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Majumder MA, Leek JT, Hansen KD, Razi A, McGuire AL. Large-scale genotype prediction from RNA sequence data necessitates a new ethical and policy framework. Nat Genet 2024:10.1038/s41588-024-01825-4. [PMID: 39039279 DOI: 10.1038/s41588-024-01825-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Affiliation(s)
- Mary A Majumder
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA.
| | - Jeffrey T Leek
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kasper D Hansen
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Afrooz Razi
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
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2
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Urzúa-Traslaviña CG, van Lieshout T, Boulogne F, Domanegg K, Zidan M, Bakker OB, Claringbould A, de Ridder J, Zwart W, Westra HJ, Deelen P, Franke L. Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes. BMC Med Genomics 2024; 17:186. [PMID: 39010058 PMCID: PMC11247850 DOI: 10.1186/s12920-024-01941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 06/18/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.
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Affiliation(s)
- Carlos G Urzúa-Traslaviña
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Tijs van Lieshout
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Floranne Boulogne
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Kevin Domanegg
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Mahmoud Zidan
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Olivier B Bakker
- Wellcome Sanger Institute, Human Genetics, Hinxton, UK
- Open Targets, Hinxton, UK
| | - Annique Claringbould
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- EMBL Heidelberg, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Jeroen de Ridder
- Oncode Institute, Utrecht, The Netherlands
- University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wilbert Zwart
- Oncode Institute, Utrecht, The Netherlands
- Division of Oncogenomics, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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Saha E, Guebila MB, Fanfani V, Shutta KH, DeMeo DL, Quackenbush J, Lopes-Ramos CM. Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601689. [PMID: 39005266 PMCID: PMC11244978 DOI: 10.1101/2024.07.02.601689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.
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Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
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4
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Ravindran A, Holappa L, Niskanen H, Skovorodkin I, Kaisto S, Beter M, Kiema M, Selvarajan I, Nurminen V, Aavik E, Aherrahrou R, Pasonen-Seppänen S, Fortino V, Laakkonen JP, Ylä-Herttuala S, Vainio S, Örd T, Kaikkonen MU. Translatome profiling reveals Itih4 as a novel smooth muscle cell-specific gene in atherosclerosis. Cardiovasc Res 2024; 120:869-882. [PMID: 38289873 PMCID: PMC11218691 DOI: 10.1093/cvr/cvae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 12/01/2023] [Accepted: 12/12/2023] [Indexed: 02/01/2024] Open
Abstract
AIMS Vascular smooth muscle cells (SMCs) and their derivatives are key contributors to the development of atherosclerosis. However, studying changes in SMC gene expression in heterogeneous vascular tissues is challenging due to the technical limitations and high cost associated with current approaches. In this paper, we apply translating ribosome affinity purification sequencing to profile SMC-specific gene expression directly from tissue. METHODS AND RESULTS To facilitate SMC-specific translatome analysis, we generated SMCTRAP mice, a transgenic mouse line expressing enhanced green fluorescent protein (EGFP)-tagged ribosomal protein L10a (EGFP-L10a) under the control of the SMC-specific αSMA promoter. These mice were further crossed with the atherosclerosis model Ldlr-/-, ApoB100/100 to generate SMCTRAP-AS mice and used to profile atherosclerosis-associated SMCs in thoracic aorta samples of 15-month-old SMCTRAP and SMCTRAP-AS mice. Our analysis of SMCTRAP-AS mice showed that EGFP-L10a expression was localized to SMCs in various tissues, including the aortic wall and plaque. The TRAP fraction demonstrated high enrichment of known SMC-specific genes, confirming the specificity of our approach. We identified several genes, including Cemip, Lum, Mfge8, Spp1, and Serpina3, which are known to be involved in atherosclerosis-induced gene expression. Moreover, we identified several novel genes not previously linked to SMCs in atherosclerosis, such as Anxa4, Cd276, inter-alpha-trypsin inhibitor-4 (Itih4), Myof, Pcdh11x, Rab31, Serpinb6b, Slc35e4, Slc8a3, and Spink5. Among them, we confirmed the SMC-specific expression of Itih4 in atherosclerotic lesions using immunofluorescence staining of mouse aortic roots and spatial transcriptomics of human carotid arteries. Furthermore, our more detailed analysis of Itih4 showed its link to coronary artery disease through the colocalization of genome-wide association studies, splice quantitative trait loci (QTL), and protein QTL signals. CONCLUSION We generated a SMC-specific TRAP mouse line to study atherosclerosis and identified Itih4 as a novel SMC-expressed gene in atherosclerotic plaques, warranting further investigation of its putative function in extracellular matrix stability and genetic evidence of causality.
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MESH Headings
- Animals
- Myocytes, Smooth Muscle/metabolism
- Myocytes, Smooth Muscle/pathology
- Atherosclerosis/genetics
- Atherosclerosis/metabolism
- Atherosclerosis/pathology
- Muscle, Smooth, Vascular/metabolism
- Muscle, Smooth, Vascular/pathology
- Disease Models, Animal
- Aortic Diseases/genetics
- Aortic Diseases/pathology
- Aortic Diseases/metabolism
- Ribosomal Proteins/genetics
- Ribosomal Proteins/metabolism
- Plaque, Atherosclerotic
- Mice, Inbred C57BL
- Male
- Gene Expression Regulation
- Gene Expression Profiling
- Phenotype
- Transcriptome
- Receptors, LDL/genetics
- Receptors, LDL/metabolism
- Apolipoprotein B-100/genetics
- Apolipoprotein B-100/metabolism
- Mice, Knockout
- Humans
- Green Fluorescent Proteins/genetics
- Green Fluorescent Proteins/metabolism
- Mice
- Mice, Transgenic
- Aorta/metabolism
- Aorta/pathology
- Female
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Affiliation(s)
- Aarthi Ravindran
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Lari Holappa
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Henri Niskanen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Ilya Skovorodkin
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Susanna Kaisto
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Mustafa Beter
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Miika Kiema
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Ilakya Selvarajan
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Valtteri Nurminen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Einari Aavik
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Rédouane Aherrahrou
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
- Institute for Cardiogenetics, Universität zu Lübeck, 23562 Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Heart Centre Lübeck, 23562 Lübeck, Germany
| | - Sanna Pasonen-Seppänen
- Institute of Biomedicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Johanna P Laakkonen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Seppo Ylä-Herttuala
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Seppo Vainio
- Disease networks research unit, Faculty of Biochemistry and Molecular Medicine, Kvantum Institute, Infotech Oulu, University of Oulu, Oulu, Finland
| | - Tiit Örd
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Minna U Kaikkonen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
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5
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Pardo-Palacios FJ, Wang D, Reese F, Diekhans M, Carbonell-Sala S, Williams B, Loveland JE, De María M, Adams MS, Balderrama-Gutierrez G, Behera AK, Gonzalez Martinez JM, Hunt T, Lagarde J, Liang CE, Li H, Meade MJ, Moraga Amador DA, Prjibelski AD, Birol I, Bostan H, Brooks AM, Çelik MH, Chen Y, Du MRM, Felton C, Göke J, Hafezqorani S, Herwig R, Kawaji H, Lee J, Li JL, Lienhard M, Mikheenko A, Mulligan D, Nip KM, Pertea M, Ritchie ME, Sim AD, Tang AD, Wan YK, Wang C, Wong BY, Yang C, Barnes I, Berry AE, Capella-Gutierrez S, Cousineau A, Dhillon N, Fernandez-Gonzalez JM, Ferrández-Peral L, Garcia-Reyero N, Götz S, Hernández-Ferrer C, Kondratova L, Liu T, Martinez-Martin A, Menor C, Mestre-Tomás J, Mudge JM, Panayotova NG, Paniagua A, Repchevsky D, Ren X, Rouchka E, Saint-John B, Sapena E, Sheynkman L, Smith ML, Suner MM, Takahashi H, Youngworth IA, Carninci P, Denslow ND, Guigó R, Hunter ME, Maehr R, Shen Y, Tilgner HU, Wold BJ, Vollmers C, Frankish A, Au KF, Sheynkman GM, Mortazavi A, Conesa A, Brooks AN. Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. Nat Methods 2024; 21:1349-1363. [PMID: 38849569 DOI: 10.1038/s41592-024-02298-3] [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: 08/02/2021] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.
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Affiliation(s)
| | - Dingjie Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fairlie Reese
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Sílvia Carbonell-Sala
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Jane E Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Maite De María
- Department of Physiological Sciences, College of Veterinary Medicine, Gainesville, FL, USA
- Cherokee Nation System Solutions, contractor to the US Geological Survey-Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Matthew S Adams
- Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Gabriela Balderrama-Gutierrez
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Amit K Behera
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jose M Gonzalez Martinez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Julien Lagarde
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Flomics Biotech, SL, Barcelona, Spain
| | - Cindy E Liang
- Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Haoran Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Marcus Jerryd Meade
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - David A Moraga Amador
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Andrey D Prjibelski
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Center for Bioinformatics and Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Hamed Bostan
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Ashley M Brooks
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Muhammed Hasan Çelik
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Ying Chen
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Mei R M Du
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Colette Felton
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jonathan Göke
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
| | - Saber Hafezqorani
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Ralf Herwig
- Department Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany
| | - Hideya Kawaji
- Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Joseph Lee
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Jian-Liang Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Matthias Lienhard
- Department Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany
| | - Alla Mikheenko
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Dennis Mulligan
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Ka Ming Nip
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Mihaela Pertea
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Matthew E Ritchie
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Andre D Sim
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Alison D Tang
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Yuk Kei Wan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Changqing Wang
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Brandon Y Wong
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Chen Yang
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - If Barnes
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Andrew E Berry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | | | - Alyssa Cousineau
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Namrita Dhillon
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Luis Ferrández-Peral
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | - Natàlia Garcia-Reyero
- Energy, Installations & Environment, Office of the Assistant Secretary of Defense, Washington, DC, USA
| | | | | | | | | | | | | | - Jorge Mestre-Tomás
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Nedka G Panayotova
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Alejandro Paniagua
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | | | - Xingjie Ren
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Eric Rouchka
- Department of Biochemistry & Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Brandon Saint-John
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Enrique Sapena
- European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Leon Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - Melissa Laird Smith
- Department of Biochemistry & Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Marie-Marthe Suner
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Hazuki Takahashi
- Center for Integrative Medical Sciences, Laboratory for Transcriptome Technology, RIKEN, Yokohama, Japan
| | | | - Piero Carninci
- Center for Integrative Medical Sciences, Laboratory for Transcriptome Technology, RIKEN, Yokohama, Japan
- Human Technopole, Milano, Italy
| | - Nancy D Denslow
- Department of Physiological Sciences, College of Veterinary Medicine, Gainesville, FL, USA
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, University of Florida, Gainesville, FL, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Margaret E Hunter
- US Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Rene Maehr
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Yin Shen
- Institute for Human Genetics, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Hagen U Tilgner
- Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York City, NY, USA
| | - Barbara J Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Christopher Vollmers
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK.
| | - Kin Fai Au
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- UVA Cancer Center, University of Virginia, Charlottesville, VA, USA.
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA.
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain.
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA.
| | - Angela N Brooks
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
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6
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Gustavsson EK, Sethi S, Gao Y, Brenton JW, García-Ruiz S, Zhang D, Garza R, Reynolds RH, Evans JR, Chen Z, Grant-Peters M, Macpherson H, Montgomery K, Dore R, Wernick AI, Arber C, Wray S, Gandhi S, Esselborn J, Blauwendraat C, Douse CH, Adami A, Atacho DAM, Kouli A, Quaegebeur A, Barker RA, Englund E, Platt F, Jakobsson J, Wood NW, Houlden H, Saini H, Bento CF, Hardy J, Ryten M. The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1. SCIENCE ADVANCES 2024; 10:eadk1296. [PMID: 38924406 PMCID: PMC11204300 DOI: 10.1126/sciadv.adk1296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Mutations in GBA1 cause Gaucher disease and are the most important genetic risk factor for Parkinson's disease. However, analysis of transcription at this locus is complicated by its highly homologous pseudogene, GBAP1. We show that >50% of short RNA-sequencing reads mapping to GBA1 also map to GBAP1. Thus, we used long-read RNA sequencing in the human brain, which allowed us to accurately quantify expression from both GBA1 and GBAP1. We discovered significant differences in expression compared to short-read data and identify currently unannotated transcripts of both GBA1 and GBAP1. These included protein-coding transcripts from both genes that were translated in human brain, but without the known lysosomal function-yet accounting for almost a third of transcription. Analyzing brain-specific cell types using long-read and single-nucleus RNA sequencing revealed region-specific variations in transcript expression. Overall, these findings suggest nonlysosomal roles for GBA1 and GBAP1 with implications for our understanding of the role of GBA1 in health and disease.
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Affiliation(s)
- Emil K. Gustavsson
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Siddharth Sethi
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, UK
| | - Yujing Gao
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, UK
| | - Jonathan W. Brenton
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Sonia García-Ruiz
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - David Zhang
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Raquel Garza
- Laboratory of Molecular Neurogenetics, Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund, Sweden
| | - Regina H. Reynolds
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - James R. Evans
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- The Francis Crick Institute, London, UK
| | - Zhongbo Chen
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Hannah Macpherson
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kylie Montgomery
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rhys Dore
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Anna I. Wernick
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- The Francis Crick Institute, London, UK
| | - Charles Arber
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Selina Wray
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sonia Gandhi
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- The Francis Crick Institute, London, UK
| | - Julian Esselborn
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, UK
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Christopher H. Douse
- Laboratory of Epigenetics and Chromatin Dynamics, Department of Experimental Medical Science, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Anita Adami
- Laboratory of Molecular Neurogenetics, Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund, Sweden
| | - Diahann A. M. Atacho
- Laboratory of Molecular Neurogenetics, Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund, Sweden
| | - Antonina Kouli
- Wellcome-MRC Cambridge Stem Cell Institute and John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Annelies Quaegebeur
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Clinical Neurosciences, University of Cambridge, Clifford Albutt Building, Cambridge, UK
| | - Roger A. Barker
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Wellcome-MRC Cambridge Stem Cell Institute and John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Frances Platt
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - Johan Jakobsson
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Laboratory of Molecular Neurogenetics, Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund, Sweden
| | - Nicholas W. Wood
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Henry Houlden
- Department of Neuromuscular Disease, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Harpreet Saini
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, UK
| | - Carla F. Bento
- Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge, UK
| | - John Hardy
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, UCL, London, UK
- UK Dementia Research Institute at UCL, UCL Queen Square Institute of Neurology, UCL, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
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7
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Mandros P, Gallagher I, Fanfani V, Chen C, Fischer J, Ismail A, Hsu L, Saha E, DeConti DK, Quackenbush J. node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.16.599201. [PMID: 38948759 PMCID: PMC11212899 DOI: 10.1101/2024.06.16.599201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.
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Affiliation(s)
- Panagiotis Mandros
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ian Gallagher
- School of Mathematics, University of Bristol, UK, and the Heilbronn Institute for Mathematical Research, Bristol, UK
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Chen Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jonas Fischer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anis Ismail
- Faculty of Bioscience Engineering, KU Leuven, Belgium
| | - Lauren Hsu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Derrick K DeConti
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
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8
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Carmen-Orozco RP, Tsao W, Ye Y, Sinha IR, Chang K, Trinh VT, Chung W, Bowden K, Troncoso JC, Blackshaw S, Hayes LR, Sun S, Wong PC, Ling JP. Elevated nuclear TDP-43 induces constitutive exon skipping. Mol Neurodegener 2024; 19:45. [PMID: 38853250 PMCID: PMC11163724 DOI: 10.1186/s13024-024-00732-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/20/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND Cytoplasmic inclusions and loss of nuclear TDP-43 are key pathological features found in several neurodegenerative disorders, suggesting both gain- and loss-of-function mechanisms of disease. To study gain-of-function, TDP-43 overexpression has been used to generate in vitro and in vivo model systems. METHODS We analyzed RNA-seq datasets from mouse and human neurons overexpressing TDP-43 to explore species specific splicing patterns. We explored the dynamics between TDP-43 levels and exon repression in vitro. Furthermore we analyzed human brain samples and publicly available RNA datasets to explore the relationship between exon repression and disease. RESULTS Our study shows that excessive levels of nuclear TDP-43 protein lead to constitutive exon skipping that is largely species-specific. Furthermore, while aberrant exon skipping is detected in some human brains, it is not correlated with disease, unlike the incorporation of cryptic exons that occurs after loss of TDP-43. CONCLUSIONS Our findings emphasize the need for caution in interpreting TDP-43 overexpression data and stress the importance of controlling for exon skipping when generating models of TDP-43 proteinopathy.
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Affiliation(s)
- Rogger P Carmen-Orozco
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - William Tsao
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Yingzhi Ye
- Department of Physiology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Irika R Sinha
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Koping Chang
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Vickie T Trinh
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - William Chung
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Kyra Bowden
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Juan C Troncoso
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Seth Blackshaw
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Lindsey R Hayes
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Shuying Sun
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Physiology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Philip C Wong
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Jonathan P Ling
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
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9
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Iwahashi N, Umakoshi H, Fujita M, Fukumoto T, Ogasawara T, Yokomoto-Umakoshi M, Kaneko H, Nakao H, Kawamura N, Uchida N, Matsuda Y, Sakamoto R, Seki M, Suzuki Y, Nakatani K, Izumi Y, Bamba T, Oda Y, Ogawa Y. Single-cell and spatial transcriptomics analysis of human adrenal aging. Mol Metab 2024; 84:101954. [PMID: 38718896 PMCID: PMC11101872 DOI: 10.1016/j.molmet.2024.101954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/30/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVE The human adrenal cortex comprises three functionally and structurally distinct layers that produce layer-specific steroid hormones. With aging, the human adrenal cortex undergoes functional and structural alteration or "adrenal aging", leading to the unbalanced production of steroid hormones. Given the marked species differences in adrenal biology, the underlying mechanisms of human adrenal aging have not been sufficiently studied. This study was designed to elucidate the mechanisms linking the functional and structural alterations of the human adrenal cortex. METHODS We conducted single-cell RNA sequencing and spatial transcriptomics analysis of the aged human adrenal cortex. RESULTS The data of this study suggest that the layer-specific alterations of multiple signaling pathways underlie the abnormal layered structure and layer-specific changes in steroidogenic cells. We also highlighted that macrophages mediate age-related adrenocortical cell inflammation and senescence. CONCLUSIONS This study is the first detailed analysis of the aged human adrenal cortex at single-cell resolution and helps to elucidate the mechanism of human adrenal aging, thereby leading to a better understanding of the pathophysiology of age-related disorders associated with adrenal aging.
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Affiliation(s)
- Norifusa Iwahashi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hironobu Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Masamichi Fujita
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tazuru Fukumoto
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tatsuki Ogasawara
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Maki Yokomoto-Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroki Kaneko
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroshi Nakao
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Namiko Kawamura
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naohiro Uchida
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yayoi Matsuda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Ryuichi Sakamoto
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Kohta Nakatani
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yoshihiro Izumi
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Takeshi Bamba
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yoshinao Oda
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
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10
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Zhang S, Heil BJ, Mao W, Chikina M, Greene CS, Heller EA. MousiPLIER: A Mouse Pathway-Level Information Extractor Model. eNeuro 2024; 11:ENEURO.0313-23.2024. [PMID: 38789274 PMCID: PMC11154669 DOI: 10.1523/eneuro.0313-23.2024] [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: 08/23/2023] [Revised: 05/10/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024] Open
Abstract
High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.
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Affiliation(s)
- Shuo Zhang
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Benjamin J Heil
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Weiguang Mao
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, Colorado 80045
| | - Elizabeth A Heller
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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11
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Chen C, Padi M. Flexible modeling of regulatory networks improves transcription factor activity estimation. NPJ Syst Biol Appl 2024; 10:58. [PMID: 38806476 PMCID: PMC11133322 DOI: 10.1038/s41540-024-00386-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024] Open
Abstract
Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.
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Affiliation(s)
- Chen Chen
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, USA
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA
| | - Megha Padi
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA.
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
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12
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Yee SW, Ferrández-Peral L, Alentorn-Moron P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Wolfreys FD, Yang J, Gestwicki JE, Capra JA, Artursson P, Newman JW, Marquès-Bonet T, Giacomini KM. Illuminating the function of the orphan transporter, SLC22A10, in humans and other primates. Nat Commun 2024; 15:4380. [PMID: 38782905 PMCID: PMC11116522 DOI: 10.1038/s41467-024-48569-7] [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: 08/14/2023] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
SLC22A10 is an orphan transporter with unknown substrates and function. The goal of this study is to elucidate its substrate specificity and functional characteristics. In contrast to orthologs from great apes, human SLC22A10, tagged with green fluorescent protein, is not expressed on the plasma membrane. Cells expressing great ape SLC22A10 orthologs exhibit significant accumulation of estradiol-17β-glucuronide, unlike those expressing human SLC22A10. Sequence alignments reveal a proline at position 220 in humans, which is a leucine in great apes. Replacing proline with leucine in SLC22A10-P220L restores plasma membrane localization and uptake function. Neanderthal and Denisovan genomes show proline at position 220, akin to modern humans, indicating functional loss during hominin evolution. Human SLC22A10 is a unitary pseudogene due to a fixed missense mutation, P220, while in great apes, its orthologs transport sex steroid conjugates. Characterizing SLC22A10 across species sheds light on its biological role, influencing organism development and steroid homeostasis.
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Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Luis Ferrández-Peral
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Pol Alentorn-Moron
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
- Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352, Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Megan L Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Niklas Handin
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Virginia M Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA, 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394, Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, US
| | - Colin M Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Finn D Wolfreys
- Department of Ophthalmology, University of California, San Francisco, CA, USA
| | - Jia Yang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Jason E Gestwicki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, USA
| | - John A Capra
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
- Science for Life Laboratories, Uppsala University, Uppsala, Sweden
| | - John W Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA, 95616, USA
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Tomàs Marquès-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain
- CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
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13
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Fisher JL, Wilk EJ, Oza VH, Gary SE, Howton TC, Flanary VL, Clark AD, Hjelmeland AB, Lasseigne BN. Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio 2024; 14:803-830. [PMID: 38531616 PMCID: PMC11073506 DOI: 10.1002/2211-5463.13796] [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: 02/11/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Vishal H. Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Sam E. Gary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Anita B. Hjelmeland
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of MedicineThe University of Alabama at BirminghamALUSA
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14
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Fukumoto T, Umakoshi H, Iwahashi N, Ogasawara T, Yokomoto-Umakoshi M, Kaneko H, Fujita M, Uchida N, Nakao H, Kawamura N, Matsuda Y, Sakamoto R, Miyazawa T, Seki M, Eto M, Oda Y, Suzuki Y, Ogawa S, Ogawa Y. Steroids-producing nodules: a two-layered adrenocortical nodular structure as a precursor lesion of cortisol-producing adenoma. EBioMedicine 2024; 103:105087. [PMID: 38570222 PMCID: PMC11121169 DOI: 10.1016/j.ebiom.2024.105087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND The human adrenal cortex consists of three functionally and structurally distinct layers; zona glomerulosa, zona fasciculata (zF), and zona reticularis (zR), and produces adrenal steroid hormones in a layer-specific manner; aldosterone, cortisol, and adrenal androgens, respectively. Cortisol-producing adenomas (CPAs) occur mostly as a result of somatic mutations associated with the protein kinase A pathway. However, how CPAs develop after adrenocortical cells acquire genetic mutations, remains poorly understood. METHODS We conducted integrated approaches combining the detailed histopathologic studies with genetic, RNA-sequencing, and spatially resolved transcriptome (SRT) analyses for the adrenal cortices adjacent to human adrenocortical tumours. FINDINGS Histopathological analysis revealed an adrenocortical nodular structure that exhibits the two-layered zF- and zR-like structure. The nodular structures harbour GNAS somatic mutations, known as a driver mutation of CPAs, and confer cell proliferative and autonomous steroidogenic capacities, which we termed steroids-producing nodules (SPNs). RNA-sequencing coupled with SRT analysis suggests that the expansion of the zF-like structure contributes to the formation of CPAs, whereas the zR-like structure is characterised by a macrophage-mediated immune response. INTERPRETATION We postulate that CPAs arise from a precursor lesion, SPNs, where two distinct cell populations might contribute differently to adrenocortical tumorigenesis. Our data also provide clues to the molecular mechanisms underlying the layered structures of human adrenocortical tissues. FUNDING KAKENHI, The Uehara Memorial Foundation, Daiwa Securities Health Foundation, Kaibara Morikazu Medical Science Promotion Foundation, Secom Science and Technology Foundation, ONO Medical Research Foundation, and Japan Foundation for Applied Enzymology.
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Affiliation(s)
- Tazuru Fukumoto
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hironobu Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Norifusa Iwahashi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tatsuki Ogasawara
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Maki Yokomoto-Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroki Kaneko
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masamichi Fujita
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Naohiro Uchida
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroshi Nakao
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Namiko Kawamura
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yayoi Matsuda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Ryuichi Sakamoto
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takashi Miyazawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Masatoshi Eto
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshinao Oda
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
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15
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Sportelli L, Eisenberg DP, Passiatore R, D'Ambrosio E, Antonucci LA, Bettina JS, Chen Q, Goldman AL, Gregory MD, Griffiths K, Hyde TM, Kleinman JE, Pardiñas AF, Parihar M, Popolizio T, Rampino A, Shin JH, Veronese M, Ulrich WS, Zink CF, Bertolino A, Howes OD, Berman KF, Weinberger DR, Pergola G. Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo. Nat Commun 2024; 15:3342. [PMID: 38688917 PMCID: PMC11061310 DOI: 10.1038/s41467-024-47456-5] [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: 09/04/2023] [Accepted: 03/22/2024] [Indexed: 05/02/2024] Open
Abstract
The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.
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Affiliation(s)
- Leonardo Sportelli
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Daniel P Eisenberg
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Roberta Passiatore
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Enrico D'Ambrosio
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Linda A Antonucci
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Jasmine S Bettina
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Aaron L Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Michael D Gregory
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Kira Griffiths
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
- Holmusk Technologies, New York, NY, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Teresa Popolizio
- Radiology Department, IRCCS Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Mattia Veronese
- Department of Information Engineering, University of Padua, Padua, Italy
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - William S Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Caroline F Zink
- Baltimore Research and Education Foundation, Baltimore, MD, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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16
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Fischer S, Lichtenthaeler C, Stepanenko A, Heyl F, Maticzka D, Kemmerer K, Klostermann M, Backofen R, Zarnack K, Weigand JE. Heterogenous nuclear ribonucleoprotein D-like controls endothelial cell functions. Biol Chem 2024; 405:229-239. [PMID: 37942876 DOI: 10.1515/hsz-2023-0254] [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: 07/05/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023]
Abstract
HnRNPs are ubiquitously expressed RNA-binding proteins, tightly controlling posttranscriptional gene regulation. Consequently, hnRNP networks are essential for cellular homeostasis and their dysregulation is associated with cancer and other diseases. However, the physiological function of hnRNPs in non-cancerous cell systems are poorly understood. We analyzed the importance of HNRNPDL in endothelial cell functions. Knockdown of HNRNPDL led to impaired proliferation, migration and sprouting of spheroids. Transcriptome analysis identified cyclin D1 (CCND1) and tropomyosin 4 (TPM4) as targets of HNRNPDL, reflecting the phenotypic changes after knockdown. Our findings underline the importance of HNRNPDL for the homeostasis of physiological processes in endothelial cells.
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Affiliation(s)
- Sandra Fischer
- Department of Pharmacy, Institute of Pharmaceutical Chemistry, University of Marburg, Marbacher Weg 6, D-35037 Marburg, Germany
| | - Chiara Lichtenthaeler
- Department of Pharmacy, Institute of Pharmaceutical Chemistry, University of Marburg, Marbacher Weg 6, D-35037 Marburg, Germany
| | - Anastasiya Stepanenko
- Buchmann Institute for Molecular Life Sciences and Institute of Molecular Biosciences, Goethe University Frankfurt, Max-von-Laue-Str. 15, D-60438 Frankfurt am Main, Germany
| | - Florian Heyl
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Daniel Maticzka
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Katrin Kemmerer
- Department of Pharmacy, Institute of Pharmaceutical Chemistry, University of Marburg, Marbacher Weg 6, D-35037 Marburg, Germany
| | - Melina Klostermann
- Buchmann Institute for Molecular Life Sciences and Institute of Molecular Biosciences, Goethe University Frankfurt, Max-von-Laue-Str. 15, D-60438 Frankfurt am Main, Germany
| | - Rolf Backofen
- Department of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany
| | - Kathi Zarnack
- Buchmann Institute for Molecular Life Sciences and Institute of Molecular Biosciences, Goethe University Frankfurt, Max-von-Laue-Str. 15, D-60438 Frankfurt am Main, Germany
| | - Julia E Weigand
- Department of Pharmacy, Institute of Pharmaceutical Chemistry, University of Marburg, Marbacher Weg 6, D-35037 Marburg, Germany
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17
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Roller A, Davydov II, Schwalie PC, Serrano-Serrano ML, Heller A, Staedler N, Ferreira CS, Dietmann G, Klaman I, Valdeolivas A, Korski K, Cannarile MA. Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+T cells in the tumor microenvironment and predicts clinical outcome in early-phase and late-phase clinical trials. J Immunother Cancer 2024; 12:e008185. [PMID: 38649280 PMCID: PMC11043740 DOI: 10.1136/jitc-2023-008185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND The immune status of a patient's tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients' immune status is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms. METHODS We systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I-III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+tumor-infiltrating lymphocytes in the TME. RESULTS The CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve inflamed=0.846; excluded=0.712; desert=0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (HR 0.88; 95% CI 0.80 to 0.97) across TCGA, and longer OS on immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI 0.58 to 0.97). CONCLUSIONS We provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy.
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Affiliation(s)
- Andreas Roller
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Iakov I Davydov
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Petra C Schwalie
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Martha L Serrano-Serrano
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Astrid Heller
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, Roche Diagnostics GmbH, Munich, Germany
| | - Nicolas Staedler
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Cláudia S Ferreira
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, Roche Diagnostics GmbH, Munich, Germany
| | - Gabriele Dietmann
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, Roche Diagnostics GmbH, Munich, Germany
| | - Irina Klaman
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, Roche Diagnostics GmbH, Munich, Germany
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Konstanty Korski
- Roche Product Development, PHC Data, Analytics and Imaging, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Michael A Cannarile
- Roche Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center, Roche Diagnostics GmbH, Munich, Germany
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18
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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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19
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Marino GB, Clarke DJB, Deng EZ, Ma’ayan A. RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588712. [PMID: 38645198 PMCID: PMC11030343 DOI: 10.1101/2024.04.09.588712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.
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Affiliation(s)
- Giacomo B. Marino
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York 10029, NY USA
| | - Daniel J. B. Clarke
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York 10029, NY USA
| | - Eden Z. Deng
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York 10029, NY USA
| | - Avi Ma’ayan
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York 10029, NY USA
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20
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Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
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Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
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21
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Sinha IR, Sandal PS, Burns GD, Mallika AP, Irwin KE, Cruz ALF, Wang V, Rodríguez JL, Wong PC, Ling JP. Large-scale RNA-seq mining reveals ciclopirox triggers TDP-43 cryptic exons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587011. [PMID: 38585725 PMCID: PMC10996699 DOI: 10.1101/2024.03.27.587011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Nuclear clearance and cytoplasmic aggregation of TDP-43 in neurons, initially identified in ALS-FTD, are hallmark pathological features observed across a spectrum of neurodegenerative diseases. We previously found that TDP-43 loss-of-function leads to the transcriptome-wide inclusion of deleterious cryptic exons in brains and biofluids post-mortem as well as during the presymptomatic stage of ALS-FTD, but upstream mechanisms that lead to TDP-43 dysregulation remain unclear. Here, we developed a web-based resource (SnapMine) to determine the levels of TDP-43 cryptic exon inclusion across hundreds of thousands of publicly available RNA sequencing datasets. We established cryptic exon inclusion across a variety of human cells and tissues to provide ground truth references for future studies on TDP-43 dysregulation. We then explored studies that were entirely unrelated to TDP-43 or neurodegeneration and found that ciclopirox olamine (CPX), an FDA-approved antifungal, can trigger the inclusion of TDP-43-associated cryptic exons in a variety of mouse and human primary cells. CPX induction of cryptic exon occurs via heavy metal toxicity and oxidative stress, suggesting that similar vulnerabilities could play a role in neurodegeneration. Our work demonstrates how diverse datasets can be linked through common biological features and underscores that public archives of sequencing data represent a vastly underutilized resource with tremendous potential for uncovering novel insights into complex biological mechanisms and diseases.
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Affiliation(s)
- Irika R Sinha
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Parker S Sandal
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Grace D Burns
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | | | - Katherine E Irwin
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Anna Lourdes F Cruz
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Vania Wang
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | - Philip C Wong
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Jonathan P Ling
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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22
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Legouis D, Rinaldi A, Malpetti D, Arnoux G, Verissimo T, Faivre A, Mangili F, Rinaldi A, Ruinelli L, Pugin J, Moll S, Clivio L, Bolis M, de Seigneux S, Azzimonti L, Cippà PE. A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease. iScience 2024; 27:109271. [PMID: 38487013 PMCID: PMC10937833 DOI: 10.1016/j.isci.2024.109271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/26/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
The application of single-cell technologies in clinical nephrology remains elusive. We generated an atlas of transcriptionally defined cell types and cell states of human kidney disease by integrating single-cell signatures reported in the literature with newly generated signatures obtained from 5 patients with acute kidney injury. We used this information to develop kidney-specific cell-level information ExtractoR (K-CLIER), a transfer learning approach specifically tailored to evaluate the role of cell types/states on bulk RNAseq data. We validated the K-CLIER as a reliable computational framework to obtain a dimensionality reduction and to link clinical data with single-cell signatures. By applying K-CLIER on cohorts of patients with different kidney diseases, we identified the most relevant cell types associated with fibrosis and disease progression. This analysis highlighted the central role of altered proximal tubule cells in chronic kidney disease. Our study introduces a new strategy to exploit the power of single-cell technologies toward clinical applications.
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Affiliation(s)
- David Legouis
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Anna Rinaldi
- Laboratories for Translational Research, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Daniele Malpetti
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Gregoire Arnoux
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Thomas Verissimo
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
| | - Anna Faivre
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
- Division of Nephrology, Department of Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Francesca Mangili
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Andrea Rinaldi
- Institute of Oncological Research, 6500 Bellinzona, Switzerland
| | | | - Jerome Pugin
- Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Solange Moll
- Division of Pathology, Department of Diagnostic, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Luca Clivio
- Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Marco Bolis
- Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland
- Laboratory of Computational Oncology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
| | - Sophie de Seigneux
- Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital and University of Geneva, 1205 Geneva, Switzerland
- Division of Nephrology, Department of Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Laura Azzimonti
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Pietro E. Cippà
- Laboratories for Translational Research, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
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23
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Razi A, Lo CC, Wang S, Leek JT, Hansen KD. Genotype prediction of 336,463 samples from public expression data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.21.562237. [PMID: 38559266 PMCID: PMC10979922 DOI: 10.1101/2023.10.21.562237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Tens of thousands of RNA-sequencing experiments comprising hundreds of thousands of individual samples have now been performed. These data represent a broad range of experimental conditions, sequencing technologies, and hypotheses under study. The Recount project has aggregated and uniformly processed hundreds of thousands of publicly available RNA-seq samples. Most of these samples only include RNA expression measurements; genotype data for these same samples would enable a wide range of analyses including variant prioritization, eQTL analysis, and studies of allele specific expression. Here, we developed a statistical model based on the existing reference and alternative read counts from the RNA-seq experiments available through Recount3 to predict genotypes at autosomal biallelic loci in coding regions. We demonstrate the accuracy of our model using large-scale studies that measured both gene expression and genotype genome-wide. We show that our predictive model is highly accurate with 99.5% overall accuracy, 99.6% major allele accuracy, and 90.4% minor allele accuracy. Our model is robust to tissue and study effects, provided the coverage is high enough. We applied this model to genotype all the samples in Recount 3 and provide the largest ready-to-use expression repository containing genotype information. We illustrate that the predicted genotype from RNA-seq data is sufficient to unravel the underlying population structure of samples in Recount3 using Principal Component Analysis.
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Affiliation(s)
- Afrooz Razi
- Department of Genetic Medicine, Johns Hopkins University School of Medicine
| | - Christopher C. Lo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
| | - Siruo Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
| | - Jeffrey T. Leek
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center
| | - Kasper D. Hansen
- Department of Genetic Medicine, Johns Hopkins University School of Medicine
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine
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24
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Veneti Z, Fasoulaki V, Kalavros N, Vlachos IS, Delidakis C, Eliopoulos AG. Polycomb-mediated silencing of miR-8 is required for maintenance of intestinal stemness in Drosophila melanogaster. Nat Commun 2024; 15:1924. [PMID: 38429303 PMCID: PMC10907375 DOI: 10.1038/s41467-024-46119-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 02/15/2024] [Indexed: 03/03/2024] Open
Abstract
Balancing maintenance of self-renewal and differentiation is a key property of adult stem cells. The epigenetic mechanisms controlling this balance remain largely unknown. Herein, we report that the Polycomb Repressive Complex 2 (PRC2) is required for maintenance of the intestinal stem cell (ISC) pool in the adult female Drosophila melanogaster. We show that loss of PRC2 activity in ISCs by RNAi-mediated knockdown or genetic ablation of the enzymatic subunit Enhancer of zeste, E(z), results in loss of stemness and precocious differentiation of enteroblasts to enterocytes. Mechanistically, we have identified the microRNA miR-8 as a critical target of E(z)/PRC2-mediated tri-methylation of histone H3 at Lys27 (H3K27me3) and uncovered a dynamic relationship between E(z), miR-8 and Notch signaling in controlling stemness versus differentiation of ISCs. Collectively, these findings uncover a hitherto unrecognized epigenetic layer in the regulation of stem cell specification that safeguards intestinal homeostasis.
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Affiliation(s)
- Zoe Veneti
- Institute of Molecular Biology and Biotechnology, Foundation of Research & Technology Hellas, Heraklion, Greece.
- Medical School, University of Crete, Heraklion, Greece.
| | - Virginia Fasoulaki
- Institute of Molecular Biology and Biotechnology, Foundation of Research & Technology Hellas, Heraklion, Greece
- Department of Biology, University of Crete, Heraklion, Greece
| | - Nikolaos Kalavros
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ioannis S Vlachos
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Christos Delidakis
- Institute of Molecular Biology and Biotechnology, Foundation of Research & Technology Hellas, Heraklion, Greece
- Department of Biology, University of Crete, Heraklion, Greece
| | - Aristides G Eliopoulos
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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25
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Franceschini GM, Quaini O, Mizuno K, Orlando F, Ciani Y, Ku SY, Sigouros M, Rothmann E, Alonso A, Benelli M, Nardella C, Auh J, Freeman D, Hanratty B, Adil M, Elemento O, Tagawa ST, Feng FY, Caffo O, Buttigliero C, Basso U, Nelson PS, Corey E, Haffner MC, Attard G, Aparicio A, Demichelis F, Beltran H. Noninvasive Detection of Neuroendocrine Prostate Cancer through Targeted Cell-free DNA Methylation. Cancer Discov 2024; 14:424-445. [PMID: 38197680 PMCID: PMC10905672 DOI: 10.1158/2159-8290.cd-23-0754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024]
Abstract
Castration-resistant prostate cancer (CRPC) is a heterogeneous disease associated with phenotypic subtypes that drive therapy response and outcome differences. Histologic transformation to castration-resistant neuroendocrine prostate cancer (CRPC-NE) is associated with distinct epigenetic alterations, including changes in DNA methylation. The current diagnosis of CRPC-NE is challenging and relies on metastatic biopsy. We developed a targeted DNA methylation assay to detect CRPC-NE using plasma cell-free DNA (cfDNA). The assay quantifies tumor content and provides a phenotype evidence score that captures diverse CRPC phenotypes, leveraging regions to inform transcriptional state. We tested the design in independent clinical cohorts (n = 222 plasma samples) and qualified it achieving an AUC > 0.93 for detecting pathology-confirmed CRPC-NE (n = 136). Methylation-defined cfDNA tumor content was associated with clinical outcomes in two prospective phase II clinical trials geared towards aggressive variant CRPC and CRPC-NE. These data support the application of targeted DNA methylation for CRPC-NE detection and patient stratification. SIGNIFICANCE Neuroendocrine prostate cancer is an aggressive subtype of treatment-resistant prostate cancer. Early detection is important, but the diagnosis currently relies on metastatic biopsy. We describe the development and validation of a plasma cell-free DNA targeted methylation panel that can quantify tumor fraction and identify patients with neuroendocrine prostate cancer noninvasively. This article is featured in Selected Articles from This Issue, p. 384.
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Affiliation(s)
- Gian Marco Franceschini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Orsetta Quaini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Kei Mizuno
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Francesco Orlando
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Sheng-Yu Ku
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Michael Sigouros
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Emily Rothmann
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Alicia Alonso
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | | | - Caterina Nardella
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Joonghoon Auh
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Dory Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Brian Hanratty
- Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mohamed Adil
- Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Olivier Elemento
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Scott T. Tagawa
- Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York
| | - Felix Y. Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Orazio Caffo
- Department of Medical Oncology, Santa Chiara Hospital, Trento, Italy
| | - Consuelo Buttigliero
- Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy
| | - Umberto Basso
- Department of Oncology, Istituto Oncologico Veneto IOV - IRCCS, Padua, Italy
| | | | - Eva Corey
- University of Washington, Seattle, Washington
| | - Michael C. Haffner
- Fred Hutchinson Cancer Research Center, Seattle, Washington
- University of Washington, Seattle, Washington
| | - Gerhardt Attard
- Cancer Institute and University College London Hospitals, University College London, London, United Kingdom
| | - Ana Aparicio
- Department of GU Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Himisha Beltran
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
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26
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Dalfovo D, Scandino R, Paoli M, Valentini S, Romanel A. Germline determinants of aberrant signaling pathways in cancer. NPJ Precis Oncol 2024; 8:57. [PMID: 38429380 PMCID: PMC10907629 DOI: 10.1038/s41698-024-00546-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024] Open
Abstract
Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.
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Affiliation(s)
- Davide Dalfovo
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy
| | - Riccardo Scandino
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy
| | - Marta Paoli
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy
| | - Samuel Valentini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy
| | - Alessandro Romanel
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy.
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27
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Ilık İA, Glažar P, Tse K, Brändl B, Meierhofer D, Müller FJ, Smith ZD, Aktaş T. Autonomous transposons tune their sequences to ensure somatic suppression. Nature 2024; 626:1116-1124. [PMID: 38355802 PMCID: PMC10901741 DOI: 10.1038/s41586-024-07081-0] [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: 01/24/2023] [Accepted: 01/16/2024] [Indexed: 02/16/2024]
Abstract
Transposable elements (TEs) are a major constituent of human genes, occupying approximately half of the intronic space. During pre-messenger RNA synthesis, intronic TEs are transcribed along with their host genes but rarely contribute to the final mRNA product because they are spliced out together with the intron and rapidly degraded. Paradoxically, TEs are an abundant source of RNA-processing signals through which they can create new introns1, and also functional2 or non-functional chimeric transcripts3. The rarity of these events implies the existence of a resilient splicing code that is able to suppress TE exonization without compromising host pre-mRNA processing. Here we show that SAFB proteins protect genome integrity by preventing retrotransposition of L1 elements while maintaining splicing integrity, via prevention of the exonization of previously integrated TEs. This unique dual role is possible because of L1's conserved adenosine-rich coding sequences that are bound by SAFB proteins. The suppressive activity of SAFB extends to tissue-specific, giant protein-coding cassette exons, nested genes and Tigger DNA transposons. Moreover, SAFB also suppresses LTR/ERV elements in species in which they are still active, such as mice and flies. A significant subset of splicing events suppressed by SAFB in somatic cells are activated in the testis, coinciding with low SAFB expression in postmeiotic spermatids. Reminiscent of the division of labour between innate and adaptive immune systems that fight external pathogens, our results uncover SAFB proteins as an RNA-based, pattern-guided, non-adaptive defence system against TEs in the soma, complementing the RNA-based, adaptive Piwi-interacting RNA pathway of the germline.
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Affiliation(s)
- İbrahim Avşar Ilık
- Otto Warburg Laboratories, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Petar Glažar
- Otto Warburg Laboratories, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Kevin Tse
- Department of Genetics, Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
| | - Björn Brändl
- Universitätsklinikum Schleswig-Holstein Campus Kiel, Zentrum für Integrative Psychiatrie, Kiel, Germany
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - David Meierhofer
- Mass Spectrometry Joint Facilities Scientific Service, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Franz-Josef Müller
- Universitätsklinikum Schleswig-Holstein Campus Kiel, Zentrum für Integrative Psychiatrie, Kiel, Germany
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Zachary D Smith
- Department of Genetics, Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
| | - Tuğçe Aktaş
- Otto Warburg Laboratories, Max Planck Institute for Molecular Genetics, Berlin, Germany.
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28
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Meimetis N, Pullen KM, Zhu DY, Nilsson A, Hoang TN, Magliacane S, Lauffenburger DA. AutoTransOP: translating omics signatures without orthologue requirements using deep learning. NPJ Syst Biol Appl 2024; 10:13. [PMID: 38287079 PMCID: PMC10825146 DOI: 10.1038/s41540-024-00341-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024] Open
Abstract
The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Krista M Pullen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Daniel Y Zhu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE, 41296, Sweden
| | - Trong Nghia Hoang
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164-236, USA
| | - Sara Magliacane
- Institute of Informatics, University of Amsterdam, Amsterdam, The Netherlands
- MIT-IBM Watson AI Lab, Cambridge, MA, 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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Ravichandran P, Parsana P, Keener R, Hansen KD, Battle A. Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576447. [PMID: 38328080 PMCID: PMC10849507 DOI: 10.1101/2024.01.20.576447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Background Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. Results We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. Conclusion This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.
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Affiliation(s)
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kaspar D Hansen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA
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Hou D, Lin H, Feng Y, Zhou K, Li X, Yang Y, Wang S, Yang X, Wang J, Zhao H, Zhang X, Fan J, Lu S, Wang D, Zhu L, Ju D, Chen YZ, Zeng X. CMAUP database update 2024: extended functional and association information of useful plants for biomedical research. Nucleic Acids Res 2024; 52:D1508-D1518. [PMID: 37897343 PMCID: PMC10767869 DOI: 10.1093/nar/gkad921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/23/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023] Open
Abstract
Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.
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Affiliation(s)
- Dongyue Hou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hanbo Lin
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuhan Feng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Kaicheng Zhou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xingxiu Li
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuan Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Shuaiqi Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xue Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiayu Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hui Zhao
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xuyao Zhang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiajun Fan
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - SongLin Lu
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Dan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Lyuhan Zhu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Dianwen Ju
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yu Zong Chen
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
- Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Xian Zeng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
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31
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Quesnel-Vallières M, Jewell S, Lynch KW, Thomas-Tikhonenko A, Barash Y. MAJIQlopedia: an encyclopedia of RNA splicing variations in human tissues and cancer. Nucleic Acids Res 2024; 52:D213-D221. [PMID: 37953365 PMCID: PMC10767883 DOI: 10.1093/nar/gkad1043] [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: 08/15/2023] [Revised: 10/11/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
Quantification of RNA splicing variations based on RNA-Sequencing can reveal tissue- and disease-specific splicing patterns. To study such splicing variations, we introduce MAJIQlopedia, an encyclopedia of splicing variations that encompasses 86 human tissues and 41 cancer datasets. MAJIQlopedia reports annotated and unannotated splicing events for a total of 486 175 alternative splice junctions in normal tissues and 338 317 alternative splice junctions in cancer. This database, available at https://majiq.biociphers.org/majiqlopedia/, includes a user-friendly interface that provides graphical representations of junction usage quantification for each junction across all tissue or cancer types. To demonstrate case usage of MAJIQlopedia, we review splicing variations in genes WT1, MAPT and BIN1, which all have known tissue or cancer-specific splicing variations. We also use MAJIQlopedia to highlight novel splicing variations in FDX1 and MEGF9 in normal tissues, and we uncover a novel exon inclusion event in RPS6KA6 that only occurs in two cancer types. Users can download the database, request the addition of data to the webtool, or install a MAJIQlopedia server to integrate proprietary data. MAJIQlopedia can serve as a reference database for researchers seeking to understand what splicing variations exist in genes of interest, and those looking to understand tissue- or cancer-specific splice isoform usage.
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Affiliation(s)
- Mathieu Quesnel-Vallières
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - San Jewell
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristen W Lynch
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrei Thomas-Tikhonenko
- Division of Cancer Pathobiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yoseph Barash
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
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32
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Clough E, Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillippy K, Sherman P, Lee H, Zhang N, Serova N, Wagner L, Zalunin V, Kochergin A, Soboleva A. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res 2024; 52:D138-D144. [PMID: 37933855 PMCID: PMC10767856 DOI: 10.1093/nar/gkad965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data files and descriptive metadata for over 200 000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.
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Affiliation(s)
- Emily Clough
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen E Wilhite
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre Ledoux
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Carlos Evangelista
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Irene F Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Maxim Tomashevsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kimberly A Marshall
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine H Phillippy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patti M Sherman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hyeseung Lee
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Naigong Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nadezhda Serova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lukas Wagner
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vadim Zalunin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrey Kochergin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alexandra Soboleva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
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Whitlock JH, Wilk EJ, Howton TC, Clark AD, Lasseigne BN. The landscape of SETBP1 gene expression and transcription factor activity across human tissues. PLoS One 2024; 19:e0296328. [PMID: 38165902 PMCID: PMC10760659 DOI: 10.1371/journal.pone.0296328] [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: 10/26/2023] [Accepted: 12/11/2023] [Indexed: 01/04/2024] Open
Abstract
The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Variants in SETBP1 can result in three different diseases determined by the introduction (germline vs. somatic) and location of the variant. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults. To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1 and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcriptional regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs. This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.
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Affiliation(s)
- Jordan H. Whitlock
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Bosi C, Bartha Á, Galbardi B, Notini G, Naldini MM, Licata L, Viale G, Mariani M, Pistilli B, Ali HR, André F, Piras M, Callari M, Barreca M, Locatelli A, Viganò L, Criscitiello C, Pusztai L, Curigliano G, Győrffy B, Dugo M, Bianchini G. Pan-cancer analysis of antibody-drug conjugate targets and putative predictors of treatment response. Eur J Cancer 2023; 195:113379. [PMID: 37913680 DOI: 10.1016/j.ejca.2023.113379] [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: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Antibody-drug conjugates (ADCs) are a rapidly expanding class of compounds in oncology. Our goal was to assess the expression of ADC targets and potential downstream determining factors of activity across pan-cancer and normal tissues. MATERIALS AND METHODS ADCs in clinical trials (n = 121) were identified through ClinicalTrials.gov, corresponding to 54 targets. Genes potentially implicated in treatment response were identified in the literature. Gene expression from The Cancer Genome Atlas (9000+ cancers of 31 cancer types), the Genotype-Tissue Expression database (n = 19,000 samples from 31 normal tissue types), and the TNMplot.com (n = 12,494 unmatched primary and metastatic samples) were used in this analysis. To compare relative expression across and within tumour types we used pooled normal tissues as reference. RESULTS For most ADC targets, mRNA levels correlated with protein expression. Pan-cancer target expression distributions identified appealing cancer types for each ADC development. Co-expression of multiple targets was common and suggested opportunities for ADC combinations. Expression levels of genes potentially implicated in ADC response downstream of the target might provide additional information (e.g. TOP1 was highly expressed in many tumour types, including breast and lung cancers). Metastatic compared to primary tissues overexpressed some ADCs targets. Single sample "targetgram" plots were generated to visualise the expression of potentially competing ADC targets and resistance/sensitivity markers highlighting high inter-patient heterogeneity. Off-cancer target expression only partially explains adverse events, while expression of determinants of payload activity explained more of the observed toxicities. CONCLUSION Our findings draw attention to new therapeutic opportunities for ADCs that can be tested in the clinic and our web platform (https://tnmplot.com) can assist in prioritising upcoming ADC targets for clinical development.
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Affiliation(s)
- Carlo Bosi
- Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Áron Bartha
- Department of Bioinformatics, Semmelweis University, Tűzoltó Utca 7-9, 1094 Budapest, Hungary; Research Centre for Natural Sciences, Oncology Biomarker Research Group, Institute of Molecular Life Sciences, Eötvös Loránd Research Network, Magyar Tudósok Körútja 2, 1117 Budapest, Hungary
| | - Barbara Galbardi
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giulia Notini
- Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Matteo M Naldini
- Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Licata
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giulia Viale
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Marco Mariani
- Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Barbara Pistilli
- Department of Medical Oncology, Gustave Roussy Cancer Center, Villejuif, France
| | - H Raza Ali
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Histopathology, Addenbrookes Hospital, Cambridge, UK
| | - Fabrice André
- Department of Medical Oncology, Gustave Roussy Cancer Center, Villejuif, France
| | - Marta Piras
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | | | - Alberta Locatelli
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Lucia Viganò
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Carmen Criscitiello
- Division of Early Drug Development, European Institute of Oncology, IRCCS, Milano, Italy; Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy
| | - Lajos Pusztai
- Department of Internal Medicine, Section of Medical Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Giuseppe Curigliano
- Division of Early Drug Development, European Institute of Oncology, IRCCS, Milano, Italy; Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Tűzoltó Utca 7-9, 1094 Budapest, Hungary; Research Centre for Natural Sciences, Oncology Biomarker Research Group, Institute of Molecular Life Sciences, Eötvös Loránd Research Network, Magyar Tudósok Körútja 2, 1117 Budapest, Hungary
| | - Matteo Dugo
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Giampaolo Bianchini
- Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
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Tang X, Liu Z, Ren J, Cao Y, Xia S, Sun Z, Luo G. Comparative RNA-sequencing analysis of the prostate in a mouse model of benign prostatic hyperplasia with bladder outlet obstruction. Mol Cell Biochem 2023; 478:2721-2737. [PMID: 36920576 PMCID: PMC10628026 DOI: 10.1007/s11010-023-04695-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
In ageing men, benign prostatic hyperplasia (BPH) is a chronic disease that leads to progressive lower urinary tract symptoms (LUTS) caused by obstruction of the bladder outlet (BOO). Patients with LUTS (such as increased frequency and urgency of urination) and complications of BOO (such as hydronephrosis and bladder stones) are at risk of serious health problems. BPH causes a rapidly rising burden of LUTS far exceeding that of other urological conditions. Treatment outcomes are unsatisfactory for BPH largely due to the lacking of fully understanding of the pathogenesis. Hormonal imbalances related to androgen and oestrogen can cause BPH, but the exact mechanism is still unknown, even the animal model is not fully understood. Additionally, there are no large-scale data to explain this mechanism. A BPH mouse model was established using mixed slow-release pellets of testosterone (T) and estradiol (E2), and we measured gene expression in mouse prostate tissue using RNA-seq, verified the results using qRT‒PCR, and used bioinformatics methods to analyse the differentially expressed genes (DEGs).
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Affiliation(s)
- Xiaohu Tang
- Medical College, Guizhou University, Guiyang, 550025, Guizhou, China
- Department of Urology Surgery, Guizhou Province People's Hospital, Guiyang, 550002, China
| | - Zhiyan Liu
- Guizhou Medical University, GuiyangGuizhou, 550025, China
| | - Jingwen Ren
- Guizhou Medical University, GuiyangGuizhou, 550025, China
| | - Ying Cao
- Medical College, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Shujie Xia
- Department of Urology Surgery, Shanghai First People's Hospital, Shanghai Jiao Tong University, Shanghai, 201620, China
| | - Zhaolin Sun
- Medical College, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Guangheng Luo
- Department of Urology Surgery, Guizhou Province People's Hospital, Guiyang, 550002, China.
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Wong ACH, Wong JJL, Rasko JEJ, Schmitz U. SpliceWiz: interactive analysis and visualization of alternative splicing in R. Brief Bioinform 2023; 25:bbad468. [PMID: 38152981 PMCID: PMC10753292 DOI: 10.1093/bib/bbad468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/31/2023] [Accepted: 11/23/2023] [Indexed: 12/29/2023] Open
Abstract
Alternative splicing (AS) is a crucial mechanism for regulating gene expression and isoform diversity in eukaryotes. However, the analysis and visualization of AS events from RNA sequencing data remains challenging. Most tools require a certain level of computer literacy and the available means of visualizing AS events, such as coverage and sashimi plots, have limitations and can be misleading. To address these issues, we present SpliceWiz, an R package with an interactive Shiny interface that allows easy and efficient AS analysis and visualization at scale. A novel normalization algorithm is implemented to aggregate splicing levels within sample groups, thereby allowing group differences in splicing levels to be accurately visualized. The tool also offers downstream gene ontology enrichment analysis, highlighting ASEs belonging to functional pathways of interest. SpliceWiz is optimized for speed and efficiency and introduces a new file format for coverage data storage that is more efficient than BigWig. Alignment files are processed orders of magnitude faster than other R-based AS analysis tools and on par with command-line tools. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. SpliceWiz is a Bioconductor package and is also available on GitHub (https://github.com/alexchwong/SpliceWiz).
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Affiliation(s)
- Alex C H Wong
- Gene and Stem Cell Therapy Program, Centenary Institute, the University of Sydney, Camperdown, NSW 2050, Australia
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
| | - Justin J-L Wong
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
| | - John E J Rasko
- Gene and Stem Cell Therapy Program, Centenary Institute, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Ulf Schmitz
- Biomedical Sciences and Molecular Biology, James Cook University, Townsville, QLD 4810, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, QLD 4810, Australia
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Prada-Luengo I, Schuster V, Liang Y, Terkelsen T, Sora V, Krogh A. N-of-one differential gene expression without control samples using a deep generative model. Genome Biol 2023; 24:263. [PMID: 37974217 PMCID: PMC10655485 DOI: 10.1186/s13059-023-03104-7] [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: 02/09/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.
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Affiliation(s)
- Iñigo Prada-Luengo
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Viktoria Schuster
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Yuhu Liang
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thilde Terkelsen
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Valentina Sora
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Anders Krogh
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark.
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Pulver C, Grun D, Duc J, Sheppard S, Planet E, Coudray A, de Fondeville R, Pontis J, Trono D. Statistical learning quantifies transposable element-mediated cis-regulation. Genome Biol 2023; 24:258. [PMID: 37950299 PMCID: PMC10637000 DOI: 10.1186/s13059-023-03085-7] [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: 08/16/2022] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Transposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryogenesis to innate immune responses. Because of their repetitive nature, TEs have the potential to form CRE platforms enabling the coordinated and genome-wide regulation of protein-coding genes by only a handful of trans-acting transcription factors (TFs). RESULTS Here, we directly test this hypothesis through mathematical modeling and demonstrate that differences in expression at protein-coding genes alone are sufficient to estimate the magnitude and significance of TE-contributed cis-regulatory activities, even in contexts where TE-derived transcription fails to do so. We leverage hundreds of overexpression experiments and estimate that, overall, gene expression is influenced by TE-embedded CREs situated within approximately 500 kb of promoters. Focusing on the cis-regulatory potential of TEs within the gene regulatory network of human embryonic stem cells, we find that pluripotency-specific and evolutionarily young TE subfamilies can be reactivated by TFs involved in post-implantation embryogenesis. Finally, we show that TE subfamilies can be split into truly regulatorily active versus inactive fractions based on additional information such as matched epigenomic data, observing that TF binding may better predict TE cis-regulatory activity than differences in histone marks. CONCLUSION Our results suggest that TE-embedded CREs contribute to gene regulation during and beyond gastrulation. On a methodological level, we provide a statistical tool that infers TE-dependent cis-regulation from RNA-seq data alone, thus facilitating the study of TEs in the next-generation sequencing era.
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Affiliation(s)
- Cyril Pulver
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Delphine Grun
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Julien Duc
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Shaoline Sheppard
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Evarist Planet
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Alexandre Coudray
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Raphaël de Fondeville
- Swiss Data Science Center, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Julien Pontis
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
- SOPHiA GENETICS SA, La Pièce 12, CH-1180, Rolle, Switzerland.
| | - Didier Trono
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
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Collado-Torres L, Klei L, Liu C, Kleinman JE, Hyde TM, Geschwind DH, Gandal MJ, Devlin B, Weinberger DR. Comparison of gene expression in living and postmortem human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.08.23298172. [PMID: 37986747 PMCID: PMC10659492 DOI: 10.1101/2023.11.08.23298172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Molecular mechanisms of neuropsychiatric disorders are challenging to study in human brain. For decades, the preferred model has been to study postmortem human brain samples despite the limitations they entail. A recent study generated RNA sequencing data from biopsies of prefrontal cortex from living patients with Parkinson's Disease and compared gene expression to postmortem tissue samples, from which they found vast differences between the two. This led the authors to question the utility of postmortem human brain studies. Through re-analysis of the same data, we unexpectedly found that the living brain tissue samples were of much lower quality than the postmortem samples across multiple standard metrics. We also performed simulations that illustrate the effects of ignoring RNA degradation in differential gene expression analyses, showing the effects can be substantial and of similar magnitude to what the authors find. For these reasons, we believe the authors' conclusions are unjustified. To the contrary, while opportunities to study gene expression in the living brain are welcome, evidence that this eclipses the value of postmortem analyses is not apparent.
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Affiliation(s)
- Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Daniel H Geschwind
- Intellectual and Developmental Disabilities Research Center, Department of Psychiatry, Department of Human Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, Center for Autism Research and Treatment, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Michael J Gandal
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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Dam SH, Olsen LR, Vitting-Seerup K. Expression and splicing mediate distinct biological signals. BMC Biol 2023; 21:220. [PMID: 37858135 PMCID: PMC10588054 DOI: 10.1186/s12915-023-01724-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Through alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases, and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown. RESULTS To solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals. CONCLUSIONS These findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.
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Affiliation(s)
- Søren Helweg Dam
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars Rønn Olsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Kristoffer Vitting-Seerup
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
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Whitlock JH, Wilk EJ, Howton TC, Clark AD, Lasseigne BN. The landscape of SETBP1 gene expression and transcription factor activity across human tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.551337. [PMID: 37873221 PMCID: PMC10592643 DOI: 10.1101/2023.08.08.551337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Distinct SETBP1 variants have been linked to three different diseases. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults. Results To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1, and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcription regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs. Conclusions This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.
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Affiliation(s)
- Jordan H. Whitlock
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Elizabeth J. Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Timothy C. Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Amanda D. Clark
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine The University of Alabama at Birmingham, Birmingham, AL, U.S.A
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Abdill RJ, Graham SP, Rubinetti V, Albert FW, Greene CS, Davis S, Blekhman R. Integration of 168,000 samples reveals global patterns of the human gut microbiome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.11.560955. [PMID: 37873416 PMCID: PMC10592789 DOI: 10.1101/2023.10.11.560955] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Understanding the factors that shape variation in the human microbiome is a major goal of research in biology. While other genomics fields have used large, pre-compiled compendia to extract systematic insights requiring otherwise impractical sample sizes, there has been no comparable resource for the 16S rRNA sequencing data commonly used to quantify microbiome composition. To help close this gap, we have assembled a set of 168,484 publicly available human gut microbiome samples, processed with a single pipeline and combined into the largest unified microbiome dataset to date. We use this resource, which is freely available at microbiomap.org, to shed light on global variation in the human gut microbiome. We find that Firmicutes, particularly Bacilli and Clostridia, are almost universally present in the human gut. At the same time, the relative abundance of the 65 most common microbial genera differ between at least two world regions. We also show that gut microbiomes in undersampled world regions, such as Central and Southern Asia, differ significantly from the more thoroughly characterized microbiomes of Europe and Northern America. Moreover, humans in these overlooked regions likely harbor hundreds of taxa that have not yet been discovered due to this undersampling, highlighting the need for diversity in microbiome studies. We anticipate that this new compendium can serve the community and enable advanced applied and methodological research.
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Affiliation(s)
- Richard J. Abdill
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Samantha P. Graham
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, USA
| | - Vincent Rubinetti
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health Artificial Intelligence (CHAI), University of Colorado School of Medicine, Aurora, CO, USA
| | - Frank W. Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, USA
| | - Casey S. Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health Artificial Intelligence (CHAI), University of Colorado School of Medicine, Aurora, CO, USA
| | - Sean Davis
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health Artificial Intelligence (CHAI), University of Colorado School of Medicine, Aurora, CO, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA
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Payá-Milans M, Peña-Chilet M, Loucera C, Esteban-Medina M, Dopazo J. Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models. Int J Mol Sci 2023; 24:14732. [PMID: 37834179 PMCID: PMC10572617 DOI: 10.3390/ijms241914732] [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: 08/10/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.
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Affiliation(s)
- Miriam Payá-Milans
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Carlos Loucera
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Marina Esteban-Medina
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
| | - Joaquín Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain; (M.P.-M.); (M.P.-C.); (C.L.); (M.E.-M.)
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, 41013 Seville, Spain
- Institute of Biomedicine of Seville, IBiS/University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Sevilla, Spain
- FPS/ELIXIR-ES, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocío, 41013 Sevilla, Spain
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Saha E, Guebila MB, Fanfani V, Fischer J, Shutta KH, Mandros P, DeMeo DL, Quackenbush J, Lopes-Ramos CM. Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559001. [PMID: 37790409 PMCID: PMC10543009 DOI: 10.1101/2023.09.22.559001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.
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Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jonas Fischer
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
| | - Panagiotis Mandros
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA 02115
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
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Sportelli L, Eisenberg DP, Passiatore R, D'Ambrosio E, Antonucci LA, Chen Q, Czarapata J, Goldman AL, Gregory M, Griffiths K, Hyde TM, Kleinman JE, Pardiñas AF, Parihar M, Popolizio T, Rampino A, Shin JH, Veronese M, Ulrich WS, Zink CF, Bertolino A, Howes OD, Berman KF, Weinberger DR, Pergola G. Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558594. [PMID: 37786720 PMCID: PMC10541621 DOI: 10.1101/2023.09.20.558594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [ 18 F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a 'bench to bedside' translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.
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47
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Nile M, Folwaczny M, Wichelhaus A, Baumert U, Janjic Rankovic M. Fluid flow shear stress and tissue remodeling-an orthodontic perspective: evidence synthesis and differential gene expression network analysis. Front Bioeng Biotechnol 2023; 11:1256825. [PMID: 37795174 PMCID: PMC10545883 DOI: 10.3389/fbioe.2023.1256825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
Introduction: This study aimed to identify and analyze in vitro studies investigating the biological effect of fluid-flow shear stress (FSS) on cells found in the periodontal ligament and bone tissue. Method: We followed the PRISMA guideline for systematic reviews. A PubMed search strategy was developed, studies were selected according to predefined eligibility criteria, and the risk of bias was assessed. Relevant data related to cell source, applied FSS, and locus-specific expression were extracted. Based on this evidence synthesis and, as an original part of this work, analysis of differential gene expression using over-representation and network-analysis was performed. Five relevant publicly available gene expression datasets were analyzed using gene set enrichment analysis (GSEA). Result: A total of 6,974 articles were identified. Titles and abstracts were screened, and 218 articles were selected for full-text assessment. Finally, 120 articles were included in this study. Sample size determination and statistical analysis related to methodological quality and the ethical statement item in reporting quality were most frequently identified as high risk of bias. The analyzed studies mostly used custom-made fluid-flow apparatuses (61.7%). FSS was most frequently applied for 0.5 h, 1 h, or 2 h, whereas FSS magnitudes ranged from 6 to 20 dyn/cm2 depending on cell type and flow profile. Fluid-flow frequencies of 1 Hz in human cells and 1 and 5 Hz in mouse cells were mostly applied. FSS upregulated genes/metabolites responsible for tissue formation (AKT1, alkaline phosphatase, BGLAP, BMP2, Ca2+, COL1A1, CTNNB1, GJA1, MAPK1/MAPK3, PDPN, RUNX2, SPP1, TNFRSF11B, VEGFA, WNT3A) and inflammation (nitric oxide, PGE-2, PGI-2, PTGS1, PTGS2). Protein-protein interaction networks were constructed and analyzed using over-representation analysis and GSEA to identify shared signaling pathways. Conclusion: To our knowledge, this is the first review giving a comprehensive overview and discussion of methodological technical details regarding fluid flow application in 2D cell culture in vitro experimental conditions. Therefore, it is not only providing valuable information about cellular molecular events and their quantitative and qualitative analysis, but also confirming the reproducibility of previously published results.
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Affiliation(s)
- Mustafa Nile
- Department of Orthodontics and Dentofacial Orthopedics, LMU University Hospital, LMU Munich, Munich, Germany
| | - Matthias Folwaczny
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Andrea Wichelhaus
- Department of Orthodontics and Dentofacial Orthopedics, LMU University Hospital, LMU Munich, Munich, Germany
| | - Uwe Baumert
- Department of Orthodontics and Dentofacial Orthopedics, LMU University Hospital, LMU Munich, Munich, Germany
| | - Mila Janjic Rankovic
- Department of Orthodontics and Dentofacial Orthopedics, LMU University Hospital, LMU Munich, Munich, Germany
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48
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Yee SW, Ferrández-Peral L, Alentorn P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Capra JA, Artursson P, Newman JW, Marques-Bonet T, Giacomini KM. Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates. RESEARCH SQUARE 2023:rs.3.rs-3263845. [PMID: 37790518 PMCID: PMC10543398 DOI: 10.21203/rs.3.rs-3263845/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17β-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.
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Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | | | - Pol Alentorn
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352 Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Niklas Handin
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Virginia M. Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, CA 94143, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States
| | - Colin M. Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - John W. Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA; Department of Nutrition, University of California, Davis, Davis, CA 95616, USA; UC Davis West Coast Metabolomics Center, Davis, CA 95616, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain; CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain; Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
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49
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Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis 2023; 46:824-838. [PMID: 37553850 DOI: 10.1002/jimd.12663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023]
Abstract
Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.
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Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Nikita Konstantinovskiy
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
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50
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Yee SW, Ferrández-Peral L, Alentorn P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Capra JA, Artursson P, Newman JW, Marques-Bonet T, Giacomini KM. Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552553. [PMID: 37609337 PMCID: PMC10441401 DOI: 10.1101/2023.08.08.552553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17β-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.
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Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | | | - Pol Alentorn
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352 Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Niklas Handin
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Virginia M. Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, CA 94143, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States
| | - Colin M. Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - John W. Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA; Department of Nutrition, University of California, Davis, Davis, CA 95616, USA; UC Davis West Coast Metabolomics Center, Davis, CA 95616, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain; CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain; Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
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