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Barchi A, Massimino L, Mandarino FV, Vespa E, Sinagra E, Almolla O, Passaretti S, Fasulo E, Parigi TL, Cagliani S, Spanò S, Ungaro F, Danese S. Microbiota profiling in esophageal diseases: Novel insights into molecular staining and clinical outcomes. Comput Struct Biotechnol J 2024; 23:626-637. [PMID: 38274997 PMCID: PMC10808859 DOI: 10.1016/j.csbj.2023.12.026] [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: 08/22/2023] [Revised: 12/22/2023] [Accepted: 12/23/2023] [Indexed: 01/27/2024] Open
Abstract
Gut microbiota is recognized nowadays as one of the key players in the development of several gastro-intestinal diseases. The first studies focused mainly on healthy subjects with staining of main bacterial species via culture-based techniques. Subsequently, lots of studies tried to focus on principal esophageal disease enlarged the knowledge on esophageal microbial environment and its role in pathogenesis. Gastro Esophageal Reflux Disease (GERD), the most widespread esophageal condition, seems related to a certain degree of mucosal inflammation, via interleukin (IL) 8 potentially enhanced by bacterial components, lipopolysaccharide (LPS) above all. Gram- bacteria, producing LPS), such as Campylobacter genus, have been found associated with GERD. Barrett esophagus (BE) seems characterized by a Gram- and microaerophils-shaped microbiota. Esophageal cancer (EC) development leads to an overturn in the esophageal environment with the shift from an oral-like microbiome to a prevalently low-abundant and low-diverse Gram--shaped microbiome. Although underinvestigated, also changes in the esophageal microbiome are associated with rare chronic inflammatory or neuropathic disease pathogenesis. The paucity of knowledge about the microbiota-driven mechanisms in esophageal disease pathogenesis is mainly due to the scarce sensitivity of sequencing technology and culture methods applied so far to study commensals in the esophagus. However, the recent advances in molecular techniques, especially with the advent of non-culture-based genomic sequencing tools and the implementation of multi-omics approaches, have revolutionized the microbiome field, with promises of implementing the current knowledge, discovering more mechanisms underneath, and giving insights into the development of novel therapies aimed to re-establish the microbial equilibrium for ameliorating esophageal diseases..
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Affiliation(s)
- Alberto Barchi
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Massimino
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Edoardo Vespa
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Emanuele Sinagra
- Gastroenterology & Endoscopy Unit, Fondazione Istituto G. Giglio, Cefalù, Italy
| | - Omar Almolla
- Università Vita-Salute San Raffaele, Faculty of Medicine, Milan, Italy
| | - Sandro Passaretti
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Tommaso Lorenzo Parigi
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Faculty of Medicine, Milan, Italy
| | - Stefania Cagliani
- Università Vita-Salute San Raffaele, Faculty of Medicine, Milan, Italy
| | - Salvatore Spanò
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Federica Ungaro
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Silvio Danese
- Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Faculty of Medicine, Milan, Italy
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Fernández-Edreira D, Liñares-Blanco J, V.-del-Río P, Fernandez-Lozano C. VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria. Comput Struct Biotechnol J 2024; 23:148-156. [PMID: 38144944 PMCID: PMC10749217 DOI: 10.1016/j.csbj.2023.11.050] [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: 09/08/2023] [Revised: 11/16/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023] Open
Abstract
This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.
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Affiliation(s)
- Diego Fernández-Edreira
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain
| | | | - Patricia V.-del-Río
- Servicio de Ginecología, Hospital Universitario Lucus Augusti (HULA). Servizo Galego de Saúde (SERGAS), Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain
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Samorodnitsky S, Wendt CH, Lock EF. Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data. Comput Stat Data Anal 2024; 197:107974. [PMID: 38947282 PMCID: PMC11210674 DOI: 10.1016/j.csda.2024.107974] [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] [Indexed: 07/02/2024]
Abstract
Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.
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Affiliation(s)
- Sarah Samorodnitsky
- Division of Biostatistics, University of Minnesota, Minneapolis, 55455, MN, USA
- Fred Hutch Cancer Center, Seattle, 98109, WA, USA
| | - Chris H. Wendt
- Minneapolis VA Health Care System, Minneapolis, 55417, MN, USA
| | - Eric F. Lock
- Division of Biostatistics, University of Minnesota, Minneapolis, 55455, MN, USA
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Rintala TJ, Fortino V. COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms. PLoS Comput Biol 2024; 20:e1012275. [PMID: 39102448 DOI: 10.1371/journal.pcbi.1012275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 06/25/2024] [Indexed: 08/07/2024] Open
Abstract
Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies.
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Affiliation(s)
- Teemu J Rintala
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
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Shi C, Cheng L, Yu Y, Chen S, Dai Y, Yang J, Zhang H, Chen J, Geng N. Multi-omics Integration Analysis: Tools and Applications in Environmental Toxicology. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024:124675. [PMID: 39103035 DOI: 10.1016/j.envpol.2024.124675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/08/2024] [Accepted: 08/03/2024] [Indexed: 08/07/2024]
Abstract
Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.
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Affiliation(s)
- Chengcheng Shi
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Lin Cheng
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Ying Yu
- College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Shuangshuang Chen
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, China
| | - Yubing Dai
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Jiajia Yang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; College of Materials Science and Engineering, Hebei University of Engineering, Handan, 056038, China
| | - Haijun Zhang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Jiping Chen
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Ningbo Geng
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
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Seffernick AE, Cao X, Cheng C, Yang W, Autry RJ, Yang JJ, Pui CH, Teachey DT, Lamba JK, Mullighan CG, Pounds SB. Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.605805. [PMID: 39131398 PMCID: PMC11312528 DOI: 10.1101/2024.07.31.605805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Motivation Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. Model We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. Results In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. Availability Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR . The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR . Contact Stanley.Pounds@stjude.org. Supplementary Information Supplementary data are available at the journal's website.
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Liu C, Li R, Wu S, Che H, Jiang D, Yu Z, Wong HS. Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10803-10816. [PMID: 37028079 DOI: 10.1109/tnnls.2023.3244021] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.
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Yang Y, Pan Z, Sun J, Welch J, Klionsky DJ. Autophagy and machine learning: Unanswered questions. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167263. [PMID: 38801963 DOI: 10.1016/j.bbadis.2024.167263] [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: 02/15/2024] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.
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Affiliation(s)
- Ying Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhaoying Pan
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianhui Sun
- Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA
| | - Joshua Welch
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel J Klionsky
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Odriozola I, Rasmussen JA, Gilbert MTP, Limborg MT, Alberdi A. A practical introduction to holo-omics. CELL REPORTS METHODS 2024; 4:100820. [PMID: 38986611 PMCID: PMC11294832 DOI: 10.1016/j.crmeth.2024.100820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/17/2024] [Accepted: 06/20/2024] [Indexed: 07/12/2024]
Abstract
Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.
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Affiliation(s)
- Iñaki Odriozola
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Jacob A Rasmussen
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - M Thomas P Gilbert
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark; University Museum, NTNU, Trondheim, Norway
| | - Morten T Limborg
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Antton Alberdi
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
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Özçam M, Lin DL, Gupta CL, Li A, Wheatley LM, Baloh CH, Sanda S, Jones SM, Lynch SV. Enhanced Gut Microbiome Capacity for Amino Acid Metabolism is associated with Peanut Oral Immunotherapy Failure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.15.24309840. [PMID: 39072014 PMCID: PMC11275660 DOI: 10.1101/2024.07.15.24309840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Peanut Oral Immunotherapy (POIT) holds promise for remission of peanut allergy, though treatment is protracted and successful in only a subset of patients. Because the gut microbiome is linked to food allergy, we sought to identify fecal microbial predictors of POIT efficacy and to develop mechanistic insights into treatment response. Longitudinal functional analysis of the fecal microbiome of children (n=79) undergoing POIT in a first double-blind, placebo-controlled clinical trial, identified five microbial-derived bile acids enriched in fecal samples prior to POIT initiation that predicted treatment efficacy (AUC 0.71). Failure to induce disease remission was associated with a distinct fecal microbiome with enhanced capacity for bile acid deconjugation, amino acid metabolism, and increased peanut peptide degradation in vitro . Thus, microbiome mechanisms of POIT failure appear to include depletion of immunomodulatory secondary bile and amino acids and the antigenic peanut peptides necessary to promote peanut allergy desensitization and remission.
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12
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Miao R, Li S, Fan D, Luoye F, Zhang J, Zheng W, Zhu M, Zhou A, Wang X, Yan S, Liang Y, Deng RL. An Integrated Multi-omics prediction model for stroke recurrence based on L net transformer layer and dynamic weighting mechanism. Comput Biol Med 2024; 179:108823. [PMID: 38991322 DOI: 10.1016/j.compbiomed.2024.108823] [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: 12/29/2023] [Revised: 06/24/2024] [Accepted: 06/26/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. METHODS We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. RESULTS We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. CONCLUSION MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.
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Affiliation(s)
- Rui Miao
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Siyuan Li
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Daying Fan
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Fangxin Luoye
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jing Zhang
- Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Wenli Zheng
- Medical Imaging Department, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Minglan Zhu
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Aiting Zhou
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xianlin Wang
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Shan Yan
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | | | - Ren-Li Deng
- Nursing Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China.
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13
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Choudhury P, Dasgupta S, Bhattacharyya P, Roychowdhury S, Chaudhury K. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. Mol Omics 2024; 20:366-389. [PMID: 38853716 DOI: 10.1039/d3mo00266g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
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Affiliation(s)
- Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sanjukta Dasgupta
- Department of Biotechnology, Brainware University, Barasat, West Bengal, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
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14
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Curion F, Rich-Griffin C, Agarwal D, Ouologuem S, Rue-Albrecht K, May L, Garcia GEL, Heumos L, Thomas T, Lason W, Sims D, Theis FJ, Dendrou CA. Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis. Genome Biol 2024; 25:181. [PMID: 38978088 PMCID: PMC11229213 DOI: 10.1186/s13059-024-03322-7] [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: 03/14/2023] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
Abstract
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
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Affiliation(s)
- Fabiola Curion
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Devika Agarwal
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Sarah Ouologuem
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Kevin Rue-Albrecht
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Lilly May
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Giulia E L Garcia
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Doctoral Training Centre, University of Oxford, Oxford, UK
| | - Lukas Heumos
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Comprehensive Pneumology Center With the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Tom Thomas
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Wojciech Lason
- Nuffield Department of Medicine, Respiratory Medicine Unit, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - David Sims
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
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15
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Mohr AE, Ortega-Santos CP, Whisner CM, Klein-Seetharaman J, Jasbi P. Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare. Biomedicines 2024; 12:1496. [PMID: 39062068 PMCID: PMC11274472 DOI: 10.3390/biomedicines12071496] [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: 04/15/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.
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Affiliation(s)
- Alex E. Mohr
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Carmen P. Ortega-Santos
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Corrie M. Whisner
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Judith Klein-Seetharaman
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Paniz Jasbi
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
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16
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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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17
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Zinter MS, Dvorak CC, Mayday MY, Reyes G, Simon MR, Pearce EM, Kim H, Shaw PJ, Rowan CM, Auletta JJ, Martin PL, Godder K, Duncan CN, Lalefar NR, Kreml EM, Hume JR, Abdel-Azim H, Hurley C, Cuvelier GDE, Keating AK, Qayed M, Killinger JS, Fitzgerald JC, Hanna R, Mahadeo KM, Quigg TC, Satwani P, Castillo P, Gertz SJ, Moore TB, Hanisch B, Abdel-Mageed A, Phelan R, Davis DB, Hudspeth MP, Yanik GA, Pulsipher MA, Sulaiman I, Segal LN, Versluys BA, Lindemans CA, Boelens JJ, DeRisi JL. Pathobiological signatures of dysbiotic lung injury in pediatric patients undergoing stem cell transplantation. Nat Med 2024; 30:1982-1993. [PMID: 38783139 PMCID: PMC11271406 DOI: 10.1038/s41591-024-02999-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/12/2024] [Indexed: 05/25/2024]
Abstract
Hematopoietic cell transplantation (HCT) uses cytotoxic chemotherapy and/or radiation followed by intravenous infusion of stem cells to cure malignancies, bone marrow failure and inborn errors of immunity, hemoglobin and metabolism. Lung injury is a known complication of the process, due in part to disruption in the pulmonary microenvironment by insults such as infection, alloreactive inflammation and cellular toxicity. How microorganisms, immunity and the respiratory epithelium interact to contribute to lung injury is uncertain, limiting the development of prevention and treatment strategies. Here we used 278 bronchoalveolar lavage (BAL) fluid samples to study the lung microenvironment in 229 pediatric patients who have undergone HCT treated at 32 children's hospitals between 2014 and 2022. By leveraging paired microbiome and human gene expression data, we identified high-risk BAL compositions associated with in-hospital mortality (P = 0.007). Disadvantageous profiles included bacterial overgrowth with neutrophilic inflammation, microbiome contraction with epithelial fibroproliferation and profound commensal depletion with viral and staphylococcal enrichment, lymphocytic activation and cellular injury, and were replicated in an independent cohort from the Netherlands (P = 0.022). In addition, a broad array of previously occult pathogens was identified, as well as a strong link between antibiotic exposure, commensal bacterial depletion and enrichment of viruses and fungi. Together these lung-immune system-microorganism interactions clarify the important drivers of fatal lung injury in pediatric patients who have undergone HCT. Further investigation is needed to determine how personalized interpretation of heterogeneous pulmonary microenvironments may be used to improve pediatric HCT outcomes.
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Affiliation(s)
- Matt S Zinter
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
- Division of Allergy, Immunology, and Bone Marrow Transplantation, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
| | - Christopher C Dvorak
- Division of Allergy, Immunology, and Bone Marrow Transplantation, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Madeline Y Mayday
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Departments of Laboratory Medicine and Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Gustavo Reyes
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Miriam R Simon
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Emma M Pearce
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Hanna Kim
- Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Shaw
- The Children's Hospital at Westmead, Sydney, New South Wales, Australia
| | - Courtney M Rowan
- Department of Pediatrics, Division of Critical Care Medicine, Indiana University, Indianapolis, IN, USA
| | - Jeffrey J Auletta
- Hematology/Oncology/BMT and Infectious Diseases, Nationwide Children's Hospital, Columbus, OH, USA
- Center for International Blood and Marrow Transplant Research, National Marrow Donor Program/Be The Match, Minneapolis, MN, USA
| | - Paul L Martin
- Division of Pediatric and Cellular Therapy, Duke University Medical Center, Durham, NC, USA
| | - Kamar Godder
- Cancer and Blood Disorders Center, Nicklaus Children's Hospital, Miami, FL, USA
| | - Christine N Duncan
- Division of Pediatric Oncology Harvard Medical School Department of Pediatrics, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA, USA
| | - Nahal R Lalefar
- Division of Pediatric Hematology/Oncology, Benioff Children's Hospital Oakland, University of California, San Francisco, Oakland, CA, USA
| | - Erin M Kreml
- Department of Child Health, Division of Critical Care Medicine, University of Arizona, Phoenix, AZ, USA
| | - Janet R Hume
- Department of Pediatrics, Division of Critical Care Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Hisham Abdel-Azim
- Department of Pediatrics, Division of Hematology/Oncology and Transplant and Cell Therapy, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Loma Linda University School of Medicine, Cancer Center, Children Hospital and Medical Center, Loma Linda, CA, USA
| | - Caitlin Hurley
- Department of Pediatric Medicine, Division of Critical Care, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Geoffrey D E Cuvelier
- CancerCare Manitoba, Manitoba Blood and Marrow Transplant Program, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Amy K Keating
- Division of Pediatric Oncology Harvard Medical School Department of Pediatrics, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA, USA
- Center for Cancer and Blood Disorders, Children's Hospital Colorado and University of Colorado, Aurora, CO, USA
| | - Muna Qayed
- Aflac Cancer & Blood Disorders Center, Children's Healthcare of Atlanta and Emory University, Atlanta, GA, USA
| | - James S Killinger
- Department of Pediatrics, Division of Pediatric Critical Care, Weill Cornell Medicine, New York, NY, USA
| | - Julie C Fitzgerald
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Rabi Hanna
- Department of Pediatric Hematology, Oncology and Blood and Marrow Transplantation, Pediatric Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kris M Mahadeo
- Division of Pediatric and Cellular Therapy, Duke University Medical Center, Durham, NC, USA
- Department of Pediatrics, Division of Hematology/Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Troy C Quigg
- Pediatric Blood and Marrow Transplantation Program, Texas Transplant Institute, Methodist Children's Hospital, San Antonio, TX, USA
- Section of Pediatric BMT and Cellular Therapy, Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Prakash Satwani
- Department of Pediatrics, Division of Pediatric Hematology, Oncology and Stem Cell Transplantation, Columbia University, New York, NY, USA
| | - Paul Castillo
- UF Health Shands Children's Hospital, University of Florida, Gainesville, FL, USA
| | - Shira J Gertz
- Department of Pediatrics, Division of Critical Care Medicine, Joseph M Sanzari Children's Hospital at Hackensack University Medical Center, Hackensack, NJ, USA
- Department of Pediatrics, Division of Critical Care Medicine, St. Barnabas Medical Center, Livingston, NJ, USA
| | - Theodore B Moore
- Department of Pediatric Hematology-Oncology, Mattel Children's Hospital, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin Hanisch
- Department of Pediatrics, Division of Infectious Diseases, Children's National Hospital, Washington DC, USA
| | - Aly Abdel-Mageed
- Section of Pediatric BMT and Cellular Therapy, Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Rachel Phelan
- Department of Pediatrics, Division of Pediatric Hematology/Oncology/BMT, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dereck B Davis
- Department of Pediatrics, Hematology/Oncology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Michelle P Hudspeth
- Adult and Pediatric Blood & Marrow Transplantation, Pediatric Hematology/Oncology, Medical University of South Carolina Children's Hospital/Hollings Cancer Center, Charleston, SC, USA
| | - Greg A Yanik
- Pediatric Blood and Bone Marrow Transplantation, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Michael A Pulsipher
- Division of Hematology, Oncology, Transplantation, and Immunology, Primary Children's Hospital, Huntsman Cancer Institute, Spense Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, USA
| | - Imran Sulaiman
- Department of Respiratory Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Leopoldo N Segal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA
| | - Birgitta A Versluys
- Department of Stem Cell Transplantation, Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Division of Pediatrics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Caroline A Lindemans
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Laura and Isaac Perlmutter Cancer Center, New York University Grossman School of Medicine, New York University Langone Health, New York, NY, USA
- Department of Stem Cell Transplantation, Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Jaap J Boelens
- Department of Stem Cell Transplantation, Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
- Division of Pediatrics, University Medical Center Utrecht, Utrecht, the Netherlands
- Transplantation and Cellular Therapy, MSK Kids, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph L DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
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18
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Immer A, Stark SG, Jacob F, Bonilla X, Thomas T, Kahles A, Goetze S, Milani ES, Wollscheid B, Rätsch G, Lehmann KV. Probabilistic pathway-based multimodal factor analysis. Bioinformatics 2024; 40:i189-i198. [PMID: 38940152 PMCID: PMC11256960 DOI: 10.1093/bioinformatics/btae216] [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: 06/29/2024] Open
Abstract
MOTIVATION Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. RESULTS We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. AVAILABILITY AND IMPLEMENTATION The tool is implemented in python and available at https://github.com/ratschlab/path-fa.
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Affiliation(s)
- Alexander Immer
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | - Stefan G Stark
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Francis Jacob
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
| | - Ximena Bonilla
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Tinu Thomas
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - André Kahles
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sandra Goetze
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
- ETH PHRT Swiss Multi-Omics Center (SMOC), 8093 Zurich, Switzerland
| | - Emanuela S Milani
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
| | - Bernd Wollscheid
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
| | - Gunnar Rätsch
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- AI Center at ETH Zurich, 8092 Zurich, Switzerland
- Biomedical Informatics Research, University Hospital Zurich, 8006 Zurich, Switzerland
- Department of Biology, ETH Zurich, 8049 Zurich, Switzerland
| | - Kjong-Van Lehmann
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
- Cancer Research Center Cologne Essen, University Hospital Cologne, 50937 Cologne, Germany
- Joint Research Center for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
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19
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Van Coillie S, Prévot J, Sánchez-Ramón S, Lowe DM, Borg M, Autran B, Segundo G, Pecoraro A, Garcelon N, Boersma C, Silva SL, Drabwell J, Quinti I, Meyts I, Ali A, Burns SO, van Hagen M, Pergent M, Mahlaoui N. Charting a course for global progress in PIDs by 2030 - proceedings from the IPOPI global multi-stakeholders' summit (September 2023). Front Immunol 2024; 15:1430678. [PMID: 39055704 PMCID: PMC11270239 DOI: 10.3389/fimmu.2024.1430678] [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: 05/10/2024] [Accepted: 06/13/2024] [Indexed: 07/27/2024] Open
Abstract
The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its second Global Multi-Stakeholders' Summit, an annual stimulating and forward-thinking meeting uniting experts to anticipate pivotal upcoming challenges and opportunities in the field of primary immunodeficiency (PID). The 2023 summit focused on three key identified discussion points: (i) How can immunoglobulin (Ig) therapy meet future personalized patient needs? (ii) Pandemic preparedness: what's next for public health and potential challenges for the PID community? (iii) Diagnosing PIDs in 2030: what needs to happen to diagnose better and to diagnose more? Clinician-Scientists, patient representatives and other stakeholders explored avenues to improve Ig therapy through mechanistic insights and tailored Ig preparations/products according to patient-specific needs and local exposure to infectious agents, amongst others. Urgency for pandemic preparedness was discussed, as was the threat of shortage of antibiotics and increasing antimicrobial resistance, emphasizing the need for representation of PID patients and other vulnerable populations throughout crisis and care management. Discussion also covered the complexities of PID diagnosis, addressing issues such as global diagnostic disparities, the integration of patient-reported outcome measures, and the potential of artificial intelligence to increase PID diagnosis rates and to enhance diagnostic precision. These proceedings outline the outcomes and recommendations arising from the 2023 IPOPI Global Multi-Stakeholders' Summit, offering valuable insights to inform future strategies in PID management and care. Integral to this initiative is its role in fostering collaborative efforts among stakeholders to prepare for the multiple challenges facing the global PID community.
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Affiliation(s)
- Samya Van Coillie
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Johan Prévot
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Silvia Sánchez-Ramón
- Department of Clinical Immunology, Health Research Institute of the Hospital Clínico San Carlos/Fundación para la Investigación Biomédica del Hospital Clínico San Carlos (IML and IdISSC), Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - David M. Lowe
- Department of Immunology, Royal Free London National Heath System (NHS) Foundation Trust, London, United Kingdom
- Institute of Immunity and Transplantation, University College London, London, United Kingdom
| | - Michael Borg
- Department of Infection Control & Sterile Services, Mater Dei Hospital, Msida, Malta
| | - Brigitte Autran
- Sorbonne-Université, Cimi-Paris, Institut national de la santé et de la recherche médicale (INSERM) U1135, centre national de la recherche scientifique (CNRS) ERL8255, Université Pierre et Marie Curie Centre de Recherche n°7 (UPMC CR7), Paris, France
| | - Gesmar Segundo
- Departamento de Pediatra, Universidade Federal de Uberlândia, Uberlandia, MG, Brazil
| | - Antonio Pecoraro
- Transfusion Medicine Unit, Azienda Sanitaria Territoriale, Ascoli Piceno, Italy
| | - Nicolas Garcelon
- Université de Paris, Imagine Institute, Data Science Platform, Institut national de la santé et de la recherche médicale Unité Mixte de Recherche (INSERM UMR) 1163, Paris, France
| | - Cornelis Boersma
- Health-Ecore B.V., Zeist, Netherlands
- Unit of Global Health, Department of Health Sciences, University Medical Center Groningen (UMCG), University of Groningen, Groningen, Netherlands
- Department of Management Sciences, Open University, Heerlen, Netherlands
| | - Susana L. Silva
- Serviço de Imunoalergologia, Unidade Local de Saúde de Santa Maria, Lisbon, Portugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Jose Drabwell
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Isabella Quinti
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Isabelle Meyts
- Department of Pediatrics, University Hospitals Leuven, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Adli Ali
- Department of Paediatrics, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Hospital Tunku Ampuan Besar Tuanku Aishah Rohani, Universiti Kebangsaan Malaysia (UKM) Specialist Children’s Hospital, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Siobhan O. Burns
- Department of Immunology, Royal Free London National Heath System (NHS) Foundation Trust, London, United Kingdom
- Institute of Immunity and Transplantation, University College London, London, United Kingdom
| | - Martin van Hagen
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Immunology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Martine Pergent
- International Patient Organisation for Primary Immunodeficiencies (IPOPI), Brussels, Belgium
| | - Nizar Mahlaoui
- Pediatric Hematology-Immunology and Rheumatology Unit, Necker-Enfants malades University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
- French National Reference Center for Primary Immune Deficiencies (CEREDIH), Necker-Enfants malades University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
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20
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Rönn T, Perfilyev A, Oskolkov N, Ling C. Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets. Sci Rep 2024; 14:14637. [PMID: 38918439 PMCID: PMC11199577 DOI: 10.1038/s41598-024-64846-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/13/2024] [Indexed: 06/27/2024] Open
Abstract
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
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Affiliation(s)
- Tina Rönn
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Nikolay Oskolkov
- Science for Life Laboratory, Department of Biology, National Bioinformatics Infrastructure Sweden, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden.
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21
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Wang Y, Jin C, Li H, Liang X, Zhao C, Wu N, Yue M, Zhao L, Yu H, Wang Q, Ge Y, Huo M, Lv X, Zhang L, Zhao G, Gai Z. Gut microbiota-metabolite interactions meditate the effect of dietary patterns on precocious puberty. iScience 2024; 27:109887. [PMID: 38784002 PMCID: PMC11112371 DOI: 10.1016/j.isci.2024.109887] [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: 01/30/2024] [Revised: 03/21/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
Precocious puberty, a pediatric endocrine disorder classified as central precocious puberty (CPP) or peripheral precocious puberty (PPP), is influenced by diet, gut microbiota, and metabolites, but the specific mechanisms remain unclear. Our study found that increased alpha-diversity and abundance of short-chain fatty acid-producing bacteria led to elevated levels of luteinizing hormone and follicle-stimulating hormone, contributing to precocious puberty. The integration of specific microbiota and metabolites has potential diagnostic value for precocious puberty. The Prevotella genus-controlled interaction factor, influenced by complex carbohydrate consumption, mediated a reduction in estradiol levels. Interactions between obesity-related bacteria and metabolites mediated the beneficial effect of seafood in reducing luteinizing hormone levels, reducing the risk of obesity-induced precocious puberty, and preventing progression from PPP to CPP. This study provides valuable insights into the complex interplay between diet, gut microbiota and metabolites in the onset, development and clinical classification of precocious puberty and warrants further investigation.
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Affiliation(s)
- Ying Wang
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Chuandi Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Microbiome-X, National Institute of Health Data Science of China, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Hongying Li
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Xiangrong Liang
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Changying Zhao
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Microbiome-X, National Institute of Health Data Science of China, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Nan Wu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Microbiome-X, National Institute of Health Data Science of China, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Min Yue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Microbiome-X, National Institute of Health Data Science of China, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Lu Zhao
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Central Laboratory, Weifang People’s Hospital/The First Affiliated Hospital of Shandong Second Medical university, Weifang 261000, China
- Shandong Laibo Biotechnology Co., Ltd., Jinan 250101, China
| | - Han Yu
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Qian Wang
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Yongsheng Ge
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Meiling Huo
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Xin Lv
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Lehai Zhang
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
| | - Guoping Zhao
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Microbiome-X, National Institute of Health Data Science of China, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
| | - Zhongtao Gai
- Children’s Hospital Affiliated to Shandong University, Shandong University, Jinan 250022, China
- Jinan Children’s Hospital, Jinan 250022, China
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22
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Caldi Gomes L, Hänzelmann S, Hausmann F, Khatri R, Oller S, Parvaz M, Tzeplaeff L, Pasetto L, Gebelin M, Ebbing M, Holzapfel C, Columbro SF, Scozzari S, Knöferle J, Cordts I, Demleitner AF, Deschauer M, Dufke C, Sturm M, Zhou Q, Zelina P, Sudria-Lopez E, Haack TB, Streb S, Kuzma-Kozakiewicz M, Edbauer D, Pasterkamp RJ, Laczko E, Rehrauer H, Schlapbach R, Carapito C, Bonetto V, Bonn S, Lingor P. Multiomic ALS signatures highlight subclusters and sex differences suggesting the MAPK pathway as therapeutic target. Nat Commun 2024; 15:4893. [PMID: 38849340 PMCID: PMC11161513 DOI: 10.1038/s41467-024-49196-y] [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: 08/03/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a debilitating motor neuron disease and lacks effective disease-modifying treatments. This study utilizes a comprehensive multiomic approach to investigate the early and sex-specific molecular mechanisms underlying ALS. By analyzing the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, alongside four transgenic mouse models (C9orf72-, SOD1-, TDP-43-, and FUS-ALS), we have uncovered significant molecular alterations associated with the disease. Here, we show that males exhibit more pronounced changes in molecular pathways compared to females. Our integrated analysis of transcriptomes, (phospho)proteomes, and miRNAomes also identified distinct ALS subclusters in humans, characterized by variations in immune response, extracellular matrix composition, mitochondrial function, and RNA processing. The molecular signatures of human subclusters were reflected in specific mouse models. Our study highlighted the mitogen-activated protein kinase (MAPK) pathway as an early disease mechanism. We further demonstrate that trametinib, a MAPK inhibitor, has potential therapeutic benefits in vitro and in vivo, particularly in females, suggesting a direction for developing targeted ALS treatments.
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Affiliation(s)
- Lucas Caldi Gomes
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Sonja Hänzelmann
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fabian Hausmann
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Robin Khatri
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sergio Oller
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mojan Parvaz
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Laura Tzeplaeff
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Laura Pasetto
- Research Center for ALS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Marie Gebelin
- Laboratoire de Spectrométrie de Masse Bio-Organique, Université de Strasbourg, Infrastructure Nationale de Protéomique, Strasbourg, France
| | - Melanie Ebbing
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Constantin Holzapfel
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Serena Scozzari
- Research Center for ALS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Johanna Knöferle
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Isabell Cordts
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Antonia F Demleitner
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Marcus Deschauer
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany
| | - Claudia Dufke
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Qihui Zhou
- German Center for Neurodegenerative Diseases (DZNE), München, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Pavol Zelina
- Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Emma Sudria-Lopez
- Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Sebastian Streb
- Functional Genomics Center Zürich, ETH Zürich and University of Zürich, Zürich, Switzerland
| | | | - Dieter Edbauer
- German Center for Neurodegenerative Diseases (DZNE), München, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - R Jeroen Pasterkamp
- Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Endre Laczko
- Functional Genomics Center Zürich, ETH Zürich and University of Zürich, Zürich, Switzerland
| | - Hubert Rehrauer
- Functional Genomics Center Zürich, ETH Zürich and University of Zürich, Zürich, Switzerland
| | - Ralph Schlapbach
- Functional Genomics Center Zürich, ETH Zürich and University of Zürich, Zürich, Switzerland
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse Bio-Organique, Université de Strasbourg, Infrastructure Nationale de Protéomique, Strasbourg, France
| | - Valentina Bonetto
- Research Center for ALS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Stefan Bonn
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Paul Lingor
- Technical University of Munich, School of Medicine, rechts der Isar Hospital, Clinical Department of Neurology, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), München, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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23
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Deek RA, Ma S, Lewis J, Li H. Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data. eLife 2024; 13:e88956. [PMID: 38832759 PMCID: PMC11149933 DOI: 10.7554/elife.88956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/10/2024] [Indexed: 06/05/2024] Open
Abstract
Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.
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Affiliation(s)
- Rebecca A Deek
- Department of Biostatistics, University of PittsburghPittsburghUnited States
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt School of MedicineNashvilleUnited States
| | - James Lewis
- Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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24
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Immune signatures of disease stages and outcomes in myocardial infarction. Nat Med 2024; 30:1539-1540. [PMID: 38789646 DOI: 10.1038/s41591-024-02954-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
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25
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Afroz S, Islam N, Habib MA, Reza MS, Ashad Alam M. Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network. Methods 2024; 226:138-150. [PMID: 38670415 DOI: 10.1016/j.ymeth.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 04/02/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.
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Affiliation(s)
- Sabrin Afroz
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Nadira Islam
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Md Ahsan Habib
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh; Statistical Learning Group, Bangladesh
| | - Md Selim Reza
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA; Statistical Learning Group, Bangladesh
| | - Md Ashad Alam
- Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA 70121, USA; Statistical Learning Group, Bangladesh.
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26
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Pekayvaz K, Losert C, Knottenberg V, Gold C, van Blokland IV, Oelen R, Groot HE, Benjamins JW, Brambs S, Kaiser R, Gottschlich A, Hoffmann GV, Eivers L, Martinez-Navarro A, Bruns N, Stiller S, Akgöl S, Yue K, Polewka V, Escaig R, Joppich M, Janjic A, Popp O, Kobold S, Petzold T, Zimmer R, Enard W, Saar K, Mertins P, Huebner N, van der Harst P, Franke LH, van der Wijst MGP, Massberg S, Heinig M, Nicolai L, Stark K. Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes. Nat Med 2024; 30:1696-1710. [PMID: 38773340 PMCID: PMC11186793 DOI: 10.1038/s41591-024-02953-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 03/26/2024] [Indexed: 05/23/2024]
Abstract
Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.
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Grants
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- Deutsches Zentrum fr Herz-Kreislaufforschung (Deutsches Zentrum fr Herz-Kreislaufforschung e.V.)
- Deutsche Herzstiftung e.V., Frankfurt a.M. Institutional Strategy LMUexcellent of LMU Munich Else-Krner-Fresenius Stiftung DFG Clinician Scientist Programme PRIME DZHK Sule B Antrag DZHK B 21-014 SE
- Was supported by the Helmholtz Association under the joint research school ;Munich School for Data Science MUDS
- DFG GO 3823/1-1, grant number: 510821390 Frderprogramm fr Forschung und Lehre der Medizinischen Fakultt der LMU the Bavarian Cancer Research Center (BZKF) Else Kroner-Fresenius-Stiftung
- Was supported by a grant from the Frderprogramm fur Forschung und Lehre (FFoLe) of the Ludwig Maximilian University (LMU) of Munich.
- DFG SFB 1123, Z02
- DFG EN 1093/2-1
- DFG KO5055-2-1 and KO5055/3-1 the Bavarian Cancer Research Center (BZKF) the international doctoral program i-Target: immunotargeting of cancer the Melanoma Research Alliance (grant number 409510), Marie Sklodowska-Curie Training Network for Optimizing Adoptive T Cell Therapy of Cancer (funded by the Horizon 2020 programme of the European Union; grant 955575), Else Kroner-Fresenius-Stiftung (IOLIN), German Cancer Aid (AvantCAR.de), the Wilhelm-Sander-Stiftung, Ernst Jung Stiftung, Institutional Strategy LMUexcellent of LMU Munich (within the framework of the German Excellence Initiative), the Go-Bio-Initiative, the m4-Award of the Bavarian Ministry for Economical Affairs, Bundesministerium fur Bildung und Forschung, European Research Council (Starting Grant 756017 and PoC Grant 101100460, by the SFB-TRR 338/1 2021452881907, Fritz-Bender Foundation, Deutsche Jose#x0301; Carreras Leuk#x00E4;mie Stiftung, Hector Foundation, the Bavarian Research Foundation, the Bruno and Helene J#x00F6;ster Foundation (360#x00B0; CAR)
- T.P. from the DFG (PE 2704/3-1)
- DFG SFB1243, A14 DFG EN 1093/2-1,
- DZHK Säule B Antrag DZHK B 21-014 SE
- DZHK Säule B Antrag DZHK B 21-014 SE DFG SFB-1470-B03 the Chan Zuckerberg Foundation ERC Advanced Grant under the European Union Horizon 2020 Research and Innovation Program (AdG788970)
- Deutsche Forschungsgemeinschaft (DFG) SFB 914, B02 and Z01 DFG SFB 1123, B06 DFG SFB1321, P10 DFG FOR 2033 ERC-2018-ADG German Centre for Cardiovascular Research (DZHK) MHA 1.4VD
- DZHK project 81Z0600106 Supported by the Chan Zuckerberg Foundation
- DZHK S#x00E4;ule B Antrag DZHK B 21-014 SE Deutsche Herzstiftung e.V., Frankfurt a.M. DFG SFB 1123, B06 DFG NI 2219/2-1 Corona Foundation German Centre for Cardiovascular Research (DZHK) Clinician Scientist Programme the Ernst und Berta Grimmke Stiftung the GTH Junior research grant
- DZHK partner site project Deutsche Forschungsgemeinschaft (DFG) SFB 914, B02 DFG SFB 1123, A07 DFG SFB 359, A03 ERC grant 947611
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Affiliation(s)
- Kami Pekayvaz
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
| | - Corinna Losert
- Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | | | - Christoph Gold
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Irene V van Blokland
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Roy Oelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hilde E Groot
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Walter Benjamins
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sophia Brambs
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Rainer Kaiser
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Adrian Gottschlich
- Department of Medicine III, LMU University Hospital, Munich, Germany
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Gordon Victor Hoffmann
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Luke Eivers
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | | | - Nils Bruns
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Susanne Stiller
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Sezer Akgöl
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Keyang Yue
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Vivien Polewka
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Raphael Escaig
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
| | - Markus Joppich
- Department of Informatics, Ludwig-Maximilian University, Munich, Germany
| | - Aleksandar Janjic
- Anthropology and Human Genomics, Faculty of Biology, Ludwig-Maximilian University, Munich, Germany
| | - Oliver Popp
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Sebastian Kobold
- Division of Clinical Pharmacology, LMU University Hospital, Member of the German Center for Lung Research (DZL), Munich, Germany
- German Cancer Consortium (DKTK), a partnership between DKFZ and LMU University Hospital, Partner Site Munich, Munich, Germany
- Einheit für Klinische Pharmakologie (EKLiP), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Tobias Petzold
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf Zimmer
- Department of Informatics, Ludwig-Maximilian University, Munich, Germany
| | - Wolfgang Enard
- Anthropology and Human Genomics, Faculty of Biology, Ludwig-Maximilian University, Munich, Germany
| | - Kathrin Saar
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Philipp Mertins
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Norbert Huebner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lude H Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Monique G P van der Wijst
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Steffen Massberg
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Matthias Heinig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
- Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany.
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | - Leo Nicolai
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
| | - Konstantin Stark
- Medizinische Klinik und Poliklinik I, LMU University Hospital, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
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27
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Newman NK, Macovsky MS, Rodrigues RR, Bruce AM, Pederson JW, Padiadpu J, Shan J, Williams J, Patil SS, Dzutsev AK, Shulzhenko N, Trinchieri G, Brown K, Morgun A. Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions. Nat Protoc 2024; 19:1750-1778. [PMID: 38472495 DOI: 10.1038/s41596-024-00960-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/29/2023] [Indexed: 03/14/2024]
Abstract
We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .
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Affiliation(s)
- Nolan K Newman
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | | | - Richard R Rodrigues
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Amanda M Bruce
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jacob W Pederson
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Jyothi Padiadpu
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Jigui Shan
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Joshua Williams
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sankalp S Patil
- College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Amiran K Dzutsev
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Natalia Shulzhenko
- Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Giorgio Trinchieri
- Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Kevin Brown
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
| | - Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
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28
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Sibilio P, Conte F, Huang Y, Castaldi PJ, Hersh CP, DeMeo DL, Silverman EK, Paci P. Correlation-based network integration of lung RNA sequencing and DNA methylation data in chronic obstructive pulmonary disease. Heliyon 2024; 10:e31301. [PMID: 38807864 PMCID: PMC11130701 DOI: 10.1016/j.heliyon.2024.e31301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 05/08/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis.
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Affiliation(s)
- Pasquale Sibilio
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Yichen Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Karolinska Institutet, 17177, Stockholm, Sweden
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29
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Mohr AE, Sweazea KL, Bowes DA, Jasbi P, Whisner CM, Sears DD, Krajmalnik-Brown R, Jin Y, Gu H, Klein-Seetharaman J, Arciero KM, Gumpricht E, Arciero PJ. Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction. Nat Commun 2024; 15:4155. [PMID: 38806467 PMCID: PMC11133430 DOI: 10.1038/s41467-024-48355-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
The gut microbiome (GM) modulates body weight/composition and gastrointestinal functioning; therefore, approaches targeting resident gut microbes have attracted considerable interest. Intermittent fasting (IF) and protein pacing (P) regimens are effective in facilitating weight loss (WL) and enhancing body composition. However, the interrelationships between IF- and P-induced WL and the GM are unknown. The current randomized controlled study describes distinct fecal microbial and plasma metabolomic signatures between combined IF-P (n = 21) versus a heart-healthy, calorie-restricted (CR, n = 20) diet matched for overall energy intake in free-living human participants (women = 27; men = 14) with overweight/obesity for 8 weeks. Gut symptomatology improves and abundance of Christensenellaceae microbes and circulating cytokines and amino acid metabolites favoring fat oxidation increase with IF-P (p < 0.05), whereas metabolites associated with a longevity-related metabolic pathway increase with CR (p < 0.05). Differences indicate GM and metabolomic factors play a role in WL maintenance and body composition. This novel work provides insight into the GM and metabolomic profile of participants following an IF-P or CR diet and highlights important differences in microbial assembly associated with WL and body composition responsiveness. These data may inform future GM-focused precision nutrition recommendations using larger sample sizes of longer duration. Trial registration, March 6, 2020 (ClinicalTrials.gov as NCT04327141), based on a previous randomized intervention trial.
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Affiliation(s)
- Alex E Mohr
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA
| | - Karen L Sweazea
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA
- Center for Evolution and Medicine, College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ, USA
| | - Devin A Bowes
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA
| | - Paniz Jasbi
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ, USA
| | - Corrie M Whisner
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA
| | - Dorothy D Sears
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Rosa Krajmalnik-Brown
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ, USA
| | - Yan Jin
- Center of Translational Science, Florida International University, Port St. Lucie, FL, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- Center of Translational Science, Florida International University, Port St. Lucie, FL, USA
| | - Judith Klein-Seetharaman
- College of Health Solutions, Arizona State University, Phoenix, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Karen M Arciero
- Human Nutrition and Metabolism Laboratory, Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Springs, NY, USA
| | | | - Paul J Arciero
- Human Nutrition and Metabolism Laboratory, Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Springs, NY, USA.
- School of Health and Rehabilitation Sciences, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA, USA.
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30
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Vieira FG, Bispo R, Lopes MB. Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers. Bioinform Biol Insights 2024; 18:11779322241249563. [PMID: 38812741 PMCID: PMC11135104 DOI: 10.1177/11779322241249563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 04/09/2024] [Indexed: 05/31/2024] Open
Abstract
Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A, and HEPN1. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.
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Affiliation(s)
- Francisca G Vieira
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
| | - Regina Bispo
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- Department of Mathematics, NOVA School of Science and Technology, Caparica, Portugal
| | - Marta B Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- Department of Mathematics, NOVA School of Science and Technology, Caparica, Portugal
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
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31
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Daskalakis NP, Iatrou A, Chatzinakos C, Jajoo A, Snijders C, Wylie D, DiPietro CP, Tsatsani I, Chen CY, Pernia CD, Soliva-Estruch M, Arasappan D, Bharadwaj RA, Collado-Torres L, Wuchty S, Alvarez VE, Dammer EB, Deep-Soboslay A, Duong DM, Eagles N, Huber BR, Huuki L, Holstein VL, Logue ΜW, Lugenbühl JF, Maihofer AX, Miller MW, Nievergelt CM, Pertea G, Ross D, Sendi MSE, Sun BB, Tao R, Tooke J, Wolf EJ, Zeier Z, Berretta S, Champagne FA, Hyde T, Seyfried NT, Shin JH, Weinberger DR, Nemeroff CB, Kleinman JE, Ressler KJ. Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood. Science 2024; 384:eadh3707. [PMID: 38781393 PMCID: PMC11203158 DOI: 10.1126/science.adh3707] [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: 03/22/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.
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Affiliation(s)
- Nikolaos P. Daskalakis
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Artemis Iatrou
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Chris Chatzinakos
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, 11203, USA
- VA New York Harbor Healthcare System, Brooklyn, NY, 11209, USA
| | - Aarti Jajoo
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Clara Snijders
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Dennis Wylie
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Christopher P. DiPietro
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Ioulia Tsatsani
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | | | - Cameron D. Pernia
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Marina Soliva-Estruch
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Dhivya Arasappan
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Rahul A. Bharadwaj
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Stefan Wuchty
- Departments of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
| | - Victor E. Alvarez
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- VA Bedford Healthcare System, Bedford, MA, 01730, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Eric B Dammer
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Amy Deep-Soboslay
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Duc M. Duong
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Nick Eagles
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Bertrand R. Huber
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Louise Huuki
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Vincent L Holstein
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Μark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Justina F. Lugenbühl
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Adam X. Maihofer
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Mark W. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Geo Pertea
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Deanna Ross
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
| | - Mohammad S. E Sendi
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Ran Tao
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - James Tooke
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Erika J. Wolf
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Zane Zeier
- Department of Psychiatry & Behavioral Sciences, Center for Therapeutic Innovation, University of Miami Miller School of Medicine; Miami, FL, 33136, USA
| | | | - Sabina Berretta
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Thomas Hyde
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Charles B. Nemeroff
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin; Austin, TX, 78712, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Kerry J. Ressler
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
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Wu S, Zhou H, Chen D, Lu Y, Li Y, Qiao J. Multi-omic analysis tools for microbial metabolites prediction. Brief Bioinform 2024; 25:bbae264. [PMID: 38859767 PMCID: PMC11165163 DOI: 10.1093/bib/bbae264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Haonan Zhou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yutong Lu
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yanni Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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33
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Novoloaca A, Broc C, Beloeil L, Yu WH, Becker J. Comparative analysis of integrative classification methods for multi-omics data. Brief Bioinform 2024; 25:bbae331. [PMID: 38985929 PMCID: PMC11234228 DOI: 10.1093/bib/bbae331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/31/2024] [Indexed: 07/12/2024] Open
Abstract
Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.
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Affiliation(s)
- Alexei Novoloaca
- BIOASTER Research Institute, 40 avenue Tony Garnier, F-69007 Lyon, France
| | - Camilo Broc
- BIOASTER Research Institute, 40 avenue Tony Garnier, F-69007 Lyon, France
| | - Laurent Beloeil
- BIOASTER Research Institute, 40 avenue Tony Garnier, F-69007 Lyon, France
| | - Wen-Han Yu
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, MA 02139, United States
| | - Jérémie Becker
- BIOASTER Research Institute, 40 avenue Tony Garnier, F-69007 Lyon, France
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Aminu M, Hong L, Vokes N, Schmidt ST, Saad M, Zhu B, Le X, Tina C, Sheshadri A, Wang B, Jaffray D, Futreal A, Lee JJ, Byers LA, Gibbons D, Heymach J, Chen K, Cheng C, Zhang J, Wu J. Joint multi-omics discriminant analysis with consistent representation learning using PANDA. RESEARCH SQUARE 2024:rs.3.rs-4353037. [PMID: 38798352 PMCID: PMC11118856 DOI: 10.21203/rs.3.rs-4353037/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.
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Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephanie T. Schmidt
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bo Zhu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cascone Tina
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bo Wang
- Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Andy Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lauren A. Byers
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Don Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chao Cheng
- Department of Medicine, Institution of Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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35
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Braytee A, He S, Tang S, Sun Y, Jiang X, Yu X, Khatri I, Chaturvedi K, Prasad M, Anaissi A. Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis. Sci Rep 2024; 14:11263. [PMID: 38760420 PMCID: PMC11101416 DOI: 10.1038/s41598-024-59670-8] [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: 12/18/2023] [Accepted: 04/12/2024] [Indexed: 05/19/2024] Open
Abstract
Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.
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Affiliation(s)
- Ali Braytee
- School of Computer Science, University of Technology Sydney, Ultimo, 2007, Australia.
| | - Sam He
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
| | - Shuxian Tang
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
| | - Yuxuan Sun
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
| | - Xiaoying Jiang
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
| | - Xuanding Yu
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
| | - Inder Khatri
- Department of Applied Mathematics, Delhi Technological University, Delhi, 110042, India
| | - Kunal Chaturvedi
- School of Computer Science, University of Technology Sydney, Ultimo, 2007, Australia
| | - Mukesh Prasad
- School of Computer Science, University of Technology Sydney, Ultimo, 2007, Australia
| | - Ali Anaissi
- School of Computer Science, The University of Sydney, Camperdown, 2006, Australia
- TD School, University of Technology Sydney, Ultimo, 2007, Australia
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Labarga A, Martínez-Gonzalez J, Barajas M. Integrative Multi-Omics Analysis for Etiology Classification and Biomarker Discovery in Stroke: Advancing towards Precision Medicine. BIOLOGY 2024; 13:338. [PMID: 38785820 PMCID: PMC11149453 DOI: 10.3390/biology13050338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024]
Abstract
Recent advancements in high-throughput omics technologies have opened new avenues for investigating stroke at the molecular level and elucidating the intricate interactions among various molecular components. We present a novel approach for multi-omics data integration on knowledge graphs and have applied it to a stroke etiology classification task of 30 stroke patients through the integrative analysis of DNA methylation and mRNA, miRNA, and circRNA. This approach has demonstrated promising performance as compared to other existing single technology approaches.
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Affiliation(s)
- Alberto Labarga
- Health Science Department, Public University of Navarra, 31006 Pamplona, Spain;
| | | | - Miguel Barajas
- Health Science Department, Public University of Navarra, 31006 Pamplona, Spain;
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37
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Wang H, Liu Z, Ma X. Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data. IEEE J Biomed Health Inform 2024; 28:3134-3145. [PMID: 38709615 DOI: 10.1109/jbhi.2024.3370868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.
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38
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Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [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: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.
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Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
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39
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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40
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Ewald JD, Zhou G, Lu Y, Kolic J, Ellis C, Johnson JD, Macdonald PE, Xia J. Web-based multi-omics integration using the Analyst software suite. Nat Protoc 2024; 19:1467-1497. [PMID: 38355833 DOI: 10.1038/s41596-023-00950-4] [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: 04/17/2023] [Accepted: 11/21/2023] [Indexed: 02/16/2024]
Abstract
The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.
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Affiliation(s)
- Jessica D Ewald
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Jelena Kolic
- Life Sciences Institute, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cara Ellis
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
| | - James D Johnson
- Life Sciences Institute, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Patrick E Macdonald
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada.
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Groeneveld CS, Sanchez-Quiles V, Dufour F, Shi M, Dingli F, Nicolle R, Chapeaublanc E, Poullet P, Jeffery D, Krucker C, Maillé P, Vacherot F, Vordos D, Benhamou S, Lebret T, Micheau O, Zinovyev A, Loew D, Allory Y, de Reyniès A, Bernard-Pierrot I, Radvanyi F. Proteogenomic Characterization of Bladder Cancer Reveals Sensitivity to Apoptosis Induced by Tumor Necrosis Factor-related Apoptosis-inducing Ligand in FGFR3-mutated Tumors. Eur Urol 2024; 85:483-494. [PMID: 37380559 DOI: 10.1016/j.eururo.2023.05.037] [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: 12/13/2022] [Revised: 04/26/2023] [Accepted: 05/26/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Molecular understanding of muscle-invasive (MIBC) and non-muscle-invasive (NMIBC) bladder cancer is currently based primarily on transcriptomic and genomic analyses. OBJECTIVE To conduct proteogenomic analyses to gain insights into bladder cancer (BC) heterogeneity and identify underlying processes specific to tumor subgroups and therapeutic outcomes. DESIGN, SETTING, AND PARTICIPANTS Proteomic data were obtained for 40 MIBC and 23 NMIBC cases for which transcriptomic and genomic data were already available. Four BC-derived cell lines harboring FGFR3 alterations were tested with interventions. INTERVENTION Recombinant tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), second mitochondrial-derived activator of caspases mimetic (birinapant), pan-FGFR inhibitor (erdafitinib), and FGFR3 knockdown. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Proteomic groups from unsupervised analyses (uPGs) were characterized using clinicopathological, proteomic, genomic, transcriptomic, and pathway enrichment analyses. Additional enrichment analyses were performed for FGFR3-mutated tumors. Treatment effects on cell viability for FGFR3-altered cell lines were evaluated. Synergistic treatment effects were evaluated using the zero interaction potency model. RESULTS AND LIMITATIONS Five uPGs, covering both NMIBC and MIBC, were identified and bore coarse-grained similarity to transcriptomic subtypes underlying common features of these different entities; uPG-E was associated with the Ta pathway and enriched in FGFR3 mutations. Our analyses also highlighted enrichment of proteins involved in apoptosis in FGFR3-mutated tumors, not captured through transcriptomics. Genetic and pharmacological inhibition demonstrated that FGFR3 activation regulates TRAIL receptor expression and sensitizes cells to TRAIL-mediated apoptosis, further increased by combination with birinapant. CONCLUSIONS This proteogenomic study provides a comprehensive resource for investigating NMIBC and MIBC heterogeneity and highlights the potential of TRAIL-induced apoptosis as a treatment option for FGFR3-mutated bladder tumors, warranting a clinical investigation. PATIENT SUMMARY We integrated proteomics, genomics, and transcriptomics to refine molecular classification of bladder cancer, which, combined with clinical and pathological classification, should lead to more appropriate management of patients. Moreover, we identified new biological processes altered in FGFR3-mutated tumors and showed that inducing apoptosis represents a new potential therapeutic option.
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Affiliation(s)
- Clarice S Groeneveld
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France; Centre de Recherche des Cordeliers, AP-HP, Université Paris Cité, Paris, France
| | - Virginia Sanchez-Quiles
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France
| | - Florent Dufour
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France; Inovarion, Paris, France
| | - Mingjun Shi
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France; Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Florent Dingli
- Centre de Recherche, CurieCoreTech Mass Spectrometry Proteomics, Institut Curie, PSL Research University, Paris, France
| | - Rémy Nicolle
- Centre de Recherche sur l'Inflammation (CRI), INSERM, U1149, CNRS, ERL 8252, Université Paris Cité, Paris, France
| | - Elodie Chapeaublanc
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France
| | - Patrick Poullet
- INSERM U900, MINES ParisTech, Institut Curie, PSL Research University, Paris, France
| | - Daniel Jeffery
- Urology Medico-Scientific Program, Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Clémentine Krucker
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France
| | - Pascale Maillé
- Département de Pathologie, Hôpital Henri Mondor, APHP, Créteil, France
| | | | - Dimitri Vordos
- Service d'Urologie, Hôpital Henri Mondor, APHP, Créteil, France
| | | | - Thierry Lebret
- Service d'Urologie, Hôpital Foch, UVSQ, Université Paris-Saclay, Suresnes, France
| | - Olivier Micheau
- INSERM, LNC UMR1231, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrei Zinovyev
- INSERM U900, MINES ParisTech, Institut Curie, PSL Research University, Paris, France
| | - Damarys Loew
- Centre de Recherche, CurieCoreTech Mass Spectrometry Proteomics, Institut Curie, PSL Research University, Paris, France
| | - Yves Allory
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France; Department of Pathology, Institut Curie, UVSQ, Université Paris-Saclay, Saint-Cloud, France
| | - Aurélien de Reyniès
- Centre de Recherche des Cordeliers, AP-HP, Université Paris Cité, Paris, France
| | - Isabelle Bernard-Pierrot
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France
| | - François Radvanyi
- Equipe labellisée Ligue Contre le Cancer, CNRS, UMR144, Institut Curie, PSL Research University, Paris, France.
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Ladakis DC, Pedrini E, Reyes-Mantilla MI, Sanjayan M, Smith MD, Fitzgerald KC, Pardo CA, Reich DS, Absinta M, Bhargava P. Metabolomics of Multiple Sclerosis Lesions Demonstrates Lipid Changes Linked to Alterations in Transcriptomics-Based Cellular Profiles. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200219. [PMID: 38547430 DOI: 10.1212/nxi.0000000000200219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVES People with multiple sclerosis (MS) have a dysregulated circulating metabolome, but the metabolome of MS brain lesions has not been studied. The aims of this study were to identify differences in the brain tissue metabolome in MS compared with controls and to assess its association with the cellular profile of corresponding tissue. METHODS MS tissues included samples from the edge and core of chronic active or inactive lesions and periplaque white matter (WM). Control specimens were obtained from normal WM. Metabolomic analysis was performed using mass-spectrometry coupled with liquid/gas chromatography and subsequently integrated with single-nucleus RNA-sequencing data by correlating metabolite abundances with relative cell counts, as well as individual genes using Multiomics Factor Analysis (MOFA). RESULTS Seventeen samples from 5 people with secondary progressive MS and 8 samples from 6 controls underwent metabolomic profiling identifying 783 metabolites. MS lesions had higher levels of sphingosines (false discovery rate-adjusted p-value[q] = 2.88E-05) and sphingomyelins and ceramides (q = 2.15E-07), but lower nucleotide (q = 0.05), energy (q = 0.001), lysophospholipid (q = 1.86E-07), and monoacylglycerol (q = 0.04) metabolite levels compared with control WM. Periplaque WM had elevated sphingomyelins and ceramides (q = 0.05) and decreased energy metabolites (q = 0.01) and lysophospholipids (q = 0.05) compared with control WM. Sphingolipids and membrane lipid metabolites were positively correlated with astrocyte and immune cell abundances and negatively correlated with oligodendrocytes. On the other hand, long-chain fatty acid, endocannabinoid, and monoacylglycerol pathways were negatively correlated with astrocyte and immune cell populations and positively correlated with oligodendrocytes. MOFA demonstrated associations between differentially expressed metabolites and genes involved in myelination and lipid biosynthesis. DISCUSSION MS lesions and perilesional WM demonstrated a significantly altered metabolome compared with control WM. Many of the altered metabolites were associated with altered cellular composition and gene expression, indicating an important role of lipid metabolism in chronic neuroinflammation in MS.
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Affiliation(s)
- Dimitrios C Ladakis
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Edoardo Pedrini
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Maria I Reyes-Mantilla
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Muraleetharan Sanjayan
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Matthew D Smith
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Kathryn C Fitzgerald
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Carlos A Pardo
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Daniel S Reich
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Martina Absinta
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
| | - Pavan Bhargava
- From the Department of Neurology (D.C.L., M.I.R.-M., M.S., M.D.S., K.C.F., C.A.P., D.S.R., M.A., P.B.), Johns Hopkins University School of Medicine, Baltimore, MD; Translational Neuropathology Unit (E.P., M.A.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy and Translational Neuroradiology Section (D.S.R., M.A.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
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Kiseleva OI, Pyatnitskiy MA, Arzumanian VA, Kurbatov IY, Ilinsky VV, Ilgisonis EV, Plotnikova OA, Sharafetdinov KK, Tutelyan VA, Nikityuk DB, Ponomarenko EA, Poverennaya EV. Multiomics Picture of Obesity in Young Adults. BIOLOGY 2024; 13:272. [PMID: 38666884 PMCID: PMC11048234 DOI: 10.3390/biology13040272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/02/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024]
Abstract
Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.
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Affiliation(s)
- Olga I. Kiseleva
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
| | - Mikhail A. Pyatnitskiy
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
- Faculty of Computer Science, National Research University Higher School of Economics, Moscow 101000, Russia
| | | | - Ilya Y. Kurbatov
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
| | | | | | - Oksana A. Plotnikova
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
| | - Khaider K. Sharafetdinov
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- Russian Medical Academy of Continuing Professional Education, Ministry of Health of the Russian Federation, Moscow 125993, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
| | - Victor A. Tutelyan
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
| | - Dmitry B. Nikityuk
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
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Shannon CP, Lee AH, Tebbutt SJ, Singh A. A Commentary on Multi-omics Data Integration in Systems Vaccinology. J Mol Biol 2024; 436:168522. [PMID: 38458605 DOI: 10.1016/j.jmb.2024.168522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Affiliation(s)
| | - Amy Hy Lee
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
| | - Scott J Tebbutt
- PROOF Centre of Excellence, Vancouver, Canada; Department of Medicine, The University of British Columbia, Vancouver, Canada; Centre for Heart Lung Innovation, Vancouver, Canada
| | - Amrit Singh
- Centre for Heart Lung Innovation, Vancouver, Canada; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, Canada.
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Acharya D, Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics 2024:elae013. [PMID: 38600757 DOI: 10.1093/bfgp/elae013] [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: 10/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.
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Affiliation(s)
- Debabrata Acharya
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
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Zhang W, Mou M, Hu W, Lu M, Zhang H, Zhang H, Luo Y, Xu H, Tao L, Dai H, Gao J, Zhu F. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery. J Chem Inf Model 2024; 64:2720-2732. [PMID: 38373720 DOI: 10.1021/acs.jcim.4c00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.
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Affiliation(s)
- Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Hu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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De Paepe E, Plekhova V, Vangeenderhuysen P, Baeck N, Bullens D, Claeys T, De Graeve M, Kamoen K, Notebaert A, Van de Wiele T, Van Den Broeck W, Vanlede K, Van Winckel M, Vereecke L, Elliott C, Cox E, Vanhaecke L. Integrated gut metabolome and microbiome fingerprinting reveals that dysbiosis precedes allergic inflammation in IgE-mediated pediatric cow's milk allergy. Allergy 2024; 79:949-963. [PMID: 38193259 DOI: 10.1111/all.16005] [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: 07/08/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND IgE-mediated cow's milk allergy (IgE-CMA) is one of the first allergies to arise in early childhood and may result from exposure to various milk allergens, of which β-lactoglobulin (BLG) and casein are the most important. Understanding the underlying mechanisms behind IgE-CMA is imperative for the discovery of novel biomarkers and the design of innovative treatment and prevention strategies. METHODS We report a longitudinal in vivo murine model, in which two mice strains (BALB/c and C57Bl/6) were sensitized to BLG using either cholera toxin or an oil emulsion (n = 6 per group). After sensitization, mice were challenged orally, their clinical signs monitored, antibody (IgE and IgG1) and cytokine levels (IL-4 and IFN-γ) measured, and fecal samples subjected to metabolomics. The results of the murine models were further extrapolated to fecal microbiome-metabolome data from our population of IgE-CMA (n = 22) and healthy (n = 23) children (Trial: NCT04249973), on which polar metabolomics, lipidomics and 16S rRNA metasequencing were performed. In vitro gastrointestinal digestions and multi-omics corroborated the microbial origin of proposed metabolic changes. RESULTS During mice sensitization, we observed multiple microbially derived metabolic alterations, most importantly bile acid, energy and tryptophan metabolites, that preceded allergic inflammation. We confirmed microbial dysbiosis, and its associated effect on metabolic alterations in our patient cohort, through in vitro digestions and multi-omics, which was accompanied by metabolic signatures of low-grade inflammation. CONCLUSION Our results indicate that gut dysbiosis precedes allergic inflammation and nurtures a chronic low-grade inflammation in children on elimination diets, opening important new opportunities for future prevention and treatment strategies.
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Affiliation(s)
- Ellen De Paepe
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, Merelbeke, Belgium
| | - Vera Plekhova
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, Merelbeke, Belgium
| | - Pablo Vangeenderhuysen
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, Merelbeke, Belgium
| | - Nele Baeck
- Department of Pediatrics, Pediatric Gastroenterology, AZ Jan Palfijn Ghent, Ghent, Belgium
| | - Dominique Bullens
- Department of Microbiology, Immunology and Transplantation, Allergy and Immunology Research Group, KU Leuven, Leuven, Belgium
- Clinical Division of Pediatrics, UZ Leuven, Leuven, Belgium
| | - Tania Claeys
- Department of Pediatrics, Pediatric Gastroenterology and Nutrition & General Pediatric Medicine, AZ Sint-Jan Bruges, Bruges, Belgium
| | - Marilyn De Graeve
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, Merelbeke, Belgium
| | - Kristien Kamoen
- Department of Pediatrics, Maria Middelares Ghent, Ghent, Belgium
| | - Anneleen Notebaert
- Department of Pediatrics, Sint-Vincentius Hospital Deinze, Deinze, Belgium
| | - Tom Van de Wiele
- Faculty of Bioscience Engineering, Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, 9000, Belgium
| | - Wim Van Den Broeck
- Faculty of Veterinary Medicine, Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Merelbeke, Belgium
| | - Koen Vanlede
- Department of General Pediatrics, VITAZ, Sint-Niklaas, Belgium
| | - Myriam Van Winckel
- Faculty of Medicine and Health Sciences, Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Lars Vereecke
- Faculty of Medicine and Health Sciences, Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Ghent Gut Inflammation Group (GGIG), Ghent, Belgium
| | - Chris Elliott
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, United Kingdom
| | - Eric Cox
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Immunology, Ghent University, Merelbeke, Belgium
| | - Lynn Vanhaecke
- Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, Merelbeke, Belgium
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, United Kingdom
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Oppong AE, Coelewij L, Robertson G, Martin-Gutierrez L, Waddington KE, Dönnes P, Nytrova P, Farrell R, Pineda-Torra I, Jury EC. Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity. iScience 2024; 27:109225. [PMID: 38433900 PMCID: PMC10907838 DOI: 10.1016/j.isci.2024.109225] [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: 09/19/2023] [Revised: 12/20/2023] [Accepted: 02/08/2024] [Indexed: 03/05/2024] Open
Abstract
There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.
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Affiliation(s)
- Alexandra E. Oppong
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Leda Coelewij
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Georgia Robertson
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Lucia Martin-Gutierrez
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Kirsty E. Waddington
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Pierre Dönnes
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
- Scicross AB, Skövde, Sweden
| | - Petra Nytrova
- Department of Neurology and Centre of Clinical, Neuroscience, First Faculty of Medicine, General University Hospital and First Faculty of Medicine, Charles University in Prague, 500 03 Prague, Czech Republic
| | - Rachel Farrell
- Department of Neuroinflammation, University College London and Institute of Neurology and National Hospital of Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Inés Pineda-Torra
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
| | - Elizabeth C. Jury
- Division of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK
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49
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Wang M, Yang N, Laterrière M, Gagné D, Omonijo F, Ibeagha-Awemu EM. Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis. J Anim Sci Biotechnol 2024; 15:46. [PMID: 38481273 PMCID: PMC10938844 DOI: 10.1186/s40104-024-00996-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/14/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes. RESULTS Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows. CONCLUSION The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.
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Affiliation(s)
- Mengqi Wang
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Naisu Yang
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Mario Laterrière
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - David Gagné
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - Faith Omonijo
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| | - Eveline M Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada.
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50
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Bhargava M, Crouser ED. Application of laboratory models for sarcoidosis research. J Autoimmun 2024:103184. [PMID: 38443221 DOI: 10.1016/j.jaut.2024.103184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
This manuscript will review the implications and applications of sarcoidosis models towards advancing our understanding of sarcoidosis disease mechanisms, identification of biomarkers, and preclinical testing of novel therapies. Emerging disease models and innovative research tools will also be considered.
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Affiliation(s)
- Maneesh Bhargava
- University of Minnesota Medical Center, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, 420 Delaware Street SE, MMC 276. Minneapolis, MN 55455, USA
| | - Elliott D Crouser
- Ohio State University Wexner Medicine Center, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, 241 W. 11th Street, Suite 5000, Columbus, OH 43201, USA.
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