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Taub L, Hampton TH, Sarkar S, Doing G, Neff SL, Finger CE, Ferreira Fukutani K, Stanton BA. E.PathDash, pathway activation analysis of publicly available pathogen gene expression data. mSystems 2024; 9:e0103024. [PMID: 39422483 PMCID: PMC11575265 DOI: 10.1128/msystems.01030-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Accepted: 09/20/2024] [Indexed: 10/19/2024] Open
Abstract
E.PathDash facilitates re-analysis of gene expression data from pathogens clinically relevant to chronic respiratory diseases, including a total of 48 studies, 548 samples, and 404 unique treatment comparisons. The application enables users to assess broad biological stress responses at the KEGG pathway or gene ontology level and also provides data for individual genes. E.PathDash reduces the time required to gain access to data from multiple hours per data set to seconds. Users can download high-quality images such as volcano plots and boxplots, differential gene expression results, and raw count data, making it fully interoperable with other tools. Importantly, users can rapidly toggle between experimental comparisons and different studies of the same phenomenon, enabling them to judge the extent to which observed responses are reproducible. As a proof of principle, we invited two cystic fibrosis scientists to use the application to explore scientific questions relevant to their specific research areas. Reassuringly, pathway activation analysis recapitulated results reported in original publications, but it also yielded new insights into pathogen responses to changes in their environments, validating the utility of the application. All software and data are freely accessible, and the application is available at scangeo.dartmouth.edu/EPathDash. IMPORTANCE Chronic respiratory illnesses impose a high disease burden on our communities and people with respiratory diseases are susceptible to robust bacterial infections from pathogens, including Pseudomonas aeruginosa and Staphylococcus aureus, that contribute to morbidity and mortality. Public gene expression datasets generated from these and other pathogens are abundantly available and an important resource for synthesizing existing pathogenic research, leading to interventions that improve patient outcomes. However, it can take many hours or weeks to render publicly available datasets usable; significant time and skills are needed to clean, standardize, and apply reproducible and robust bioinformatic pipelines to the data. Through collaboration with two microbiologists, we have shown that E.PathDash addresses this problem, enabling them to elucidate pathogen responses to a variety of over 400 experimental conditions and generate mechanistic hypotheses for cell-level behavior in response to disease-relevant exposures, all in a fraction of the time.
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Affiliation(s)
- Lily Taub
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Thomas H Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Sharanya Sarkar
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Georgia Doing
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Samuel L Neff
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Carson E Finger
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Kiyoshi Ferreira Fukutani
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Bruce A Stanton
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
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Pichol-Thievend C, Anezo O, Pettiwala AM, Bourmeau G, Montagne R, Lyne AM, Guichet PO, Deshors P, Ballestín A, Blanchard B, Reveilles J, Ravi VM, Joseph K, Heiland DH, Julien B, Leboucher S, Besse L, Legoix P, Dingli F, Liva S, Loew D, Giani E, Ribecco V, Furumaya C, Marcos-Kovandzic L, Masliantsev K, Daubon T, Wang L, Diaz AA, Schnell O, Beck J, Servant N, Karayan-Tapon L, Cavalli FMG, Seano G. VC-resist glioblastoma cell state: vessel co-option as a key driver of chemoradiation resistance. Nat Commun 2024; 15:3602. [PMID: 38684700 PMCID: PMC11058782 DOI: 10.1038/s41467-024-47985-z] [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: 11/10/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Glioblastoma (GBM) is a highly lethal type of cancer. GBM recurrence following chemoradiation is typically attributed to the regrowth of invasive and resistant cells. Therefore, there is a pressing need to gain a deeper understanding of the mechanisms underlying GBM resistance to chemoradiation and its ability to infiltrate. Using a combination of transcriptomic, proteomic, and phosphoproteomic analyses, longitudinal imaging, organotypic cultures, functional assays, animal studies, and clinical data analyses, we demonstrate that chemoradiation and brain vasculature induce cell transition to a functional state named VC-Resist (vessel co-opting and resistant cell state). This cell state is midway along the transcriptomic axis between proneural and mesenchymal GBM cells and is closer to the AC/MES1-like state. VC-Resist GBM cells are highly vessel co-opting, allowing significant infiltration into the surrounding brain tissue and homing to the perivascular niche, which in turn induces even more VC-Resist transition. The molecular and functional characteristics of this FGFR1-YAP1-dependent GBM cell state, including resistance to DNA damage, enrichment in the G2M phase, and induction of senescence/stemness pathways, contribute to its enhanced resistance to chemoradiation. These findings demonstrate how vessel co-option, perivascular niche, and GBM cell plasticity jointly drive resistance to therapy during GBM recurrence.
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Affiliation(s)
- Cathy Pichol-Thievend
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Oceane Anezo
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Aafrin M Pettiwala
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
- Institut Curie, PSL University, 75005, Paris, France
| | - Guillaume Bourmeau
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Remi Montagne
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Anne-Marie Lyne
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Pierre-Olivier Guichet
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Pauline Deshors
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Alberto Ballestín
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Benjamin Blanchard
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Juliette Reveilles
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Vidhya M Ravi
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Kevin Joseph
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Boris Julien
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | | | - Laetitia Besse
- Institut Curie, PSL University, Université Paris-Saclay, CNRS UMS2016, INSERM US43, Multimodal Imaging Center, 91400, Orsay, France
| | - Patricia Legoix
- Institut Curie, PSL University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
| | - Florent Dingli
- Institut Curie, PSL University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Stephane Liva
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Damarys Loew
- Institut Curie, PSL University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Elisa Giani
- Department of Biomedical Sciences, Humanitas University, 20072, Pieve Emanuele, Italy
| | - Valentino Ribecco
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Charita Furumaya
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Laura Marcos-Kovandzic
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Konstantin Masliantsev
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Thomas Daubon
- Université Bordeaux, CNRS, IBGC, UMR5095, Bordeaux, France
| | - Lin Wang
- Department of Computational and Quantitative Medicine, Hematologic Malignancies Research Institute and Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Aaron A Diaz
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Nicolas Servant
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Lucie Karayan-Tapon
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Florence M G Cavalli
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Giorgio Seano
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France.
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Mubeen S, Tom Kodamullil A, Hofmann-Apitius M, Domingo-Fernández D. On the influence of several factors on pathway enrichment analysis. Brief Bioinform 2022; 23:bbac143. [PMID: 35453140 PMCID: PMC9116215 DOI: 10.1093/bib/bbac143] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/21/2022] [Accepted: 03/30/2022] [Indexed: 02/01/2023] Open
Abstract
Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.
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Affiliation(s)
- Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
- Fraunhofer Center for Machine Learning, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
- Fraunhofer Center for Machine Learning, Germany
- Enveda Biosciences, Boulder, CO, 80301, USA
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