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Rabotnick MH, Ehlinger J, Haidari A, Goodrich JM. Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity. Mol Cell Endocrinol 2023; 578:112046. [PMID: 37598796 PMCID: PMC10592024 DOI: 10.1016/j.mce.2023.112046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/11/2023] [Accepted: 08/16/2023] [Indexed: 08/22/2023]
Abstract
Endocrine disrupting chemicals (EDCs) are a diverse group of toxicants detected in populations globally. Prenatal EDC exposures impact birth and childhood outcomes. EDCs work through persistent changes at the molecular, cellular, and organ level. Molecular and biochemical signals or 'omics' can be measured at various functional levels - including the epigenome, transcriptome, proteome, metabolome, and the microbiome. In this narrative review, we introduce each omics and give examples of associations with prenatal EDC exposures. There is substantial research on epigenomic modifications in offspring exposed to EDCs during gestation, and a growing number of studies evaluating the transcriptome, proteome, metabolome, or microbiome in response to these exposures. Multi-omics, integrating data across omics layers, may improve understanding of disrupted function pathways related to early life exposures. We highlight several data integration methods to consider in multi-omics studies. Information from multi-omics can improve understanding of the biological processes and mechanisms underlying prenatal EDC toxicity.
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Affiliation(s)
- Margaret H Rabotnick
- Department of Environmental Health Sciences, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jessa Ehlinger
- Department of Environmental Health Sciences, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Ariana Haidari
- Department of Environmental Health Sciences, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jaclyn M Goodrich
- Department of Environmental Health Sciences, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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2
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Heuts BMH, Martens JHA. Understanding blood development and leukemia using sequencing-based technologies and human cell systems. Front Mol Biosci 2023; 10:1266697. [PMID: 37886034 PMCID: PMC10598665 DOI: 10.3389/fmolb.2023.1266697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023] Open
Abstract
Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.
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Affiliation(s)
- Branco M H Heuts
- Department of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Joost H A Martens
- Department of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, Netherlands
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3
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Erfanian N, Heydari AA, Feriz AM, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Razavi SM, Nasseri S, Safarpour H, Sahebkar A. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023; 165:115077. [PMID: 37393865 DOI: 10.1016/j.biopha.2023.115077] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023] Open
Abstract
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.
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Affiliation(s)
- Nafiseh Erfanian
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA; Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Adib Miraki Feriz
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Pablo Iañez
- Cellular Systems Genomics Group, Josep Carreras Research Institute, Barcelona, Spain
| | - Afshin Derakhshani
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | | | - Mohsen Farahpour
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Saeed Nasseri
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Safarpour
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran.
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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4
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Mu C, Zhao Q, Zhao Q, Yang L, Pang X, Liu T, Li X, Wang B, Fung SY, Cao H. Multi-omics in Crohn's disease: New insights from inside. Comput Struct Biotechnol J 2023; 21:3054-3072. [PMID: 37273853 PMCID: PMC10238466 DOI: 10.1016/j.csbj.2023.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/06/2023] Open
Abstract
Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.
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Affiliation(s)
- Chenlu Mu
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Qianjing Zhao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Qing Zhao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Lijiao Yang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Xiaoqi Pang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Tianyu Liu
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Xiaomeng Li
- Department of Immunology, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Bangmao Wang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Shan-Yu Fung
- Department of Immunology, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Hailong Cao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
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Wang Y, Tang S, Ma R, Zamit I, Wei Y, Pan Y. Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review. Comput Struct Biotechnol J 2022; 20:6149-6162. [DOI: 10.1016/j.csbj.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
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6
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Gupta S, Vundavilli H, Osorio RSA, Itoh MN, Mohsen A, Datta A, Mizuguchi K, Tripathi LP. Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of Non-Small Cell Lung Cancer. IEEE J Biomed Health Inform 2022; 26:4785-4793. [PMID: 35820010 DOI: 10.1109/jbhi.2022.3190038] [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: 11/08/2022]
Abstract
Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC.
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Monroy Kuhn JM, Miok V, Lutter D. Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks. BIOINFORMATICS ADVANCES 2022; 2:vbac042. [PMID: 36699352 PMCID: PMC9710706 DOI: 10.1093/bioadv/vbac042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 05/09/2022] [Accepted: 06/01/2022] [Indexed: 02/01/2023]
Abstract
Summary Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions. Availability and implementation The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | - Viktorian Miok
- Computational Discovery Unit, Institute for Diabetes & Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Astrocyte-Neuron Networks, Institute for Diabetes & Obesity, Helmholtz Zentrum München, Neuherberg, Germany
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Translational multi-omics microbiome research for strategies to improve cattle production and health. Emerg Top Life Sci 2022; 6:201-213. [PMID: 35311904 DOI: 10.1042/etls20210257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/27/2022]
Abstract
Cattle microbiome plays a vital role in cattle growth and performance and affects many economically important traits such as feed efficiency, milk/meat yield and quality, methane emission, immunity and health. To date, most cattle microbiome research has focused on metataxonomic and metagenomic characterization to reveal who are there and what they may do, preventing the determination of the active functional dynamics in vivo and their causal relationships with the traits. Therefore, there is an urgent need to combine other advanced omics approaches to improve microbiome analysis to determine their mode of actions and host-microbiome interactions in vivo. This review will critically discuss the current multi-omics microbiome research in beef and dairy cattle, aiming to provide insights on how the information generated can be applied to future strategies to improve production efficiency, health and welfare, and environment-friendliness in cattle production through microbiome manipulations.
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9
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Correa R, Alonso-Pupo N, Hernández Rodríguez EW. Multi-omics data integration approaches for precision oncology. Mol Omics 2022; 18:469-479. [DOI: 10.1039/d1mo00411e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...
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10
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Wani N, Barh D, Raza K. Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis. J Integr Bioinform 2021; 18:jib-2021-0029. [PMID: 34800012 PMCID: PMC8709739 DOI: 10.1515/jib-2021-0029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/28/2021] [Indexed: 12/14/2022] Open
Abstract
Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.
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Affiliation(s)
- Nisar Wani
- Computer Science and Engineering Department, Govt. College of Engineering and Technology Safapora, Ganderbal Kashmir, J&K, India
| | - Debmalya Barh
- Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, WB, India.,Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
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Naseri A, Sharghi M, Hasheminejad SMH. Enhancing gene regulatory networks inference through hub-based data integration. Comput Biol Chem 2021; 95:107589. [PMID: 34673384 DOI: 10.1016/j.compbiolchem.2021.107589] [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: 05/21/2021] [Revised: 08/11/2021] [Accepted: 10/04/2021] [Indexed: 12/09/2022]
Abstract
One of the main research topics in computational biology is Gene Regulatory Network (GRN) reconstruction that refers to inferring the relationships between genes involved in regulating cell conditions in response to internal or external stimuli. To this end, most computational methods use only transcriptional gene expression data to reconstruct gene regulatory networks, but recent studies suggest that gene expression data must be integrated with other types of data to obtain more accurate models predicting real relationships between genes. In this study, a diffusion-based method is enhanced to integrate biological data of network types besides structural prior knowledge. The Random Walk with Restart algorithm (RWR) with an emphasis on hub nodes is executed separately on each network, and then jointly optimizes low-dimensional feature vectors for network nodes by diffusion component analysis. Next, these feature vectors are used to infer gene regulatory networks. Fourteen centrality measures are studied for the detection of hub nodes to be used in the RWR algorithm, and the best centrality measure having the greatest effect on the improvement of gene network inference is selected. A case study for the Saccharomyces cerevisiae and E. coli networks shows that using the proposed features in comparison with gene expression data alone results in 0.02-0.08 units improvement in Area Under Receiver Characteristic Operator (AUROC) criteria across different gene regulatory network inference methods. Furthermore, the proposed method was applied to the esophageal cancer data to infer its gene regulatory network. The proposed framework substantially improves accuracy and scalability of GRN inference. The fused features and the best centrality measure detected can be used to provide functional insights about genes or proteins in various biological applications. Moreover, it can be served as a general framework for network data and structural data integration and analysis problems in various scientific disciplines including biology.
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Affiliation(s)
- Atefeh Naseri
- Department of Computer Engineering, Alzahra University, Tehran, Iran.
| | - Mehran Sharghi
- Department of Computer Engineering, Alzahra University, Tehran, Iran.
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Yang L, Zheng S, Xu X, Sun Y, Wang X, Li J. Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study. JMIR MEDICAL EDUCATION 2021; 7:e24027. [PMID: 34596575 PMCID: PMC8520135 DOI: 10.2196/24027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/16/2020] [Accepted: 08/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Medical postgraduates' demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important. OBJECTIVE This study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school. METHODS We developed a medical data mining course called "Practical Techniques of Medical Data Mining" for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms-Rain Classroom, Tencent Meeting, and WeChat-were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience. RESULTS In total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students' characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated "very positive" or "positive"), and MedHub received the best feedback, both in function (80/83, 96% chose "satisfied") and teaching effect (80/83, 96% chose "satisfied"). The case study showed that our course was able to fill the gap between student expectations and learning outcomes. CONCLUSIONS We developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.
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Affiliation(s)
- Lin Yang
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Si Zheng
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaowei Xu
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yueping Sun
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuwen Wang
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiao Li
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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13
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Conard AM, Goodman N, Hu Y, Perrimon N, Singh R, Lawrence C, Larschan E. TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data. Nucleic Acids Res 2021; 49:W641-W653. [PMID: 34125906 PMCID: PMC8262710 DOI: 10.1093/nar/gkab384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/13/2021] [Accepted: 04/28/2021] [Indexed: 01/17/2023] Open
Abstract
Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
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Affiliation(s)
- Ashley Mae Conard
- Computer Science Department, Brown University, Providence, RI 02912, USA.,Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Nathaniel Goodman
- Computer Science Department, Brown University, Providence, RI 02912, USA
| | - Yanhui Hu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Director of Bioinformatics DRSC/TRiP Functional Genomics Resources, Harvard Medical School, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Ritambhara Singh
- Computer Science Department, Brown University, Providence, RI 02912, USA.,Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Charles Lawrence
- Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA.,Applied Math Department, Brown University, Providence, RI 02912, USA
| | - Erica Larschan
- Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA.,Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA
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Fiorentino G, Visintainer R, Domenici E, Lauria M, Marchetti L. MOUSSE: Multi-Omics Using Subject-Specific SignaturEs. Cancers (Basel) 2021; 13:cancers13143423. [PMID: 34298641 PMCID: PMC8304726 DOI: 10.3390/cancers13143423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature. Abstract High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.
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Affiliation(s)
- Giuseppe Fiorentino
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Roberto Visintainer
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
| | - Enrico Domenici
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Mario Lauria
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Mathematics, University of Trento, 38123 Povo, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Correspondence:
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15
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Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021; 19:3735-3746. [PMID: 34285775 PMCID: PMC8258788 DOI: 10.1016/j.csbj.2021.06.030] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/25/2022] Open
Abstract
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Corresponding author.
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16
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Zhao N, Jia L, He X, Zhang B. Proteomics of mucosal exosomes of Cynoglossus semilaevis altered when infected by Vibrio harveyi. DEVELOPMENTAL AND COMPARATIVE IMMUNOLOGY 2021; 119:104045. [PMID: 33582105 DOI: 10.1016/j.dci.2021.104045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
The cargo of exosomes contains proteins with various functions, which might be promising biomarkers for disease diagnosis and prognosis. To explore the impact of the Vibrio harveyi pathogen on Cynoglossus semilaevis from a different perspective and develop promising biomarkers for infection, the exosomes from epidermal mucus of healthy controls(EC)and sick fish(ES)were extracted and identified, coupled with proteomic screening through iTRAQ followed with LC-MS/MS. 1531 credible proteins were obtained relating to structural, metabolic and immunological functions. 359 different expressed proteins (DEPs) (FC > 2 or FC < 0.5) were found, with 161 up-regulated and 198 down-regulated in ES. Based on the database of C. semilaevis on Uniprot, 71 proteins were characterized as concrete names, including 19 up-regulated proteins and 52 down-regulated proteins, and were selected as subjects for further studies. Ferritin, Toll-like receptor 5S protein and Calcium-transporting ATPase were upregulated, while Histone H2B and Eukaryotic translation initiation factor 5A were downregulated, consistent with the expression levels of related mRNAs in skin tissue verified by qRT-PCR. The integrated analysis between miRomics and proteomics also provided possible regulatory relationships mediated by mucous exosomes during infection. The signature proteins in mucosal exosomes could make sense in the explanation of the infection defending mechanism and the development of biomarkers which can differentiate diseased and healthy C. semilaevis individuals.
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Affiliation(s)
- Na Zhao
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Shanghai Ocean University), Ministry of Education, International Research Center for Marine Biosciences at Shanghai Ocean University, Ministry of Science and Technology, Shanghai, China
| | - Lei Jia
- Tianjin Fisheries Research Institute, Tianjin, China
| | - Xiaoxu He
- Tianjin Fisheries Research Institute, Tianjin, China
| | - Bo Zhang
- Tianjin Fisheries Research Institute, Tianjin, China.
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17
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Zhou M, Varol A, Efferth T. Multi-omics approaches to improve malaria therapy. Pharmacol Res 2021; 167:105570. [PMID: 33766628 DOI: 10.1016/j.phrs.2021.105570] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 01/07/2023]
Abstract
Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.
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Affiliation(s)
- Min Zhou
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Ayşegül Varol
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany.
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18
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Cava C, Sabetian S, Castiglioni I. Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data. ENTROPY 2021; 23:e23020225. [PMID: 33670375 PMCID: PMC7918754 DOI: 10.3390/e23020225] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 01/06/2023]
Abstract
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, Segrate, 20090 Milan, Italy
- Correspondence:
| | - Soudabeh Sabetian
- Infertility Research Center, Shiraz University of Medical Sciences, Shiraz, Iran;
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell’Ateneo Nuovo, 20126 Milan, Italy;
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Wani N, Raza K. MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks. PeerJ Comput Sci 2021; 7:e363. [PMID: 33817013 PMCID: PMC7924726 DOI: 10.7717/peerj-cs.363] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 12/29/2020] [Indexed: 06/12/2023]
Abstract
High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) inference approach wherein multiple heterogeneous datasets are fused using MKL paradigm. We formulate the GRN learning problem as a supervised classification problem, whereby genes regulated by a specific transcription factor are separated from other non-regulated genes. A parallel execution architecture is devised to learn a large scale GRN by decomposing the initial classification problem into a number of subproblems that run as multiple processes on a multi-processor machine. We evaluate the approach in terms of increased speedup and inference potential using genomic data from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The results thus obtained demonstrate that the proposed method exhibits better classification accuracy and enhanced speedup compared to other state-of-the-art methods while learning large scale GRNs from multiple and heterogeneous datasets.
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Affiliation(s)
- Nisar Wani
- Govt. Degree College Baramulla, Jammu & Kashmir, India
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
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20
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Labory J, Fierville M, Ait-El-Mkadem S, Bannwarth S, Paquis-Flucklinger V, Bottini S. Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives. Front Mol Biosci 2020; 7:590842. [PMID: 33240932 PMCID: PMC7667268 DOI: 10.3389/fmolb.2020.590842] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/14/2020] [Indexed: 01/06/2023] Open
Abstract
Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.
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Affiliation(s)
- Justine Labory
- Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice, France
| | - Morgane Fierville
- Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice, France
| | - Samira Ait-El-Mkadem
- Université Côte d'Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre hospitalier universitaire (CHU) de Nice, Nice, France
| | - Sylvie Bannwarth
- Université Côte d'Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre hospitalier universitaire (CHU) de Nice, Nice, France
| | - Véronique Paquis-Flucklinger
- Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice, France.,Université Côte d'Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre hospitalier universitaire (CHU) de Nice, Nice, France
| | - Silvia Bottini
- Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice, France
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21
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms. Sci Rep 2020; 10:14149. [PMID: 32843692 PMCID: PMC7447758 DOI: 10.1038/s41598-020-70941-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 07/22/2020] [Indexed: 01/01/2023] Open
Abstract
The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
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22
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Bonne Køhler J, Jers C, Senissar M, Shi L, Derouiche A, Mijakovic I. Importance of protein Ser/Thr/Tyr phosphorylation for bacterial pathogenesis. FEBS Lett 2020; 594:2339-2369. [PMID: 32337704 DOI: 10.1002/1873-3468.13797] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 04/16/2020] [Accepted: 04/20/2020] [Indexed: 12/13/2022]
Abstract
Protein phosphorylation regulates a large variety of biological processes in all living cells. In pathogenic bacteria, the study of serine, threonine, and tyrosine (Ser/Thr/Tyr) phosphorylation has shed light on the course of infectious diseases, from adherence to host cells to pathogen virulence, replication, and persistence. Mass spectrometry (MS)-based phosphoproteomics has provided global maps of Ser/Thr/Tyr phosphosites in bacterial pathogens. Despite recent developments, a quantitative and dynamic view of phosphorylation events that occur during bacterial pathogenesis is currently lacking. Temporal, spatial, and subpopulation resolution of phosphorylation data is required to identify key regulatory nodes underlying bacterial pathogenesis. Herein, we discuss how technological improvements in sample handling, MS instrumentation, data processing, and machine learning should improve bacterial phosphoproteomic datasets and the information extracted from them. Such information is expected to significantly extend the current knowledge of Ser/Thr/Tyr phosphorylation in pathogenic bacteria and should ultimately contribute to the design of novel strategies to combat bacterial infections.
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Affiliation(s)
- Julie Bonne Køhler
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Carsten Jers
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Mériem Senissar
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Lei Shi
- Systems and Synthetic Biology Division, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Abderahmane Derouiche
- Systems and Synthetic Biology Division, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ivan Mijakovic
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.,Systems and Synthetic Biology Division, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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23
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Rigoglio NN, Rabelo ACS, Borghesi J, de Sá Schiavo Matias G, Fratini P, Prazeres PHDM, Pimentel CMMM, Birbrair A, Miglino MA. The Tumor Microenvironment: Focus on Extracellular Matrix. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1245:1-38. [PMID: 32266651 DOI: 10.1007/978-3-030-40146-7_1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The extracellular matrix (ECM) regulates the development and maintains tissue homeostasis. The ECM is composed of a complex network of molecules presenting distinct biochemical properties to regulate cell growth, survival, motility, and differentiation. Among their components, proteoglycans (PGs) are considered one of the main components of ECM. Its composition, biomechanics, and anisotropy are exquisitely tuned to reflect the physiological state of the tissue. The loss of ECM's homeostasis is seen as one of the hallmarks of cancer and, typically, defines transitional events in tumor progression and metastasis. In this chapter, we discuss the types of proteoglycans and their roles in cancer. It has been observed that the amount of some ECM components is increased, while others are decreased, depending on the type of tumor. However, both conditions corroborate with tumor progression and malignancy. Therefore, ECM components have an increasingly important role in carcinogenesis and this leads us to believe that their understanding may be a key in the discovery of new anti-tumor therapies. In this book, the main ECM components will be discussed in more detail in each chapter.
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Affiliation(s)
- Nathia Nathaly Rigoglio
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - Ana Carolina Silveira Rabelo
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - Jessica Borghesi
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - Gustavo de Sá Schiavo Matias
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | - Paula Fratini
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil
| | | | | | - Alexander Birbrair
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
- Department of Pathology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Maria Angelica Miglino
- Department of Surgery, School of Veterinary Medicine and Animal Science, University of Sao Paulo, Sao Paulo, Brazil.
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Jiang D, Armour CR, Hu C, Mei M, Tian C, Sharpton TJ, Jiang Y. Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities. Front Genet 2019; 10:995. [PMID: 31781153 PMCID: PMC6857202 DOI: 10.3389/fgene.2019.00995] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 09/18/2019] [Indexed: 12/21/2022] Open
Abstract
The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.
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Affiliation(s)
- Duo Jiang
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Courtney R Armour
- Department of Microbiology, Oregon State University, Corvallis, OR, United States
| | - Chenxiao Hu
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Meng Mei
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Chuan Tian
- Department of Statistics, Oregon State University, Corvallis, OR, United States
| | - Thomas J Sharpton
- Department of Statistics, Oregon State University, Corvallis, OR, United States
- Department of Microbiology, Oregon State University, Corvallis, OR, United States
| | - Yuan Jiang
- Department of Statistics, Oregon State University, Corvallis, OR, United States
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