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Xiong G, LeRoy NJ, Bekiranov S, Sheffield NC, Zhang A. DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data. Bioinformatics 2024; 40:btae434. [PMID: 38950178 PMCID: PMC11236288 DOI: 10.1093/bioinformatics/btae434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/28/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024] Open
Abstract
MOTIVATION Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges. RESULTS In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. AVAILABILITY AND IMPLEMENTATION https://github.com/Teddy-XiongGZ/DeepGSEA.
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Affiliation(s)
- Guangzhi Xiong
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, United States
| | - Nathan J LeRoy
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22904, United States
| | - Stefan Bekiranov
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, United States
| | - Nathan C Sheffield
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22904, United States
| | - Aidong Zhang
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, United States
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2
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Paquette A, Ahuna K, Hwang YM, Pearl J, Liao H, Shannon P, Kadam L, Lapehn S, Bucher M, Roper R, Funk C, MacDonald J, Bammler T, Baloni P, Brockway H, Mason WA, Bush N, Lewinn KZ, Karr CJ, Stamatoyannopoulos J, Muglia LJ, Jones H, Sadovsky Y, Myatt L, Sathyanarayana S, Price ND. A genome scale transcriptional regulatory model of the human placenta. SCIENCE ADVANCES 2024; 10:eadf3411. [PMID: 38941464 PMCID: PMC11212735 DOI: 10.1126/sciadv.adf3411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/28/2024] [Indexed: 06/30/2024]
Abstract
Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.
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Affiliation(s)
- Alison Paquette
- University of Washington, Seattle, WA, USA
- Seattle Children’s Research Institute, Seattle, WA, USA
| | - Kylia Ahuna
- Oregon Health and Sciences University, Portland, OR, USA
| | | | | | - Hanna Liao
- University of Washington, Seattle, WA, USA
| | | | - Leena Kadam
- Oregon Health and Sciences University, Portland, OR, USA
| | | | - Matthew Bucher
- Oregon Health and Sciences University, Portland, OR, USA
| | - Ryan Roper
- Institute for Systems Biology, Seattle, WA, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | - Heather Brockway
- Department of Physiology and Aging, University of Florida, Gainesville, FL, USA
| | - W. Alex Mason
- University of Tennessee Health Sciences Center, Memphis, TN, USA
| | - Nicole Bush
- University of California San Francisco, San Francisco, CA, USA
| | - Kaja Z. Lewinn
- University of California San Francisco, San Francisco, CA, USA
| | | | | | - Louis J. Muglia
- The Burroughs Wellcome Fund, Research Triangle Park, NC, USA
- Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Yoel Sadovsky
- Magee Womens Research Institute, Pittsburgh, PA, USA
- University of Pittsburgh, Pittsburgh, PA, USA
| | - Leslie Myatt
- Oregon Health and Sciences University, Portland, OR, USA
| | - Sheela Sathyanarayana
- University of Washington, Seattle, WA, USA
- Seattle Children’s Research Institute, Seattle, WA, USA
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York City, NY, USA
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3
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He S, Gubin MM, Rafei H, Basar R, Dede M, Jiang X, Liang Q, Tan Y, Kim K, Gillison ML, Rezvani K, Peng W, Haymaker C, Hernandez S, Solis LM, Mohanty V, Chen K. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles. iScience 2024; 27:110096. [PMID: 38957791 PMCID: PMC11217617 DOI: 10.1016/j.isci.2024.110096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/03/2024] [Accepted: 05/21/2024] [Indexed: 07/04/2024] Open
Abstract
Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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Affiliation(s)
- Shan He
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Matthew M. Gubin
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hind Rafei
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rafet Basar
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xianli Jiang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Qingnan Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kunhee Kim
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maura L. Gillison
- Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Weiyi Peng
- Department of Biology and Biochemistry, The University of Houston, Houston, TX, USA
| | - Cara Haymaker
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharia Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luisa M. Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Koopmans F. GOAT: efficient and robust identification of gene set enrichment. Commun Biol 2024; 7:744. [PMID: 38898151 PMCID: PMC11187187 DOI: 10.1038/s42003-024-06454-5] [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: 02/07/2024] [Accepted: 06/14/2024] [Indexed: 06/21/2024] Open
Abstract
Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene sets within a list of gene effect sizes that represent experimental outcomes is an everyday task in life science that crucially depends on robust and sensitive statistical tools. We here present GOAT, a parameter-free algorithm for gene set enrichment analysis of preranked gene lists. The algorithm can precompute null distributions from standardized gene scores, enabling enrichment testing of the GO database in one second. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene list length and gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT identifies more significant GO terms as compared to current methods. GOAT is freely available as an R package and user-friendly online tool for gene set enrichment analyses that includes interactive data visualizations: https://ftwkoopmans.github.io/goat .
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Affiliation(s)
- Frank Koopmans
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, 1081 HV, Amsterdam, The Netherlands.
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5
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Aronson SL, Walker C, Thijssen B, van de Vijver KK, Horlings HM, Sanders J, Alkemade M, Koole SN, Lopez-Yurda M, Lok CAR, Rottenberg S, van Rheenen J, Sonke GS, van Driel WJ, Kester LA, Hahn K. Tumour microenvironment characterisation to stratify patients for hyperthermic intraperitoneal chemotherapy in high-grade serous ovarian cancer (OVHIPEC-1). Br J Cancer 2024:10.1038/s41416-024-02731-6. [PMID: 38866963 DOI: 10.1038/s41416-024-02731-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Hyperthermic intraperitoneal chemotherapy (HIPEC) improves survival in patients with Stage III ovarian cancer following interval cytoreductive surgery (CRS). Optimising patient selection is essential to maximise treatment efficacy and avoid overtreatment. This study aimed to identify biomarkers that predict HIPEC benefit by analysing gene signatures and cellular composition of tumours from participants in the OVHIPEC-1 trial. METHODS Whole-transcriptome RNA sequencing data were retrieved from high-grade serous ovarian cancer (HGSOC) samples from 147 patients obtained during interval CRS. We performed differential gene expression analysis and applied deconvolution methods to estimate cell-type proportions in bulk mRNA data, validated by histological assessment. We tested the interaction between treatment and potential predictors on progression-free survival using Cox proportional hazards models. RESULTS While differential gene expression analysis did not yield any predictive biomarkers, the cellular composition, as characterised by deconvolution, indicated that the absence of macrophages and the presence of B cells in the tumour microenvironment are potential predictors of HIPEC benefit. The histological assessment confirmed the predictive value of macrophage absence. CONCLUSION Immune cell composition, in particular macrophages absence, may predict response to HIPEC in HGSOC and these hypothesis-generating findings warrant further investigation. CLINICAL TRIAL REGISTRATION NCT00426257.
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Affiliation(s)
- S Lot Aronson
- Center for Gynecologic Oncology Amsterdam, Department of Gynecologic Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Cédric Walker
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Bram Thijssen
- Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Koen K van de Vijver
- Center for Gynecologic Oncology Amsterdam, Department of Gynecologic Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Pathology & Cancer Research Institute Ghent (CRIG), Ghent University Hospital, Ghent, Belgium
| | - Hugo M Horlings
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joyce Sanders
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Maartje Alkemade
- Core Facility Molecular Pathology and Biobanking, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Simone N Koole
- Center for Gynecologic Oncology Amsterdam, Department of Gynecologic Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Marta Lopez-Yurda
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Christianne A R Lok
- Center for Gynecologic Oncology Amsterdam, Department of Gynecologic Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sven Rottenberg
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, Bern, Switzerland
| | - Jacco van Rheenen
- Department of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Willemien J van Driel
- Center for Gynecologic Oncology Amsterdam, Department of Gynecologic Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Lennart A Kester
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Kerstin Hahn
- Department of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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6
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Parodis I, Lindblom J, Barturen G, Ortega-Castro R, Cervera R, Pers JO, Genre F, Hiepe F, Gerosa M, Kovács L, De Langhe E, Piantoni S, Stummvoll G, Vasconcelos C, Vigone B, Witte T, Alarcón-Riquelme ME, Beretta L. Molecular characterisation of lupus low disease activity state (LLDAS) and DORIS remission by whole-blood transcriptome-based pathways in a pan-European systemic lupus erythematosus cohort. Ann Rheum Dis 2024; 83:889-900. [PMID: 38373843 PMCID: PMC11187369 DOI: 10.1136/ard-2023-224795] [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: 07/30/2023] [Accepted: 02/05/2024] [Indexed: 02/21/2024]
Abstract
OBJECTIVES To unveil biological milieus underlying low disease activity (LDA) and remission versus active systemic lupus erythematosus (SLE). METHODS We determined differentially expressed pathways (DEPs) in SLE patients from the PRECISESADS project (NTC02890121) stratified into patients fulfilling and not fulfilling the criteria of (1) Lupus LDA State (LLDAS), (2) Definitions of Remission in SLE remission, and (3) LLDAS exclusive of remission. RESULTS We analysed data from 321 patients; 40.8% were in LLDAS, and 17.4% in DORIS remission. After exclusion of patients in remission, 28.3% were in LLDAS. Overall, 604 pathways differed significantly in LLDAS versus non-LLDAS patients with an false-discovery rate-corrected p (q)<0.05 and a robust effect size (dr)≥0.36. Accordingly, 288 pathways differed significantly between DORIS remitters and non-remitters (q<0.05 and dr≥0.36). DEPs yielded distinct molecular clusters characterised by differential serological, musculoskeletal, and renal activity. Analysis of partially overlapping samples showed no DEPs between LLDAS and DORIS remission. Drug repurposing potentiality for treating SLE was unveiled, as were important pathways underlying active SLE whose modulation could aid attainment of LLDAS/remission, including toll-like receptor (TLR) cascades, Bruton tyrosine kinase (BTK) activity, the cytotoxic T lymphocyte antigen 4 (CTLA-4)-related inhibitory signalling, and the nucleotide-binding oligomerization domain leucine-rich repeat-containing protein 3 (NLRP3) inflammasome pathway. CONCLUSIONS We demonstrated for the first time molecular signalling pathways distinguishing LLDAS/remission from active SLE. LLDAS/remission was associated with reversal of biological processes related to SLE pathogenesis and specific clinical manifestations. DEP clustering by remission better grouped patients compared with LLDAS, substantiating remission as the ultimate treatment goal in SLE; however, the lack of substantial pathway differentiation between the two states justifies LLDAS as an acceptable goal from a biological perspective.
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Affiliation(s)
- Ioannis Parodis
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
- Department of Rheumatology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Julius Lindblom
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Guillermo Barturen
- GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada / Andalusian Regional Government, Granada, Spain, Medical Genomics, Granada, Spain
- Department of Genetics, Faculty of Sciences, University of Granada, Granada, Spain
| | | | - Ricard Cervera
- Department of Autoimmune Diseases, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Catalonia, Spain
| | - Jacques-Olivier Pers
- Centre Hospitalier Universitaire de Brest, Hopital de la Cavale Blanche, Brest, France
| | - Fernanda Genre
- Research Group on Genetic Epidemiology and Atherosclerosis in Systemic Diseases and in Metabolic Bone Diseases of the Musculoskeletal System, IDIVAL, Santander, Spain
| | - Falk Hiepe
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Ellen De Langhe
- Katholieke Universiteit Leuven and Universitair Ziekenhuis Leuven, Leuven, Belgium
| | - Silvia Piantoni
- Rheumatology and Clinical Immunology Unit, Department of Clinical and Experimental Sciences, Azienda Socio Sanitaria Territoriale Spedali Civili and University of Brescia, Brescia, Italy
| | | | | | - Barbara Vigone
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Marta E Alarcón-Riquelme
- GENYO, Centre for Genomics and Oncological Research: Pfizer, University of Granada / Andalusian Regional Government, Granada, Spain, Medical Genomics, Granada, Spain
- Department of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Lorenzo Beretta
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Cadavid JL, Li NT, McGuigan AP. Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease. BIOPHYSICS REVIEWS 2024; 5:021301. [PMID: 38617201 PMCID: PMC11008916 DOI: 10.1063/5.0179125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.
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8
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Curvello R, Berndt N, Hauser S, Loessner D. Recreating metabolic interactions of the tumour microenvironment. Trends Endocrinol Metab 2024; 35:518-532. [PMID: 38212233 DOI: 10.1016/j.tem.2023.12.005] [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: 10/19/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 01/13/2024]
Abstract
Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.
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Affiliation(s)
- Rodrigo Curvello
- Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia
| | - Nikolaus Berndt
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany; Institute of Computer-assisted Cardiovascular Medicine, Deutsches Herzzentrum der Charité, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sandra Hauser
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Department of Radiopharmaceutical and Chemical Biology, Dresden, Germany
| | - Daniela Loessner
- Department of Chemical and Biological Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia; Leibniz Institute of Polymer Research Dresden e.V., Max Bergmann Center of Biomaterials, Dresden, Germany; Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia; Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Science, Monash University, Melbourne, Victoria, Australia.
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Cao Z, Zhan G, Qin J, Cupertino RB, Ottino-Gonzalez J, Murphy A, Pancholi D, Hahn S, Yuan D, Callas P, Mackey S, Garavan H. Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics. Neuroimage 2024; 293:120622. [PMID: 38648869 PMCID: PMC11132826 DOI: 10.1016/j.neuroimage.2024.120622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.
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Affiliation(s)
- Zhipeng Cao
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China; Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA.
| | - Guilai Zhan
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China
| | - Jinmei Qin
- Shanghai Xuhui Mental Health Center, Shanghai 200232, China
| | - Renata B Cupertino
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Jonatan Ottino-Gonzalez
- Division of Endocrinology, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Alistair Murphy
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Devarshi Pancholi
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Sage Hahn
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Dekang Yuan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Peter Callas
- Department of Mathematics and Statistics, University of Vermont College of Engineering and Mathematical Sciences, Burlington VT, 05401, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA
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Weinstein SM, Vandekar SN, Li B, Alexander‐Bloch AF, Raznahan A, Li M, Gur RE, Gur RC, Roalf DR, Park MTM, Chakravarty M, Baller EB, Linn KA, Satterthwaite TD, Shinohara RT. Network enrichment significance testing in brain-phenotype association studies. Hum Brain Mapp 2024; 45:e26714. [PMID: 38878300 PMCID: PMC11179683 DOI: 10.1002/hbm.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/08/2024] [Accepted: 05/04/2024] [Indexed: 06/19/2024] Open
Abstract
Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and BiostatisticsTemple University College of Public HealthPhiladelphiaPennsylvaniaUSA
| | - Simon N. Vandekar
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Bin Li
- Department of Computer and Information SciencesTemple University College of Science and TechnologyPhiladelphiaPennsylvaniaUSA
| | - Aaron F. Alexander‐Bloch
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
- Department of Child and Adolescent Psychiatry and Behavioral ScienceChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMarylandUSA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Integrated Program in NeuroscienceMcGill UniversityQCCanada
| | - Mallar Chakravarty
- Department of PsychiatryMcGill UniversityQCCanada
- Cerebral Imaging Centre, Douglas Research Centre, McGill UniversityQCCanada
| | - Erica B. Baller
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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11
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Candia J, Ferrucci L. Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks. PLoS One 2024; 19:e0302696. [PMID: 38753612 PMCID: PMC11098418 DOI: 10.1371/journal.pone.0302696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.
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Affiliation(s)
- Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
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12
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He S, Gubin MM, Rafei H, Basar R, Dede M, Jiang X, Liang Q, Tan Y, Kim K, Gillison ML, Rezvani K, Peng W, Haymaker C, Hernandez S, Solis LM, Mohanty V, Chen K. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593433. [PMID: 38798470 PMCID: PMC11118452 DOI: 10.1101/2024.05.10.593433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.
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Lin Q, Cai B, Ke R, Chen L, Ni X, Liu H, Lin X, Wang B, Shan X. Integrative bioinformatics and experimental validation of hub genetic markers in acne vulgaris: Toward personalized diagnostic and therapeutic strategies. J Cosmet Dermatol 2024; 23:1777-1799. [PMID: 38268224 DOI: 10.1111/jocd.16152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/10/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Acne vulgaris is a widespread chronic inflammatory dermatological condition. The precise molecular and genetic mechanisms of its pathogenesis remain incompletely understood. This research synthesizes existing databases, targeting a comprehensive exploration of core genetic markers. METHODS Gene expression datasets (GSE6475, GSE108110, and GSE53795) were retrieved from the GEO. Differentially expressed genes (DEGs) were identified using the limma package. Enrichment analyses were conducted using GSVA for pathway assessment and clusterProfiler for GO and KEGG analyses. PPI networks and immune cell infiltration were analyzed using the STRING database and ssGSEA, respectively. We investigated the correlation between hub gene biomarkers and immune cell infiltration using Spearman's rank analysis. ROC curve analysis validated the hub genes' diagnostic accuracy. miRNet, TarBase v8.0, and ChEA3 identified miRNA/transcription factor-gene interactions, while DrugBank delineated drug-gene interactions. Experiments utilized HaCaT cells stimulated with Propionibacterium acnes, treated with retinoic acid and methotrexate, and evaluated using RT-qPCR, ELISA, western blot, lentiviral transduction, CCK-8, wound-healing, and transwell assays. RESULTS There were 104 genes with consistent differences across the three datasets of paired acne and normal skin. Functional analyses emphasized the significant enrichment of these DEGs in immune-related pathways. PPI network analysis pinpointed hub genes PTPRC, CXCL8, ITGB2, and MMP9 as central players in acne pathogenesis. Elevated levels of specific immune cell infiltration in acne lesions corroborated the inflammatory nature of the disease. ROC curve analysis identified the acne diagnostic potential of four hub genes. Key miRNAs, particularly hsa-mir-124-3p, and central transcription factors like TFEC were noted as significant regulators. In vitro validation using HaCaT cells confirmed the upregulation of hub genes following Propionibacterium acnes exposure, while CXCL8 knockdown reduced pro-inflammatory cytokines, cell proliferation, and migration. DrugBank insights led to the exploration of retinoic acid and methotrexate, both of which mitigated gene expression upsurge and inflammatory mediator secretion. CONCLUSION This comprehensive study elucidated pivotal genes associated with acne pathogenesis, notably PTPRC, CXCL8, ITGB2, and MMP9. The findings underscore potential biomarkers, therapeutic targets, and the therapeutic potential of agents like retinoic acid and methotrexate. The congruence between bioinformatics and experimental validations suggests promising avenues for personalized acne treatments.
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Affiliation(s)
- Qian Lin
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute for Translational Medicine, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Beichen Cai
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute for Translational Medicine, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Ruonan Ke
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, Fujian, China
| | - Lu Chen
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Xuejun Ni
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Hekun Liu
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute for Translational Medicine, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Xinjian Lin
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, Fujian, China
| | - Biao Wang
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute for Translational Medicine, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiuying Shan
- Department of Plastic Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Plastic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
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14
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Swindell WR. Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords. Front Genet 2024; 15:1385114. [PMID: 38689650 PMCID: PMC11059082 DOI: 10.3389/fgene.2024.1385114] [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: 02/11/2024] [Accepted: 04/03/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs). Methods This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified. Results The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037). Conclusion This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.
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Affiliation(s)
- William R. Swindell
- Department of Internal Medicine, Division of Hospital Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
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15
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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [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: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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16
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Mueller FB, Yang H, Li C, Dadhania DM, Xiang JZ, Salvatore S, Seshan SV, Sharma VK, Suthanthiran M, Muthukumar T. RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection. Transplantation 2024; 108:911-922. [PMID: 38291584 PMCID: PMC10963156 DOI: 10.1097/tp.0000000000004896] [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] [Indexed: 02/01/2024]
Abstract
BACKGROUND Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management. METHODS We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways. RESULTS T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR. CONCLUSIONS Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.
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Affiliation(s)
- Franco B. Mueller
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Hua Yang
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Carol Li
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Darshana M. Dadhania
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
| | - Jenny Z. Xiang
- Genomics Resources Core Facility, Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY
| | - Steven Salvatore
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY
| | - Surya V. Seshan
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY
| | - Vijay K. Sharma
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Manikkam Suthanthiran
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
| | - Thangamani Muthukumar
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
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Cho SB. Comorbidity Genes of Alzheimer's Disease and Type 2 Diabetes Associated with Memory and Cognitive Function. Int J Mol Sci 2024; 25:2211. [PMID: 38396891 PMCID: PMC10889845 DOI: 10.3390/ijms25042211] [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: 01/02/2024] [Revised: 02/02/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction network identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed and relevant evidence summarized. Gene information will be useful in the discovery of biomarkers and the identification of tentative therapeutic targets for AD and T2DM.
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Affiliation(s)
- Seong Beom Cho
- Department of Biomedical Informatics, College of Medicine, Gachon University, 38-13, Dokgeom-ro 3 Street, Namdon-gu, Incheon 21565, Republic of Korea
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18
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Chang LY, Lee MZ, Wu Y, Lee WK, Ma CL, Chang JM, Chen CW, Huang TC, Lee CH, Lee JC, Tseng YY, Lin CY. Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles. Nucleic Acids Res 2024; 52:e17. [PMID: 38096046 PMCID: PMC10853793 DOI: 10.1093/nar/gkad1187] [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: 01/17/2022] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 02/10/2024] Open
Abstract
Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.
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Affiliation(s)
- Lan-Yun Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Meng-Zhan Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Yujia Wu
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Wen-Kai Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chia-Liang Ma
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Jun-Mao Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Ciao-Wen Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzu-Chun Huang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chia-Hwa Lee
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, New Taipei City 235, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, New Taipei City 235, Taiwan
| | - Jih-Chin Lee
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 110, Taiwan
| | - Yu-Yao Tseng
- Department of Food Science, Nutrition, and Nutraceutical Biotechnology, Shih Chien University, Taipei 104, Taiwan
| | - Chun-Yu Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Cancer and Immunology Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- School of Dentistry, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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19
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Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, Hassan R, Mahdin H, Sen SC. Robustness evaluations of pathway activity inference methods on gene expression data. BMC Bioinformatics 2024; 25:23. [PMID: 38216898 PMCID: PMC10785356 DOI: 10.1186/s12859-024-05632-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: 07/15/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.
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Affiliation(s)
- Tay Xin Hui
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Shahreen Kasim
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia.
| | - Izzatdin Abdul Aziz
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Mohd Farhan Md Fudzee
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Nazleeni Samiha Haron
- Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Malaysia
| | - Tole Sutikno
- Department of Electrical Engineering, Universitas Ahmad Dahlan (UAD), 55166, Yogyakarta, Indonesia
| | - Rohayanti Hassan
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
| | - Hairulnizam Mahdin
- Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), 83000, Batu Pahat, Malaysia
| | - Seah Choon Sen
- Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia
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Ulhaq ZS, Boncan DAT, Chan TF, Tse WKF. Insights from metabolomics and transcriptomics studies on Perfluorohexanesulfonic acid (PFHxS) exposed zebrafish embryos. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166833. [PMID: 37673246 DOI: 10.1016/j.scitotenv.2023.166833] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/22/2023] [Accepted: 09/02/2023] [Indexed: 09/08/2023]
Abstract
Perfluorohexanesulfonic acid (PFHxS) is a short-chain perfluoroalkyl substance widely used to replace the banned perfluorooctanesulfonic acid (PFOS) in various industrial and household products. It can be found in the environment and human bodies; however, its potential toxicities are not well studied. Zebrafish have been extensively used as a model for studying toxicants, and currently, two studies have reported on the toxicity of PFHxS in zebrafish from different approaches. Ulhaq and Tse (J Hazard Mater. 2023; 457: 131722) conducted general biological experiments and applied transcriptomics to demonstrate that PFHxS at a concentration of 5 μM could affect glucose and fatty acid metabolism, leading to oxidative stress, developmental defects, and cell cycle arrest. Xu et al. (Sci Total Environ. 2023; 887: 163770) employed metabolomics and showed that concentrations of various metabolites changed after exposure to 3 and 10 μM PFHxS. As we observed a match between the metabolomics data and our biochemistry experimental findings, we integrated the two studies, which enabled us to unfold the possible mechanism of the deregulated metabolites. We identified 22 differential expressed genes (DEGs) in the tricarboxylic acid (TCA) cycle, 17 DEGs in glcyolytic process, including the critical glucokinase under the carbon metabolism. Besides, genes likes aldehyde dehydrogenases, and histone-lysine N-methyltransferases that participate in lipid peroxidation and amino metabolism respectively were spotted. Lastly, we further strengthen our discoveries by undergoing the gene set enrichment analysis. This article could provide insights into the toxicity of PFHxS, as well as prospects for environmental studies.
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Affiliation(s)
- Zulvikar Syambani Ulhaq
- Laboratory of Developmental Disorders and Toxicology, Center for Promotion of International Education and Research, Faculty of Agriculture, Kyushu University, Fukuoka 8190395, Japan; Research Center for Pre-clinical and Clinical Medicine, National Research and Innovation Agency, Republic of Indonesia, Cibinong 16911, Indonesia
| | - Delbert Almerick T Boncan
- School of Life Sciences, State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong
| | - Ting Fung Chan
- School of Life Sciences, State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong
| | - William Ka Fai Tse
- Laboratory of Developmental Disorders and Toxicology, Center for Promotion of International Education and Research, Faculty of Agriculture, Kyushu University, Fukuoka 8190395, Japan.
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Ang MY, Takeuchi F, Kato N. Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets. J Hum Genet 2023; 68:823-833. [PMID: 37620670 PMCID: PMC10678330 DOI: 10.1038/s10038-023-01189-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/15/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVES Genome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. However, identifying the causal genes, pathways, and tissues/cell types responsible for these associations remains a challenge, and standardized analysis workflows are lacking. Additionally, due to limited treatment options for obesity, there is a need for the development of new pharmacological therapies. This study aimed to address these issues by performing step-wise utilization of knowledgebase for gene prioritization and assessing the potential relevance of key obesity genes as therapeutic targets. METHODS AND RESULTS First, we generated a list of 28,787 obesity-associated SNPs from the publicly available GWAS dataset (approximately 800,000 individuals in the GIANT meta-analysis). Then, we prioritized 1372 genes with significant in silico evidence against genomic and transcriptomic data, including transcriptionally regulated genes in the brain from transcriptome-wide association studies. In further narrowing down the gene list, we selected key genes, which we found to be useful for the discovery of potential drug seeds as demonstrated in lipid GWAS separately. We thus identified 74 key genes for obesity, which are highly interconnected and enriched in several biological processes that contribute to obesity, including energy expenditure and homeostasis. Of 74 key genes, 37 had not been reported for the pathophysiology of obesity. Finally, by drug-gene interaction analysis, we detected 23 (of 74) key genes that are potential targets for 78 approved and marketed drugs. CONCLUSIONS Our results provide valuable insights into new treatment options for obesity through a data-driven approach that integrates multiple up-to-date knowledgebases.
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Affiliation(s)
- Mia Yang Ang
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Norihiro Kato
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Gene Diagnostics and Therapeutics, Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
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22
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Bellocchi C, Wang X, Lyons MA, Marchini M, Lorini M, Carbonelli V, Montano N, Assassi S, Beretta L. Reactome pathway analysis from whole-blood transcriptome reveals unique characteristics of systemic sclerosis patients at the preclinical stage. Front Immunol 2023; 14:1266391. [PMID: 38022564 PMCID: PMC10654742 DOI: 10.3389/fimmu.2023.1266391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This study aims to characterize differential expressed pathways (DEP) in subjects with preclinical systemic sclerosis (PreSSc) characterized uniquely by Raynaud phenomenon, specific autoantibodies, and/or capillaroscopy positive for scleroderma pattern. Methods Whole-blood samples from 33 PreSSc with clinical prospective data (baseline and after 4 years of follow-up) and 16 matched healthy controls (HC) were analyzed for global gene expression transcriptome analysis via RNA sequencing. Functional Analysis of Individual Microarray Expression method annotated Reactome individualized pathways. ANOVA analysis identified DEP whose predictive capability were tested in logistic regression models after extensive internal validation. Results At 4 years, 42.4% subjects progressed (evolving PreSSc), while the others kept stable PreSSc clinical features (stable PreSSc). At baseline, out of 831 pathways, 541 DEP were significant at a false discovery rate <0.05, differentiating PreSSc versus HC with an AUROC = 0.792 ± 0.242 in regression models. Four clinical groups were identified via unsupervised clustering (HC, HC and PreSSc with HC-like features, PreSSc and HC with PreSSc-like features, and PreSSc). Biological signatures changed with disease progression while remaining unchanged in stable subjects. The magnitude of change was related to the baseline cluster, yet no DEP at baseline was predictive of progression. Disease progression was mostly related to changes in signal transduction pathways especially linked to calcium-related events and inositol 1,4,5-triphosphate metabolism. Conclusion PreSSc had distinguished Reactome pathway signatures compared to HC. Progression to definite SSc was characterized by a shift in biological fingertips. Calcium-related events promoting endothelial damage and vasculopathy may be relevant to disease progression.
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Affiliation(s)
- Chiara Bellocchi
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Scleroderma Unit, Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Xuan Wang
- Biostatistics, Baylor Institute for Immunology Research, Dallas, TX, United States
| | - Marka A. Lyons
- Division of Rheumatology, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Maurizio Marchini
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Maurizio Lorini
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Vincenzo Carbonelli
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Nicola Montano
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Shervin Assassi
- Division of Rheumatology, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Lorenzo Beretta
- Scleroderma Unit, Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
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23
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McGovern KC, Nixon MP, Silverman JD. Addressing erroneous scale assumptions in microbe and gene set enrichment analysis. PLoS Comput Biol 2023; 19:e1011659. [PMID: 37983251 PMCID: PMC10695402 DOI: 10.1371/journal.pcbi.1011659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 12/04/2023] [Accepted: 11/04/2023] [Indexed: 11/22/2023] Open
Abstract
By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.
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Affiliation(s)
- Kyle C. McGovern
- Program in Bioinformatics and Genomics, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Michelle Pistner Nixon
- College of Information Sciences and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Justin D. Silverman
- Program in Bioinformatics and Genomics, Pennsylvania State University, State College, Pennsylvania, United States of America
- College of Information Sciences and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America
- Departments of Medicine and Statistics, Pennsylvania State University, State College, Pennsylvania, United States of America
- Institute for Computational and Data Science, Pennsylvania State University, State College, Pennsylvania, United States of America
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24
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Huttenhower C, Finn RD, McHardy AC. Challenges and opportunities in sharing microbiome data and analyses. Nat Microbiol 2023; 8:1960-1970. [PMID: 37783751 DOI: 10.1038/s41564-023-01484-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.
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Affiliation(s)
- Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Departments of Biostatistics and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alice Carolyn McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
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25
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Leventhal EL, Daamen AR, Grammer AC, Lipsky PE. An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients. iScience 2023; 26:108042. [PMID: 37860757 PMCID: PMC10582499 DOI: 10.1016/j.isci.2023.108042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/03/2023] [Accepted: 09/21/2023] [Indexed: 10/21/2023] Open
Abstract
Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.
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Affiliation(s)
- Emily L. Leventhal
- AMPEL BioSolutions LLC, and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | - Andrea R. Daamen
- AMPEL BioSolutions LLC, and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | - Amrie C. Grammer
- AMPEL BioSolutions LLC, and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | - Peter E. Lipsky
- AMPEL BioSolutions LLC, and the RILITE Research Institute, Charlottesville, VA 22902, USA
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26
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Gibbs DL, Strasser MK, Huang S. Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng). BIOINFORMATICS ADVANCES 2023; 3:vbad150. [PMID: 37886712 PMCID: PMC10599965 DOI: 10.1093/bioadv/vbad150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/03/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023]
Abstract
Summary Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on the differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single-cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis. Availability and implementation The gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using "pip install gssnng." More information and demo notebooks: see https://github.com/IlyaLab/gssnng.
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Affiliation(s)
- David L Gibbs
- Shmulevich Lab, Institute for Systems Biology, Seattle, WA 98106, United States
| | - Michael K Strasser
- Huang Lab, Institute for Systems Biology, Seattle, WA 98106, United States
| | - Sui Huang
- Huang Lab, Institute for Systems Biology, Seattle, WA 98106, United States
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27
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Zheng C, Wang M, Yamada R, Okada D. Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity. Comput Struct Biotechnol J 2023; 21:4988-5002. [PMID: 37867964 PMCID: PMC10589751 DOI: 10.1016/j.csbj.2023.09.042] [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: 04/18/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023] Open
Abstract
Gene sets are functional units for living cells. Previously, limited studies investigated the complex relations among gene sets, but documents about their altering patterns across biological conditions still need to be prepared. In this study, we adopted and modified a classical k-nearest neighbor-based association function to detect inter-gene-set similarities. Based on this method, we built multiplex networks of gene sets for the first time; these networks contain layers of gene sets corresponding to different populations of cells. The context-based multiplex networks can capture meaningful biological variation and have considerable differences from knowledge-based networks of gene sets built on Jaccard similarity, as demonstrated in this study. Furthermore, at the scale of individual gene sets, the structural coefficients of gene sets (multiplex PageRank centrality, clustering coefficient, and participation coefficient) disclose the diversity of gene sets from the perspective of structural properties and make it easier to identify unique gene sets. In gene set enrichment analysis (GSEA), each gene set is treated independently, and its contextual and relational attributes are ignored. The structural coefficients of gene sets can supplement GSEA with information about the overall picture of gene sets, promoting the constructive reorganization of the enriched terms and helping researchers better prioritize and select gene sets.
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Affiliation(s)
- Cheng Zheng
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto, 6068507, Kyoto, Japan
| | - Man Wang
- Department of Signal Transduction, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, 5650871, Osaka, Japan
| | - Ryo Yamada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto, 6068507, Kyoto, Japan
| | - Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto, 6068507, Kyoto, Japan
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28
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Du J, Gu XR, Yu XX, Cao YJ, Hou J. Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value. BLOOD SCIENCE 2023; 5:221-236. [PMID: 37941914 PMCID: PMC10629747 DOI: 10.1097/bs9.0000000000000172] [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: 05/17/2023] [Accepted: 09/18/2023] [Indexed: 11/10/2023] Open
Abstract
Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiao-Ran Gu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Xiao-Xiao Yu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Yang-Jia Cao
- Department of Hematology, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shanxi 710000, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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29
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Zhang L, Ren BC, Wei F, Liu Y, Gao Y, Yuan B. Ferroptosis regulator NOS2 is closely associated with the prognosis and cell malignant behaviors of hepatoblastoma: a bioinformatic and in vitro study. Front Oncol 2023; 13:1228199. [PMID: 37795447 PMCID: PMC10546316 DOI: 10.3389/fonc.2023.1228199] [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: 05/24/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
Background Hepatoblastoma (HB) is the most common liver tumor in children with easy metastasis. The emergence of ferroptosis as a novel form of cell death has gained increased attention in various human cancers. However, the roles of ferroptosis-related (FR) genes in HB remain elusive. Methods The GSE133039, GSE131329, and GSE81928 datasets were utilized for screening core FR genes in HB. Through Lasso regression analysis and using the support vector machine recursive feature elimination (SVM-RFE) algorithm, three candidate FR genes were obtained for characterizing HB. Their expression patterns and their clinical associations were explored through the 'Limma' R package, and their diagnostic potential was evaluated using ROC curves. Nitric oxide synthase 2 (NOS2) emerged as a candidate for further analyses. The CIBERSORT algorithm and GSEA dataset were used to respectively investigate the immune and metabolism effects of NOS2; the former was validated through immunofluorescence. The GSDC database was employed to analyze the correlation between NOS2 expression and the therapeutic efficacy of multiple drugs. PCR, Western blotting, colony formation assays, and Transwell experiments, were used to determine biological functions of NOS2 in HB cells. Potential upstream transcription factors of NOS2 were predicted through the TRRUST, hTFtarget, GeneCards, and JASPAR databases. Results NQO1, SLC1A4, and NOS2 were identified as potential genes in HB and found to be significantly upregulated in tumor samples. Nevertheless, only NOS2 was closely associated with HB clinicopathological characteristics; high NOS2 expression indicated poor prognosis, metastatic tendency, and late clinical stage. Immune analyses indicated that high NOS2 expression was concomitant with decreased infiltration levels of CD8+ T cells but increased infiltration levels of macrophages. GSEA revealed that NOS2 failed to affect the enrichments of glycolysis, fatty acid metabolism, and cholesterol biosynthesis in HB. Moreover, NOS2 was positively correlated with the IC50 values of trametinib, lapatinib, and cisplatin. NOS2 overexpression promoted the proliferation, migration and invasion of HepG2 and HuH-6 cells. JUND was identified as a potential transcriptional regulator of NOS2 by binding to its promoter (5'-TTCTGACTCTTTT-3'). Conclusion NOS2 plays a significant role in HB clinical assessments and holds promise as a novel therapeutic target.
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Affiliation(s)
- Lan Zhang
- Department of Pediatrics, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Bin-cheng Ren
- Department of Rheumatology and Immunology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Fei Wei
- Department of Pediatrics, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yan Liu
- Department of Pediatrics, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Ya Gao
- Department of Pediatric Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Bo Yuan
- Department of General Surgery, Xi’an Central Hospital, Xi’an, China
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30
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Hakobyan S, Stepanyan A, Nersisyan L, Binder H, Arakelyan A. PSF toolkit: an R package for pathway curation and topology-aware analysis. Front Genet 2023; 14:1264656. [PMID: 37680201 PMCID: PMC10482229 DOI: 10.3389/fgene.2023.1264656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.
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Affiliation(s)
- Siras Hakobyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
| | | | | | - Hans Binder
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Arsen Arakelyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Russian-Armenian University, Yerevan, Armenia
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Yen NTH, Phat NK, Oh JH, Park SM, Moon KS, Thu VTA, Cho YS, Shin JG, Long NP, Kim DH. Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure. Toxicol Appl Pharmacol 2023; 473:116597. [PMID: 37321324 DOI: 10.1016/j.taap.2023.116597] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/17/2023]
Abstract
Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.
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Affiliation(s)
- Nguyen Thi Hai Yen
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Nguyen Ky Phat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Jung-Hwa Oh
- Korea Institute of Toxicology, Daejeon 34114, Republic of Korea
| | - Se-Myo Park
- Korea Institute of Toxicology, Daejeon 34114, Republic of Korea
| | - Kyoung-Sik Moon
- Korea Institute of Toxicology, Daejeon 34114, Republic of Korea
| | - Vo Thuy Anh Thu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Yong-Soon Cho
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea; Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Republic of Korea.
| | - Dong Hyun Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea.
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Cho SB. Molecular Mechanisms of Endometriosis Revealed Using Omics Data. Biomedicines 2023; 11:2210. [PMID: 37626707 PMCID: PMC10452455 DOI: 10.3390/biomedicines11082210] [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: 06/06/2023] [Revised: 07/22/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.
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Affiliation(s)
- Seong Beom Cho
- Department of Biomedical Informatics, College of Medicine, Gachon University, 38-13, Dokgeom-ro 3 Street Namdon-gu, Incheon 21565, Republic of Korea
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33
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Karatzas E, Baltoumas FA, Aplakidou E, Kontou PI, Stathopoulos P, Stefanis L, Bagos PG, Pavlopoulos GA. Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources. Bioinformatics 2023; 39:btad490. [PMID: 37540207 PMCID: PMC10423032 DOI: 10.1093/bioinformatics/btad490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/31/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023] Open
Abstract
Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.
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Affiliation(s)
- Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Panagiota I Kontou
- Department of Mathematics, University of Thessaly, Lamia, 35100, Greece
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Panos Stathopoulos
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
- School of Medicine, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - Leonidas Stefanis
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, 11527, Greece
- Hellenic Army Academy, Vari, 16673, Greece
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Jablonski KP, Beerenwinkel N. Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression. Bioinformatics 2023; 39:btad522. [PMID: 37610338 PMCID: PMC10471899 DOI: 10.1093/bioinformatics/btad522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 07/04/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Abstract
MOTIVATION Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. RESULTS We address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA. AVAILABILITY AND IMPLEMENTATION pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.
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Affiliation(s)
- Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland
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35
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Kamalian A, Ho SG, Patel M, Lewis A, Bakker A, Albert M, O’Brien RJ, Moghekar A, Lutz MW. Exploratory Assessment of Proteomic Network Changes in Cerebrospinal Fluid of Mild Cognitive Impairment Patients: A Pilot Study. Biomolecules 2023; 13:1094. [PMID: 37509130 PMCID: PMC10377001 DOI: 10.3390/biom13071094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/30/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
(1) Background: Despite the existence of well-established, CSF-based biomarkers such as amyloid-β and phosphorylated-tau, the pathways involved in the pathophysiology of Alzheimer's disease (AD) remain an active area of research. (2) Methods: We measured 3072 proteins in CSF samples of AD-biomarker positive mild cognitive impairment (MCI) participants (n = 38) and controls (n = 48), using the Explore panel of the Olink proximity extension assay (PEA). We performed group comparisons, association studies with diagnosis, age, and APOE ε4 status, overrepresentation analysis (ORA), and gene set enrichment analysis (GSEA) to determine differentially expressed proteins and dysregulated pathways. (3) Results: GSEA results demonstrated an enrichment of granulocyte-related and chemotactic pathways (core enrichment proteins: ITGB2, ITGAM, ICAM1, SELL, SELP, C5, IL1A). Moreover, some of the well-replicated, differentially expressed proteins in CSF included: ITGAM, ITGB2, C1QA, TREM2, GFAP, NEFL, MMP-10, and a novel tau-related marker, SCRN1. (4) Conclusion: Our results highlight the upregulation of neuroinflammatory pathways, especially chemotactic and granulocyte recruitment in CSF of early AD patients.
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Affiliation(s)
- Aida Kamalian
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Sara G. Ho
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Megha Patel
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Richard J. O’Brien
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; (A.K.)
| | - Michael W. Lutz
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
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36
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Greenberg ZF, Graim KS, He M. Towards artificial intelligence-enabled extracellular vesicle precision drug delivery. Adv Drug Deliv Rev 2023:114974. [PMID: 37356623 DOI: 10.1016/j.addr.2023.114974] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 06/27/2023]
Abstract
Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.
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Affiliation(s)
- Zachary F Greenberg
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida, 32610, USA
| | - Kiley S Graim
- Department of Computer & Information Science & Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, 32610, USA
| | - Mei He
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida, 32610, USA.
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37
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Cecchetto M, Peruzza L, Giubilato E, Bernardini I, Rovere GD, Marcomini A, Regoli F, Bargelloni L, Patarnello T, Semenzin E, Milan M. An innovative index to incorporate transcriptomic data into weight of evidence approaches for environmental risk assessment. ENVIRONMENTAL RESEARCH 2023; 227:115745. [PMID: 36972774 DOI: 10.1016/j.envres.2023.115745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/15/2023] [Accepted: 03/22/2023] [Indexed: 05/08/2023]
Abstract
The sharp decrease in the cost of RNA-sequencing and the rapid improvement in computational analysis of eco-toxicogenomic data have brought new insights into the adverse effects of chemicals on aquatic organisms. Yet, transcriptomics is generally applied qualitatively in environmental risk assessments, hampering more effective exploitation of this evidence through multidisciplinary studies. In view of this limitation, a methodology is here presented to quantitatively elaborate transcriptional data in support to environmental risk assessment. The proposed methodology makes use of results from the application of Gene Set Enrichment Analysis to recent studies investigating the response of Mytilus galloprovincialis and Ruditapes philippinarum exposed to contaminants of emerging concern. The degree of changes in gene sets and the relevance of physiological reactions are integrated in the calculation of a hazard index. The outcome is then classified according to five hazard classes (from absent to severe), providing an evaluation of whole-transcriptome effects of chemical exposure. The application to experimental and simulated datasets proved that the method can effectively discriminate different levels of altered transcriptomic responses when compared to expert judgement (Spearman correlation coefficient of 0.96). A further application to data collected in two independent studies of Salmo trutta and Xenopus tropicalis exposed to contaminants confirmed the potential extension of the methodology to other aquatic species. This methodology can serve as a proof of concept for the integration of "genomic tools" in environmental risk assessment based on multidisciplinary investigations. To this end, the proposed transcriptomic hazard index can now be incorporated into quantitative Weight of Evidence approaches and weighed, with results from other types of analysis, to elucidate the role of chemicals in adverse ecological effects.
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Affiliation(s)
- Martina Cecchetto
- Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, via Torino 155, 30172, Mestre-Venezia, Italy
| | - Luca Peruzza
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy
| | - Elisa Giubilato
- Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, via Torino 155, 30172, Mestre-Venezia, Italy
| | - Ilaria Bernardini
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy
| | - Giulia Dalla Rovere
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy
| | - Antonio Marcomini
- Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, via Torino 155, 30172, Mestre-Venezia, Italy
| | - Francesco Regoli
- Department of Life and Environmental Sciences, Marche Polytechnic University, Via Brecce Bianche, 60131, Ancona, Italy; NFBC, National Future Biodiversity Center, Palermo, Italy
| | - Luca Bargelloni
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy
| | - Tomaso Patarnello
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy
| | - Elena Semenzin
- Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, via Torino 155, 30172, Mestre-Venezia, Italy.
| | - Massimo Milan
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Padova, Italy; NFBC, National Future Biodiversity Center, Palermo, Italy
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38
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Ferrucci L, Candia J, Ubaida-Mohien C, Lyaskov A, Banskota N, Leeuwenburgh C, Wohlgemuth S, Guralnik JM, Kaileh M, Zhang D, Sufit R, De S, Gorospe M, Munk R, Peterson CA, McDermott MM. Transcriptomic and Proteomic of Gastrocnemius Muscle in Peripheral Artery Disease. Circ Res 2023; 132:1428-1443. [PMID: 37154037 PMCID: PMC10213145 DOI: 10.1161/circresaha.122.322325] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/17/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Few effective therapies exist to improve lower extremity muscle pathology and mobility loss due to peripheral artery disease (PAD), in part because mechanisms associated with functional impairment remain unclear. METHODS To better understand mechanisms of muscle impairment in PAD, we performed in-depth transcriptomic and proteomic analyses on gastrocnemius muscle biopsies from 31 PAD participants (mean age, 69.9 years) and 29 age- and sex-matched non-PAD controls (mean age, 70.0 years) free of diabetes or limb-threatening ischemia. RESULTS Transcriptomic and proteomic analyses suggested activation of hypoxia-compensatory mechanisms in PAD muscle, including inflammation, fibrosis, apoptosis, angiogenesis, unfolded protein response, and nerve and muscle repair. Stoichiometric proportions of mitochondrial respiratory proteins were aberrant in PAD compared to non-PAD, suggesting that respiratory proteins not in complete functional units are not removed by mitophagy, likely contributing to abnormal mitochondrial activity. Supporting this hypothesis, greater mitochondrial respiratory protein abundance was significantly associated with greater complex II and complex IV respiratory activity in non-PAD but not in PAD. Rate-limiting glycolytic enzymes, such as hexokinase and pyruvate kinase, were less abundant in muscle of people with PAD compared with non-PAD participants, suggesting diminished glucose metabolism. CONCLUSIONS In PAD muscle, hypoxia induces accumulation of mitochondria respiratory proteins, reduced activity of rate-limiting glycolytic enzymes, and an enhanced integrated stress response that modulates protein translation. These mechanisms may serve as targets for disease modification.
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Affiliation(s)
- Luigi Ferrucci
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Julián Candia
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | | | - Alexey Lyaskov
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Nirad Banskota
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Christiaan Leeuwenburgh
- University of Florida, Institute on Aging, Department of Physiology and Aging, Gainesville, FL, USA
| | - Stephanie Wohlgemuth
- University of Florida, Institute on Aging, Department of Physiology and Aging, Gainesville, FL, USA
| | - Jack M. Guralnik
- University of Maryland School of Medicine, Department of Epidemiology and Public Health, Baltimore, MD, USA
| | - Mary Kaileh
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Dongxue Zhang
- Northwestern University Feinberg School of Medicine, Department of Neurology, Chicago, IL, USA
| | - Robert Sufit
- Northwestern University Feinberg School of Medicine, Department of Neurology, Chicago, IL, USA
| | - Supriyo De
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Myriam Gorospe
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Rachel Munk
- National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Charlotte A. Peterson
- Center for Muscle Biology. College of Health Sciences, University of Kentucky, Lexington, KY, USA
| | - Mary M. McDermott
- Northwestern University Feinberg School of Medicine, Department of Medicine, Chicago, IL, USA
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39
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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40
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Clarke ML, Lemma RB, Walton DS, Volpe G, Noyvert B, Gabrielsen OS, Frampton J. MYB insufficiency disrupts proteostasis in hematopoietic stem cells, leading to age-related neoplasia. Blood 2023; 141:1858-1870. [PMID: 36603185 PMCID: PMC10646772 DOI: 10.1182/blood.2022019138] [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: 11/21/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023] Open
Abstract
MYB plays a key role in gene regulation throughout the hematopoietic hierarchy and is critical for the maintenance of normal hematopoietic stem cells (HSC). Acquired genetic dysregulation of MYB is involved in the etiology of a number of leukemias, although inherited noncoding variants of the MYB gene are a susceptibility factor for many hematological conditions, including myeloproliferative neoplasms (MPN). The mechanisms that connect variations in MYB levels to disease predisposition, especially concerning age dependency in disease initiation, are completely unknown. Here, we describe a model of Myb insufficiency in mice that leads to MPN, myelodysplasia, and leukemia in later life, mirroring the age profile of equivalent human diseases. We show that this age dependency is intrinsic to HSC, involving a combination of an initial defective cellular state resulting from small effects on the expression of multiple genes and a progressive accumulation of further subtle changes. Similar to previous studies showing the importance of proteostasis in HSC maintenance, we observed altered proteasomal activity and elevated proliferation indicators, followed by elevated ribosome activity in young Myb-insufficient mice. We propose that these alterations combine to cause an imbalance in proteostasis, potentially creating a cellular milieu favoring disease initiation.
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Affiliation(s)
- Mary L. Clarke
- Institute of Cancer & Genomic Sciences, College of Medical & Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Roza B. Lemma
- Department of Biosciences, University of Oslo, Oslo, Norway
- Centre for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - David S. Walton
- Institute of Cancer & Genomic Sciences, College of Medical & Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Giacomo Volpe
- Institute of Cancer & Genomic Sciences, College of Medical & Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Boris Noyvert
- Institute of Cancer & Genomic Sciences, College of Medical & Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- CRUK Birmingham Centre and Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
| | | | - Jon Frampton
- Institute of Cancer & Genomic Sciences, College of Medical & Dental Sciences, University of Birmingham, Birmingham, United Kingdom
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Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet 2023; 39:308-319. [PMID: 36750393 DOI: 10.1016/j.tig.2023.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/24/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023]
Abstract
Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.
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Affiliation(s)
- Kangmei Zhao
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
| | - Seung Yon Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
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42
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Griffin AT, Vlahos LJ, Chiuzan C, Califano A. NaRnEA: An Information Theoretic Framework for Gene Set Analysis. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25030542. [PMID: 36981431 PMCID: PMC10048242 DOI: 10.3390/e25030542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/03/2023] [Accepted: 03/13/2023] [Indexed: 05/26/2023]
Abstract
Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.
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Affiliation(s)
- Aaron T. Griffin
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lukas J. Vlahos
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Codruta Chiuzan
- Department of Biostatistics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- JP Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
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43
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Zaghen S, Konzock O, Fu J, Kerkhoven EJ. Abolishing storage lipids induces protein misfolding and stress responses in Yarrowia lipolytica. J Ind Microbiol Biotechnol 2023; 50:kuad031. [PMID: 37742215 PMCID: PMC10563384 DOI: 10.1093/jimb/kuad031] [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: 08/15/2023] [Accepted: 09/19/2023] [Indexed: 09/26/2023]
Abstract
Yarrowia lipolytica naturally saves excess carbon as storage lipids. Engineering efforts allow redirecting the high precursor flux required for lipid synthesis toward added-value chemicals such as polyketides, flavonoids, and terpenoids. To redirect precursor flux from storage lipids to other products, four genes involved in triacylglycerol and sterol ester synthesis (DGA1, DGA2, LRO1, and ARE1) can be deleted. To elucidate the effect of the deletions on cell physiology and regulation, we performed chemostat cultivations under carbon and nitrogen limitations, followed by transcriptome analysis. We found that storage lipid-free cells show an enrichment of the unfolded protein response, and several biological processes related to protein refolding and degradation are enriched. Additionally, storage lipid-free cells show an altered lipid class distribution with an abundance of potentially cytotoxic free fatty acids under nitrogen limitation. Our findings not only highlight the importance of lipid metabolism on cell physiology and proteostasis, but can also aid the development of improved chassy strains of Y. lipolytica for commodity chemical production.
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Affiliation(s)
- Simone Zaghen
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Göteborg, Sweden
| | - Oliver Konzock
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Göteborg, Sweden
| | - Jing Fu
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Göteborg, Sweden
| | - Eduard J Kerkhoven
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Göteborg, Sweden
- SciLifeLab, Chalmers University of Technology, Göteborg 412 96, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800Lyngby, Denmark
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44
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Vasilopoulou C, McDaid-McCloskey SL, McCluskey G, Duguez S, Morris AP, Duddy W. Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS. Int J Mol Sci 2023; 24:4021. [PMID: 36835433 PMCID: PMC9966913 DOI: 10.3390/ijms24044021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/26/2023] [Accepted: 02/02/2023] [Indexed: 02/19/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.
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Affiliation(s)
- Christina Vasilopoulou
- Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK
| | | | - Gavin McCluskey
- Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK
| | - Stephanie Duguez
- Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester M13 9PT, UK
| | - William Duddy
- Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry BT47 6SB, UK
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45
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Cortical profiles of numerous psychiatric disorders and normal development share a common pattern. Mol Psychiatry 2023; 28:698-709. [PMID: 36380235 DOI: 10.1038/s41380-022-01855-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 10/19/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022]
Abstract
The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.
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46
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Justin Margret J, Jain SK. Overview of gene expression techniques with an emphasis on vitamin D related studies. Curr Med Res Opin 2023; 39:205-217. [PMID: 36537177 DOI: 10.1080/03007995.2022.2159148] [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] [Indexed: 12/24/2022]
Abstract
Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.
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Affiliation(s)
- Jeffrey Justin Margret
- Department of Pediatrics, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA, USA
| | - Sushil K Jain
- Department of Pediatrics, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA, USA
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47
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Liu CJ, Hu FF, Xie GY, Miao YR, Li XW, Zeng Y, Guo AY. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform 2023; 24:6957252. [PMID: 36549921 DOI: 10.1093/bib/bbac558] [Citation(s) in RCA: 88] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/16/2022] [Indexed: 12/24/2022] Open
Abstract
Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.
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Affiliation(s)
- Chun-Jie Liu
- Wuhan University of Science and Technology and Huazhong University of Science and Technology
| | - Fei-Fei Hu
- Wuhan University of Science and Technology
| | - Gui-Yan Xie
- Huazhong University of Science and Technology
| | - Ya-Ru Miao
- Huazhong University of Science and Technology
| | - Xin-Wen Li
- Wuhan University of Science and Technology
| | - Yan Zeng
- Wuhan University of Science and Technology
| | - An-Yuan Guo
- Huazhong University of Science and Technology
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48
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Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform 2023; 24:6972298. [PMID: 36611240 DOI: 10.1093/bib/bbac600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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Affiliation(s)
- Joel Defo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
| | - Denis Awany
- South African Tuberculosis Vaccine Initiative (SATVI), University of Cape Town, 7925, South Africa
| | - Raj Ramesar
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
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49
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Chen JW, Shrestha L, Green G, Leier A, Marquez-Lago TT. The hitchhikers' guide to RNA sequencing and functional analysis. Brief Bioinform 2023; 24:bbac529. [PMID: 36617463 PMCID: PMC9851315 DOI: 10.1093/bib/bbac529] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 01/10/2023] Open
Abstract
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.
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Affiliation(s)
- Jiung-Wen Chen
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lisa Shrestha
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - George Green
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Microbiology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
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50
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Yang F, Chen X, Zhang H, Zhao GD, Yang H, Qiu J, Meng S, Wu P, Tao L, Wang Q, Huang G. Single-Cell Transcriptome Identifies the Renal Cell Type Tropism of Human BK Polyomavirus. Int J Mol Sci 2023; 24:ijms24021330. [PMID: 36674845 PMCID: PMC9861348 DOI: 10.3390/ijms24021330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/12/2023] Open
Abstract
BK polyomavirus (BKPyV) infection is the main factor affecting the prognosis of kidney transplant recipients, as no antiviral agent is yet available. A better understanding of the renal-cell-type tropism of BKPyV can serve to develop new treatment strategies. In this study, the single-cell transcriptomic analysis demonstrated that the ranking of BKPyV tropism for the kidney was proximal tubule cells (PT), collecting duct cells (CD), and glomerular endothelial cells (GEC) according to the signature of renal cell type and immune microenvironment. In normal kidneys, we found that BKPyV infection-related transcription factors P65 and CEBPB were PT-specific transcription factors, and PT showed higher glycolysis/gluconeogenesis activities than CD and GEC. Furthermore, in the BKPyV-infected kidneys, the percentage of late viral transcripts in PT was significantly higher than in CD and GEC. In addition, PT had the smallest cell-cell interactions with immune cells compared to CD and GEC in both normal and BKPyV-infected kidneys. Subsequently, we indirectly demonstrated the ranking of BKPyV tropism via the clinical observation of sequential biopsies. Together, our results provided in-depth insights into the renal cell-type tropism of BKPyV in vivo at single-cell resolution and proposed a novel antiviral target.
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Affiliation(s)
- Feng Yang
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Sun Yat-Sen University, Guangzhou 510080, China
- Guangdong Provincial International Cooperation Based of Science and Technology (Organ Transplantation), Sun Yat-Sen University, Guangzhou 510080, China
| | - Xutao Chen
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Hui Zhang
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Guo-Dong Zhao
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Huifei Yang
- Department of Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510080, China
| | - Jiang Qiu
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Siyan Meng
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Penghan Wu
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Liang Tao
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qin Wang
- Department of Pharmacology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
- Correspondence: (Q.W.); (G.H.)
| | - Gang Huang
- Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Sun Yat-Sen University, Guangzhou 510080, China
- Guangdong Provincial International Cooperation Based of Science and Technology (Organ Transplantation), Sun Yat-Sen University, Guangzhou 510080, China
- Correspondence: (Q.W.); (G.H.)
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