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Chen H, Zhang S, Zhang L, Geng J, Lu J, Hou C, He P, Lu X. Multi role ChatGPT framework for transforming medical data analysis. Sci Rep 2024; 14:13930. [PMID: 38886470 PMCID: PMC11183233 DOI: 10.1038/s41598-024-64585-5] [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: 03/07/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
The application of ChatGPTin the medical field has sparked debate regarding its accuracy. To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to improve ChatGPT's performance in medical data analysis by optimizing prompt words, integrating real-world data, and implementing quality control protocols. Compared to the singular ChatGPT model, MRCF significantly outperforms traditional manual analysis in interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identification of incorrect information. Notably, MRCF is over 600 times more time-efficient than conventional manual annotation methods and costs only one-tenth as much. Leveraging MRCF, we have established two user-friendly databases for efficient and straightforward drug repositioning analysis. This research not only enhances the accuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insights for data analysis models across various professional domains.
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Affiliation(s)
- Haoran Chen
- School of Management, Shanxi Medical University, Taiyuan, 030000, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, 100853, China
| | - Shengxiao Zhang
- Department of Rheumatology and Immunology, The Second Hospital of Shanxi Medical University, Taiyuan, China
- Key Laboratory of Coal Environmental Pathogenicity and Prevention at Shanxi Medical University, Ministry of Education, Taiyuan, Shanxi, China
- SXMU-Tsinghua Collaborative Innovation Center for Frontier Medicine, Shanxi Medical University, Taiyuan, China
| | - Lizhong Zhang
- Basic Medicine College, Shanxi Medical University, Taiyuan, 030000, China
| | - Jie Geng
- Basic Medicine College, Shanxi Medical University, Taiyuan, 030000, China
| | - Jinqi Lu
- Department of Computer Science, Boston University, 665 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Chuandong Hou
- Department of Hematology, The Second Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Geriatric Disease, Beijing, 100853, China
- Medical School of Chinese PLA, Beijing, 100853, China
| | - Peifeng He
- School of Management, Shanxi Medical University, Taiyuan, 030000, China.
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, 030000, China.
| | - Xuechun Lu
- School of Management, Shanxi Medical University, Taiyuan, 030000, China.
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, 100853, China.
- Department of Hematology, The Second Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Geriatric Disease, Beijing, 100853, China.
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Oviya IR, Sankar D, Manoharan S, Prabahar A, Raja K. Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer's Disease. Genes (Basel) 2024; 15:614. [PMID: 38790243 PMCID: PMC11121575 DOI: 10.3390/genes15050614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/28/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Alzheimer's disease (AD), a multifactorial neurodegenerative disorder, is prevalent among the elderly population. It is a complex trait with mutations in multiple genes. Although the US Food and Drug Administration (FDA) has approved a few drugs for AD treatment, a definitive cure remains elusive. Research efforts persist in seeking improved treatment options for AD. Here, a hybrid pipeline is proposed to apply text mining to identify comorbid diseases for AD and an omics approach to identify the common genes between AD and five comorbid diseases-dementia, type 2 diabetes, hypertension, Parkinson's disease, and Down syndrome. We further identified the pathways and drugs for common genes. The rationale behind this approach is rooted in the fact that elderly individuals often receive multiple medications for various comorbid diseases, and an insight into the genes that are common to comorbid diseases may enhance treatment strategies. We identified seven common genes-PSEN1, PSEN2, MAPT, APP, APOE, NOTCH, and HFE-for AD and five comorbid diseases. We investigated the drugs interacting with these common genes using LINCS gene-drug perturbation. Our analysis unveiled several promising candidates, including MG-132 and Masitinib, which exhibit potential efficacy for both AD and its comorbid diseases. The pipeline can be extended to other diseases.
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Affiliation(s)
- Iyappan Ramalakshmi Oviya
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai 641112, India;
| | - Divya Sankar
- Department of Sciences, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai 601103, India;
| | - Sharanya Manoharan
- Department of Bioinformatics, Stella Maris College, Chennai 600086, India;
| | - Archana Prabahar
- Center for Gene Regulation in Health and Disease, Department of Biological, Geological, and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA;
| | - Kalpana Raja
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA
- Section for Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, USA
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3
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Clarke DJB, Marino GB, Deng EZ, Xie Z, Evangelista JE, Ma'ayan A. Rummagene: massive mining of gene sets from supporting materials of biomedical research publications. Commun Biol 2024; 7:482. [PMID: 38643247 PMCID: PMC11032387 DOI: 10.1038/s42003-024-06177-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 04/10/2024] [Indexed: 04/22/2024] Open
Abstract
Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .
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Affiliation(s)
- Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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4
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Dhaka B, Zimmerli M, Hanhart D, Moser M, Guillen-Ramirez H, Mishra S, Esposito R, Polidori T, Widmer M, García-Pérez R, Julio MKD, Pervouchine D, Melé M, Chouvardas P, Johnson R. Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates. Nucleic Acids Res 2024; 52:2821-2835. [PMID: 38348970 PMCID: PMC11014264 DOI: 10.1093/nar/gkae075] [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: 05/31/2023] [Revised: 01/15/2024] [Accepted: 01/26/2024] [Indexed: 03/09/2024] Open
Abstract
A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.
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Affiliation(s)
- Bhavya Dhaka
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
| | - Marc Zimmerli
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Daniel Hanhart
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Mario B Moser
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Hugo Guillen-Ramirez
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sanat Mishra
- Indian Institute of Science Education and Research, Mohali, India
| | - Roberta Esposito
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Taisia Polidori
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Maro Widmer
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Raquel García-Pérez
- Department of Life Sciences, Barcelona Supercomputing Centre, Barcelona 08034, Spain
| | - Marianna Kruithof-de Julio
- Department of Urology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Urology Research Laboratory, Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Dmitri Pervouchine
- Center for Cellular and Molecular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Marta Melé
- Department of Life Sciences, Barcelona Supercomputing Centre, Barcelona 08034, Spain
| | - Panagiotis Chouvardas
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- Department of Urology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Urology Research Laboratory, Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Rory Johnson
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- FutureNeuro SFI Research Centre, University College Dublin, Dublin D04 V1W8, Ireland
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5
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Petralia F, Ma W, Yaron TM, Caruso FP, Tignor N, Wang JM, Charytonowicz D, Johnson JL, Huntsman EM, Marino GB, Calinawan A, Evangelista JE, Selvan ME, Chowdhury S, Rykunov D, Krek A, Song X, Turhan B, Christianson KE, Lewis DA, Deng EZ, Clarke DJB, Whiteaker JR, Kennedy JJ, Zhao L, Segura RL, Batra H, Raso MG, Parra ER, Soundararajan R, Tang X, Li Y, Yi X, Satpathy S, Wang Y, Wiznerowicz M, González-Robles TJ, Iavarone A, Gosline SJC, Reva B, Robles AI, Nesvizhskii AI, Mani DR, Gillette MA, Klein RJ, Cieslik M, Zhang B, Paulovich AG, Sebra R, Gümüş ZH, Hostetter G, Fenyö D, Omenn GS, Cantley LC, Ma'ayan A, Lazar AJ, Ceccarelli M, Wang P. Pan-cancer proteogenomics characterization of tumor immunity. Cell 2024; 187:1255-1277.e27. [PMID: 38359819 PMCID: PMC10988632 DOI: 10.1016/j.cell.2024.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/29/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.
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Affiliation(s)
- Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
| | - Francesca Pia Caruso
- BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy; Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Nicole Tignor
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joshua M Wang
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Daniel Charytonowicz
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Myvizhi Esai Selvan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Xiaoyu Song
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Berk Turhan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karen E Christianson
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David A Lewis
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeffrey R Whiteaker
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jacob J Kennedy
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Lei Zhao
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Rossana Lazcano Segura
- Departments of Pathology & Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Harsh Batra
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maria Gabriela Raso
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Edwin Roger Parra
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rama Soundararajan
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ximing Tang
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Ying Wang
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Maciej Wiznerowicz
- Department of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznań, Poland; International Institute for Molecular Oncology, 60-203 Poznań, Poland; Department of Oncology, Heliodor Swiecicki Clinical Hospital, 60-203 Poznań, Poland
| | - Tania J González-Robles
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Antonio Iavarone
- Department of Neurological Surgery, Department of Biochemistry, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Alexey I Nesvizhskii
- Departments of Pathology and Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Marcin Cieslik
- Departments of Pathology and Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Amanda G Paulovich
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Galen Hostetter
- Pathology and Biorepository Core, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - David Fenyö
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & Environmental Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Michele Ceccarelli
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA; Department of Public Health Sciences, University of Miami, Miami, FL, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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6
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Feng F, Duan Q, Jiang X, Kao X, Zhang D. DendroX: multi-level multi-cluster selection in dendrograms. BMC Genomics 2024; 25:134. [PMID: 38308243 PMCID: PMC10835886 DOI: 10.1186/s12864-024-10048-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/24/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram. RESULTS We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities. CONCLUSIONS DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.
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Affiliation(s)
- Feiling Feng
- Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Qiaonan Duan
- Department of Clinical and Translational Medicine, 3D Medicines Inc., Shanghai, China
| | - Xiaoqing Jiang
- Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Xiaoming Kao
- Research Institute of General Surgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
| | - Dadong Zhang
- Department of Clinical and Translational Medicine, 3D Medicines Inc., Shanghai, China.
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7
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Kang H, Pan S, Lin S, Wang YY, Yuan N, Jia P. PharmGWAS: a GWAS-based knowledgebase for drug repurposing. Nucleic Acids Res 2024; 52:D972-D979. [PMID: 37831083 PMCID: PMC10767932 DOI: 10.1093/nar/gkad832] [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: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023] Open
Abstract
Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.
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Affiliation(s)
- Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yin-Ying Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Na Yuan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
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8
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Marino GB, Ahmed N, Xie Z, Jagodnik KM, Han J, Clarke DJB, Lachmann A, Keller MP, Attie AD, Ma’ayan A. D2H2: diabetes data and hypothesis hub. BIOINFORMATICS ADVANCES 2023; 3:vbad178. [PMID: 38107655 PMCID: PMC10723036 DOI: 10.1093/bioadv/vbad178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/25/2023] [Accepted: 12/02/2023] [Indexed: 12/19/2023]
Abstract
Motivation There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2). Results D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools' API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation. Availability and implementation D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.
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Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Nasheath Ahmed
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kathleen M Jagodnik
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Jason Han
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, United States
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, United States
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
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9
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Xie Z, Chen C, Ma’ayan A. Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis. PeerJ 2023; 11:e16351. [PMID: 37953774 PMCID: PMC10638921 DOI: 10.7717/peerj.16351] [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] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/04/2023] [Indexed: 11/14/2023] Open
Abstract
Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.
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Affiliation(s)
- Zhuorui Xie
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clara Chen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avi Ma’ayan
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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10
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Ji X, Williams KP, Zheng W. Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer. Cancer Inform 2023; 22:11769351231202588. [PMID: 37846218 PMCID: PMC10576937 DOI: 10.1177/11769351231202588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/01/2023] [Indexed: 10/18/2023] Open
Abstract
The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.
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Affiliation(s)
- Xiaojia Ji
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Kevin P Williams
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
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11
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Takasugi M, Yoshida Y, Nonaka Y, Ohtani N. Gene expressions associated with longer lifespan and aging exhibit similarity in mammals. Nucleic Acids Res 2023; 51:7205-7219. [PMID: 37351606 PMCID: PMC10415134 DOI: 10.1093/nar/gkad544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/08/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
Although molecular features underlying aging and species maximum lifespan (MLS) have been comprehensively studied by transcriptome analyses, the actual impact of transcriptome on aging and MLS remains elusive. Here, we found that transcriptional signatures that are associated with mammalian MLS exhibited significant similarity to those of aging. Moreover, transcriptional signatures of longer MLS and aging both exhibited significant similarity to that of longer-lived mouse strains, suggesting that gene expression patterns associated with species MLS contribute to extended lifespan even within a species and that aging-related gene expression changes overall represent adaptations that extend lifespan rather than deterioration. Finally, we found evidence of co-evolution of MLS and promoter sequences of MLS-associated genes, highlighting the evolutionary contribution of specific transcription factor binding motifs such as that of E2F1 in shaping MLS-associated gene expression signature. Our results highlight the importance of focusing on adaptive aspects of aging transcriptome and demonstrate that cross-species genomics can be a powerful approach for understanding adaptive aging transcriptome.
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Affiliation(s)
- Masaki Takasugi
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Yuya Yoshida
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Yoshiki Nonaka
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
| | - Naoko Ohtani
- Department of Pathophysiology, Osaka Metropolitan University, Graduate School of Medicine, Osaka, Japan
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12
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Evangelista JE, Clarke DJB, Xie Z, Marino GB, Utti V, Jenkins SL, Ahooyi TM, Bologa CG, Yang JJ, Binder JL, Kumar P, Lambert CG, Grethe JS, Wenger E, Taylor D, Oprea TI, de Bono B, Ma'ayan A. Toxicology knowledge graph for structural birth defects. COMMUNICATIONS MEDICINE 2023; 3:98. [PMID: 37460679 PMCID: PMC10352311 DOI: 10.1038/s43856-023-00329-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/29/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vivian Utti
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sherry L Jenkins
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Taha Mohseni Ahooyi
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Cristian G Bologa
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeremy J Yang
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jessica L Binder
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Praveen Kumar
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Christophe G Lambert
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeffrey S Grethe
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Eric Wenger
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Deanne Taylor
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Tudor I Oprea
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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13
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Marino G, Ngai M, Clarke DB, Fleishman R, Deng E, Xie Z, Ahmed N, Ma’ayan A. GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery. Nucleic Acids Res 2023; 51:W213-W224. [PMID: 37166966 PMCID: PMC10320068 DOI: 10.1093/nar/gkad399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/23/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.
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Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Ngai
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Reid H Fleishman
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nasheath Ahmed
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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14
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Evangelista JE, Xie Z, Marino GB, Nguyen N, Clarke DB, Ma’ayan A. Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic Acids Res 2023; 51:W168-W179. [PMID: 37166973 PMCID: PMC10320098 DOI: 10.1093/nar/gkad393] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/23/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Nhi Nguyen
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
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15
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Xie D, Huang Q, Zhou P. Drug Discovery Targeting Post-Translational Modifications in Response to DNA Damages Induced by Space Radiation. Int J Mol Sci 2023; 24:ijms24087656. [PMID: 37108815 PMCID: PMC10142602 DOI: 10.3390/ijms24087656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
DNA damage in astronauts induced by cosmic radiation poses a major barrier to human space exploration. Cellular responses and repair of the most lethal DNA double-strand breaks (DSBs) are crucial for genomic integrity and cell survival. Post-translational modifications (PTMs), including phosphorylation, ubiquitylation, and SUMOylation, are among the regulatory factors modulating a delicate balance and choice between predominant DSB repair pathways, such as non-homologous end joining (NHEJ) and homologous recombination (HR). In this review, we focused on the engagement of proteins in the DNA damage response (DDR) modulated by phosphorylation and ubiquitylation, including ATM, DNA-PKcs, CtIP, MDM2, and ubiquitin ligases. The involvement and function of acetylation, methylation, PARylation, and their essential proteins were also investigated, providing a repository of candidate targets for DDR regulators. However, there is a lack of radioprotectors in spite of their consideration in the discovery of radiosensitizers. We proposed new perspectives for the research and development of future agents against space radiation by the systematic integration and utilization of evolutionary strategies, including multi-omics analyses, rational computing methods, drug repositioning, and combinations of drugs and targets, which may facilitate the use of radioprotectors in practical applications in human space exploration to combat fatal radiation hazards.
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Affiliation(s)
- Dafei Xie
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
| | - Qi Huang
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
- Department of Preventive Medicine, School of Public Health, University of South China, Changsheng West Road 28th, Zhengxiang District, Hengyang 421001, China
| | - Pingkun Zhou
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
- Department of Preventive Medicine, School of Public Health, University of South China, Changsheng West Road 28th, Zhengxiang District, Hengyang 421001, China
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16
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Das D, Chakrabarty B, Srinivasan R, Roy A. Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures. J Chem Inf Model 2023; 63:1882-1893. [PMID: 36971750 DOI: 10.1021/acs.jcim.2c01301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.
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17
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Marino GB, Wojciechowicz ML, Clarke DJB, Kuleshov MV, Xie Z, Jeon M, Lachmann A, Ma’ayan A. lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs. Database (Oxford) 2023; 2023:7069621. [PMID: 36869839 PMCID: PMC9985331 DOI: 10.1093/database/baad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 01/25/2023] [Accepted: 02/11/2023] [Indexed: 03/05/2023]
Abstract
Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.
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Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L Wojciechowicz
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim V Kuleshov
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Avi Ma’ayan
- *Corresponding author: Tel: +001-212-241-1153; Fax: +001-212-849-2456;
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18
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Hong J, Wong B, Rhodes CJ, Kurt Z, Schwantes-An TH, Mickler EA, Gräf S, Eyries M, Lutz KA, Pauciulo MW, Trembath RC, Montani D, Morrell NW, Wilkins MR, Nichols WC, Trégouët DA, Aldred MA, Desai AA, Tuder RM, Geraci MW, Eghbali M, Stearman RS, Yang X. Integrative Multiomics to Dissect the Lung Transcriptional Landscape of Pulmonary Arterial Hypertension. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.12.523812. [PMID: 36712057 PMCID: PMC9882207 DOI: 10.1101/2023.01.12.523812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Pulmonary arterial hypertension (PAH) remains an incurable and often fatal disease despite currently available therapies. Multiomics systems biology analysis can shed new light on PAH pathobiology and inform translational research efforts. Using RNA sequencing on the largest PAH lung biobank to date (96 disease and 52 control), we aim to identify gene co-expression network modules associated with PAH and potential therapeutic targets. Co-expression network analysis was performed to identify modules of co-expressed genes which were then assessed for and prioritized by importance in PAH, regulatory role, and therapeutic potential via integration with clinicopathologic data, human genome-wide association studies (GWAS) of PAH, lung Bayesian regulatory networks, single-cell RNA-sequencing data, and pharmacotranscriptomic profiles. We identified a co-expression module of 266 genes, called the pink module, which may be a response to the underlying disease process to counteract disease progression in PAH. This module was associated not only with PAH severity such as increased PVR and intimal thickness, but also with compensated PAH such as lower number of hospitalizations, WHO functional class and NT-proBNP. GWAS integration demonstrated the pink module is enriched for PAH-associated genetic variation in multiple cohorts. Regulatory network analysis revealed that BMPR2 regulates the main target of FDA-approved riociguat, GUCY1A2, in the pink module. Analysis of pathway enrichment and pink hub genes (i.e. ANTXR1 and SFRP4) suggests the pink module inhibits Wnt signaling and epithelial-mesenchymal transition. Cell type deconvolution showed the pink module correlates with higher vascular cell fractions (i.e. myofibroblasts). A pharmacotranscriptomic screen discovered ubiquitin-specific peptidases (USPs) as potential therapeutic targets to mimic the pink module signature. Our multiomics integrative study uncovered a novel gene subnetwork associated with clinicopathologic severity, genetic risk, specific vascular cell types, and new therapeutic targets in PAH. Future studies are warranted to investigate the role and therapeutic potential of the pink module and targeting USPs in PAH.
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Jeon M, Xie Z, Evangelista JE, Wojciechowicz ML, Clarke DJB, Ma’ayan A. Transforming L1000 profiles to RNA-seq-like profiles with deep learning. BMC Bioinformatics 2022; 23:374. [PMID: 36100892 PMCID: PMC9472394 DOI: 10.1186/s12859-022-04895-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 08/17/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractThe L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson’s correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.
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Kwee I, Martinelli A, Khayal LA, Akhmedov M. metaLINCS: an R package for meta-level analysis of LINCS L1000 drug signatures using stratified connectivity mapping. BIOINFORMATICS ADVANCES 2022; 2:vbac064. [PMID: 36699415 PMCID: PMC9710587 DOI: 10.1093/bioadv/vbac064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/18/2022] [Accepted: 09/08/2022] [Indexed: 02/01/2023]
Abstract
Summary Accessing the collection of perturbed gene expression profiles, such as the LINCS L1000 connectivity map, is usually performed at the individual dataset level, followed by a summary performed by counting individual hits for each perturbagen. With the metaLINCS R package, we present an alternative approach that combines rank correlation and gene set enrichment analysis to identify meta-level enrichment at the perturbagen level and, in the case of drugs, at the mechanism of action level. This significantly simplifies the interpretation and highlights overarching themes in the data. We demonstrate the functionality of the package and compare its performance against those of three currently used approaches. Availability and implementation metaLINCS is released under GPL3 license. Source code and documentation are freely available on GitHub (https://github.com/bigomics/metaLINCS). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Ivo Kwee
- To whom correspondence should be addressed.
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21
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Qian Y, Itzel T, Ebert M, Teufel A. Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers. Cancers (Basel) 2022; 14:cancers14174322. [PMID: 36077860 PMCID: PMC9454845 DOI: 10.3390/cancers14174322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications. METHODS We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures. RESULTS Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively. CONCLUSION Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.
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Affiliation(s)
- Yuquan Qian
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Timo Itzel
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Matthias Ebert
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health Baden-Württemberg (CPDBW), Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Andreas Teufel
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health Baden-Württemberg (CPDBW), Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
- Correspondence: ; Tel.: +49-(0)621-383-4983; Fax: +49-(0)621-383-1467
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22
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Chan WH, Hsu YJ, Cheng CP, Chou KN, Chen CL, Huang SM, Kan WC, Chiu YL. Assessing the Global Impact on the Mouse Kidney After Traumatic Brain Injury: A Transcriptomic Study. J Inflamm Res 2022; 15:4833-4851. [PMID: 36042866 PMCID: PMC9420446 DOI: 10.2147/jir.s375088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/19/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose In this study, we use animal models combined with bioinformatics strategies to investigate the potential changes in overall renal transcriptional expression after traumatic brain injury. Methods Microarray analysis was performed after kidney acquisition using unilateral controlled cortical impact as the primary mouse TBI model. Multi-oriented gene set enrichment analysis was performed for differentially expressed genes. Results The results showed that TBI affected the gene set associated with mitochondria function in kidney cells, and a negative enrichment of gene sets associated with immune cell migration and epidermal development was also observed. Analysis of the disease phenotype gene set revealed that differential expression of mitochondria-related genes was associated with lactate metabolism. Alternatively, activation and adhesion of immune cells associated with the complement system may promote autoinflammation in kidney tissue. The simulated immune cell infiltration analysis showed an increase in the proportion of activated memory CD4 T cells and a decrease in the proportion of resting memory CD4 T cells, suggesting that activated memory CD4 T cell infiltration may be involved in the inflammation of renal tissue and cause damage to renal cells, such as principal cells, mesangial cells and loops of Henle cells. Conclusion This study is the first to reveal the effects of brain trauma on the kidney. TBI may affect the expression of mitochondria function-related gene sets in renal cells by increasing lactate. It may also affect renal mesangial cells by inducing increased infiltration of immune cells through mechanisms related to complement system activation or autoimmune antibodies.
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Affiliation(s)
- Wei-Hung Chan
- Department of Anesthesiology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China.,Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Yu-Juei Hsu
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Chiao-Pei Cheng
- Department of Anesthesiology, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Kuan-Nien Chou
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei City, Taiwan, Republic of China.,Department of Neurosurgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Chin-Li Chen
- Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Shih-Ming Huang
- Department of Biochemistry, National Defense Medical Center, Taipei City, Taiwan, Republic of China
| | - Wei-Chih Kan
- Department of Nephrology, Department of Internal Medicine, Chi-Mei Medical Center, Tainan City, Taiwan, Republic of China.,Department of Biological Science and Technology, Chung Hwa University of Medical Technology, Tainan City, Taiwan, Republic of China
| | - Yi-Lin Chiu
- Department of Biochemistry, National Defense Medical Center, Taipei City, Taiwan, Republic of China
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23
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Xie Z, Kropiwnicki E, Wojciechowicz ML, Jagodnik KM, Shu I, Bailey A, Clarke DJB, Jeon M, Evangelista JE, Kuleshov M, Lachmann A, Parigi AA, Sanchez JM, Jenkins SL, Ma’ayan A. Getting Started with LINCS Datasets and Tools. Curr Protoc 2022; 2:e487. [PMID: 35876555 PMCID: PMC9326873 DOI: 10.1002/cpz1.487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.
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Affiliation(s)
- Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Eryk Kropiwnicki
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Ingrid Shu
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Allison Bailey
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J. B. Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Minji Jeon
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Maxim Kuleshov
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Abhijna A. Parigi
- School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Jose M. Sanchez
- School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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