAP were processed, of which 13 were from patients who died. Comparison of gene expression between patients who died and those who survived yielded 49 differentially expressed genes, 36 of which were upregulated and 13 downregulated. Gene set enrichment analysis (GSEA) identified four positively enriched gene sets in survivors, mainly associated with the interferon-alpha response, apoptosis, and sex hormone pathways. Similarly, GSEA identified seven positively enriched gene sets, associated with the oxidative stress, endoplasmic reticulum stress, oxidative phosphorylation, and angiogenesis pathways, in the patients who died. Protein-protein-interaction-network analysis identified FOS, CDC42, SLC26A10, EIF4G2, CCND3, ASXL1, UBE2S, and AURKA as the main gene hubs. (4) Conclusions: We found differences in gene expression profiles at hospital admission between CAP patients who died and those who survived. Our findings may help to identify novel candidate pathways and targets for potential intervention and biomarkers for risk stratification.
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Affiliation(s)
- Diego Viasus
- Department of Medicine, Division of Health Sciences, Universidad del Norte and Hospital Universidad del Norte, Barranquilla 081001, Colombia
- Correspondence:
| | - Antonella F. Simonetti
- Department of Internal Medicine, Consorci Sanitari Alt Penedès-Garraf, 08720 Sant Pere de Ribes, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Institulo de Salud Carlos III, 28029 Madrid, Spain
| | - Lara Nonell
- MARGenomics, Hospital del Mar Medical Research Institute (IMIM), 08003 Barcelona, Spain
| | - Oscar Vidal
- Department of Medicine, Division of Health Sciences, Universidad del Norte and Hospital Universidad del Norte, Barranquilla 081001, Colombia
| | - Yolanda Meije
- Unit of Infectious Disease, Department of Internal Medicine, Hospital de Barcelona—Societat Cooperativa d’Instal·lacions Assistencials Sanitàries (SCIAS), 08029 Barcelona, Spain
| | - Lucía Ortega
- Unit of Infectious Disease, Department of Internal Medicine, Hospital de Barcelona—Societat Cooperativa d’Instal·lacions Assistencials Sanitàries (SCIAS), 08029 Barcelona, Spain
| | - Magdalena Arnal
- MARGenomics, Hospital del Mar Medical Research Institute (IMIM), 08003 Barcelona, Spain
| | | | - Montserrat Sierra
- Microbiology Unit, Department of Clinical Laboratory, Hospital de Barcelona—Societat Cooperativa d’Instal·lacions Assistencials Sanitàries (SCIAS), 08029 Barcelona, Spain
| | - Alexander Rombauts
- Department of Infectious Diseases, Bellvitge University Hospital—Bellvitge Biomedical Research Institute (IDIBELL), 08907 Barcelona, Spain
| | - Gabriela Abelenda-Alonso
- Department of Infectious Diseases, Bellvitge University Hospital—Bellvitge Biomedical Research Institute (IDIBELL), 08907 Barcelona, Spain
| | - Gemma Blanchart
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Medical Research Institute (IMIM), 08003 Barcelona, Spain
| | - Carlota Gudiol
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Institulo de Salud Carlos III, 28029 Madrid, Spain
- Department of Infectious Diseases, Bellvitge University Hospital—Bellvitge Biomedical Research Institute (IDIBELL), 08907 Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, 08907 Barcelona, Spain
| | - Jordi Carratalà
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Institulo de Salud Carlos III, 28029 Madrid, Spain
- Department of Infectious Diseases, Bellvitge University Hospital—Bellvitge Biomedical Research Institute (IDIBELL), 08907 Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, 08907 Barcelona, Spain
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Holmgren S, Bell SM, Wignall J, Duncan CG, Kwok RK, Cronk R, Osborn K, Black S, Thessen A, Schmitt C. Workshop Report: Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Harmonized Language. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2317. [PMID: 36767684 PMCID: PMC9915042 DOI: 10.3390/ijerph20032317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Harmonized language is essential to finding, sharing, and reusing large-scale, complex data. Gaps and barriers prevent the adoption of harmonized language approaches in environmental health sciences (EHS). To address this, the National Institute of Environmental Health Sciences and partners created the Environmental Health Language Collaborative (EHLC). The purpose of EHLC is to facilitate a community-driven effort to advance the development and adoption of harmonized language approaches in EHS. EHLC is a forum to pinpoint language harmonization gaps, to facilitate the development of, raise awareness of, and encourage the use of harmonization approaches and tools, and to develop new standards and recommendations. To ensure that EHLC's focus and structure would be sustainable long-term and meet the needs of the field, EHLC launched an inaugural workshop in September 2021 focused on "Developing Sustainable Language Solutions" and "Building a Sustainable Community". When the attendees were surveyed, 91% said harmonized language solutions would be of high value/benefit, and 60% agreed to continue contributing to EHLC efforts. Based on workshop discussions, future activities will focus on targeted collaborative use-case working groups in addition to offering education and training on ontologies, metadata, and standards, and developing an EHS language resource portal.
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Affiliation(s)
- Stephanie Holmgren
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
| | | | | | - Christopher G. Duncan
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
| | - Richard K. Kwok
- Division of Neuroscience, National Institute on Aging (NIA), Bethesda, MD 20892, USA
| | - Ryan Cronk
- Health Sciences, ICF, Reston, VA 20190, USA
| | | | | | - Anne Thessen
- Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Charles Schmitt
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
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Fahlgren N, Kapoor M, Yordanova G, Papatheodorou I, Waese J, Cole B, Harrison P, Ware D, Tickle T, Paten B, Burdett T, Elsik CG, Tuggle CK, Provart NJ. Toward a data infrastructure for the Plant Cell Atlas. PLANT PHYSIOLOGY 2023; 191:35-46. [PMID: 36200899 PMCID: PMC9806565 DOI: 10.1093/plphys/kiac468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.
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Affiliation(s)
- Noah Fahlgren
- Donald Danforth Plant Science Center, Saint Louis, Missouri 63132, USA
| | - Muskan Kapoor
- Bioinformatics and Computational Biology Program, Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
| | | | | | - Jamie Waese
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
| | - Benjamin Cole
- DOE-Joint Genome Institute, Lawrence Berkeley National Laboratory, 1, Cyclotron Road, Berkeley, California 94720, USA
| | - Peter Harrison
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Doreen Ware
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, New York 11724, USA
- USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Timothy Tickle
- Data Sciences Platform, The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, Massachusetts 02142, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, Baskin School of Engineering, 1156 High Street, Santa Cruz, California 95064, USA
| | - Tony Burdett
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Christine G Elsik
- Division of Animal Sciences/Division of Plant Science & Technology/Institute for Data Science & Informatics, University of Missouri, Columbia, Missouri 65211, USA
| | - Christopher K Tuggle
- Bioinformatics and Computational Biology Program, Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
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Gouesbet G. Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis. Methods Mol Biol 2023; 2642:257-294. [PMID: 36944884 DOI: 10.1007/978-1-0716-3044-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.
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Affiliation(s)
- Gwenola Gouesbet
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, Biodiversité, Evolution)] - UMR 6553, Rennes, France.
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45
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Elzinga J, Grouls M, Hooiveld GJEJ, van der Zande M, Smidt H, Bouwmeester H. Systematic comparison of transcriptomes of Caco-2 cells cultured under different cellular and physiological conditions. Arch Toxicol 2023; 97:737-753. [PMID: 36680592 PMCID: PMC9862247 DOI: 10.1007/s00204-022-03430-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/13/2022] [Indexed: 01/22/2023]
Abstract
There is a need for standardized in vitro models emulating the functionalities of the human intestinal tract to study human intestinal health without the use of laboratory animals. The Caco-2 cell line is a well-accepted and highly characterized intestinal barrier model, which has been intensively used to study intestinal (drug) transport, host-microbe interactions and chemical or drug toxicity. This cell line has been cultured in different in vitro models, ranging from simple static to complex dynamic microfluidic models. We aimed to investigate the effect of these different in vitro experimental variables on gene expression. To this end, we systematically collected and extracted data from studies in which transcriptome analyses were performed on Caco-2 cells grown on permeable membranes. A collection of 13 studies comprising 100 samples revealed a weak association of experimental variables with overall as well as individual gene expression. This can be explained by the large heterogeneity in cell culture practice, or the lack of adequate reporting thereof, as suggested by our systematic analysis of experimental parameters not included in the main analysis. Given the rapidly increasing use of in vitro cell culture models, including more advanced (micro) fluidic models, our analysis reinforces the need for improved, standardized reporting protocols. Additionally, our systematic analysis serves as a template for future comparative studies on in vitro transcriptome and other experimental data.
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Affiliation(s)
- Janneke Elzinga
- Laboratory of Microbiology, Wageningen University and Research, Wageningen, The Netherlands.
| | - Menno Grouls
- Division of Toxicology, Wageningen University and Research, Wageningen, The Netherlands
| | - Guido J E J Hooiveld
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Meike van der Zande
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Hauke Smidt
- Laboratory of Microbiology, Wageningen University and Research, Wageningen, The Netherlands
| | - Hans Bouwmeester
- Division of Toxicology, Wageningen University and Research, Wageningen, The Netherlands
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46
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Yu C, Qi X, Yan W, Wu W, Shen B. Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:576-585. [PMID: 35085089 DOI: 10.1109/tcbb.2022.3144170] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.
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47
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Porubsky VL, Sauro HM. A Practical Guide to Reproducible Modeling for Biochemical Networks. Methods Mol Biol 2023; 2634:107-138. [PMID: 37074576 DOI: 10.1007/978-1-0716-3008-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.
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Affiliation(s)
| | - Herbert M Sauro
- University of Washington, Department of Bioengineering, Seattle, WA, USA
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48
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Yu H, Li L, Huffman A, Beverley J, Hur J, Merrell E, Huang HH, Wang Y, Liu Y, Ong E, Cheng L, Zeng T, Zhang J, Li P, Liu Z, Wang Z, Zhang X, Ye X, Handelman SK, Sexton J, Eaton K, Higgins G, Omenn GS, Athey B, Smith B, Chen L, He Y. A new framework for host-pathogen interaction research. Front Immunol 2022; 13:1066733. [PMID: 36591248 PMCID: PMC9797517 DOI: 10.3389/fimmu.2022.1066733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
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Affiliation(s)
- Hong Yu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Li Li
- Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
- Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, United States
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Hsin-hui Huang
- University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yang Wang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Liang Cheng
- Department of Bioinformatics, Harbin Medical University, Harbin, Helongjian, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingsong Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pengpai Li
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhiping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhigang Wang
- Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangyan Zhang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Xianwei Ye
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | | | - Jonathan Sexton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathryn Eaton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gerry Higgins
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gilbert S. Omenn
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, United States
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Parmagnani AS, Mannino G, Maffei ME. Transcriptomics and Metabolomics of Reactive Oxygen Species Modulation in Near-Null Magnetic Field-Induced Arabidopsis thaliana. Biomolecules 2022; 12:biom12121824. [PMID: 36551252 PMCID: PMC9775259 DOI: 10.3390/biom12121824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022] Open
Abstract
The geomagnetic field (GMF) is a natural component of Earth's biosphere. GMF reduction to near-null values (NNMF) induces gene expression modulation that generates biomolecular, morphological, and developmental changes. Here, we evaluate the effect of NNMF on gene expression and reactive oxygen species (ROS) production in time-course experiments on Arabidopsis thaliana. Plants exposed to NNMF in a triaxial Helmholtz coils system were sampled from 10 min to 96 h to evaluate differentially expressed genes (DEGs) of oxidative stress responses by gene microarray. In 24-96 h developing stages, H2O2 and polyphenols were also analyzed from roots and shoots. A total of 194 DEGs involved in oxidative reactions were selected, many of which showed a fold change ≥±2 in at least one timing point. Heatmap clustering showed DEGs both between roots/shoots and among the different time points. NNMF induced a lower H2O2 than GMF, in agreement with the expression of ROS-related genes. Forty-four polyphenols were identified, the content of which progressively decreased during NNMF exposition time. The comparison between polyphenols content and DEGs showed overlapping patterns. These results indicate that GMF reduction induces metabolomic and transcriptomic modulation of ROS-scavenging enzymes and H2O2 production in A. thaliana, which is paralleled by the regulation of antioxidant polyphenols.
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50
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Lee KM, Corley R, Jarabek AM, Kleinstreuer N, Paini A, Stucki AO, Bell S. Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT-NAMs Symposium. TOXICS 2022; 10:760. [PMID: 36548593 PMCID: PMC9781465 DOI: 10.3390/toxics10120760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 06/17/2023]
Abstract
New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward the adoption of NAMs for human health and environmental toxicity assessment. However, additional efforts are needed to expand their development and their use in regulatory decision making. A virtual symposium was held during the 2021 Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) Smoke Science and Product Technology (SSPT) conference (titled "Advancing New Alternative Methods for Tobacco Harm Reduction"), with the goals of introducing the concepts and potential application of NAMs in the evaluation of potentially reduced-risk (PRR) tobacco products. At the symposium, experts from regulatory agencies, research organizations, and NGOs shared insights on the status of available tools, strengths, limitations, and opportunities in the application of NAMs using case examples from safety assessments of chemicals and tobacco products. Following seven presentations providing background and application of NAMs, a discussion was held where the presenters and audience discussed the outlook for extending the NAMs toxicological applications for tobacco products. The symposium, endorsed by the CORESTA In Vitro Tox Subgroup, Biomarker Subgroup, and NextG Tox Task Force, illustrated common ground and interest in science-based engagement across the scientific community and stakeholders in support of tobacco regulatory science. Highlights of the symposium are summarized in this paper.
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Affiliation(s)
| | - Richard Corley
- Greek Creek Toxicokinetics Consulting, LLC, Boise, ID 83714, USA
| | - Annie M. Jarabek
- Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, NC 27711, USA
| | - Alicia Paini
- European Commission Joint Research Center (EC JRC), 2749 Ispra, Italy
| | - Andreas O. Stucki
- PETA Science Consortium International e.V., 70499 Stuttgart, Germany
| | - Shannon Bell
- Inotiv-RTP, Research Triangle Park, NC 27709, USA
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