1
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Junaid M, Lee EJ, Lim SB. Single-cell and spatial omics: exploring hypothalamic heterogeneity. Neural Regen Res 2025; 20:1525-1540. [PMID: 38993130 DOI: 10.4103/nrr.nrr-d-24-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024] Open
Abstract
Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Eun Jeong Lee
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
- Department of Brain Science, Ajou University School of Medicine, Suwon, South Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
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2
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Panagaki T, Janickova L, Petrovic D, Zuhra K, Ditrói T, Jurányi EP, Bremer O, Ascenção K, Philipp TM, Nagy P, Filipovic MR, Szabo C. Neurobehavioral dysfunction in a mouse model of Down syndrome: upregulation of cystathionine β-synthase, H 2S overproduction, altered protein persulfidation, synaptic dysfunction, endoplasmic reticulum stress, and autophagy. GeroScience 2024; 46:4275-4314. [PMID: 38558215 PMCID: PMC11336008 DOI: 10.1007/s11357-024-01146-8] [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: 02/14/2024] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
Down syndrome (DS) is a genetic condition where the person is born with an extra chromosome 21. DS is associated with accelerated aging; people with DS are prone to age-related neurological conditions including an early-onset Alzheimer's disease. Using the Dp(17)3Yey/ + mice, which overexpresses a portion of mouse chromosome 17, which encodes for the transsulfuration enzyme cystathionine β-synthase (CBS), we investigated the functional role of the CBS/hydrogen sulfide (H2S) pathway in the pathogenesis of neurobehavioral dysfunction in DS. The data demonstrate that CBS is higher in the brain of the DS mice than in the brain of wild-type mice, with primary localization in astrocytes. DS mice exhibited impaired recognition memory and spatial learning, loss of synaptosomal function, endoplasmic reticulum stress, and autophagy. Treatment of mice with aminooxyacetate, a prototypical CBS inhibitor, improved neurobehavioral function, reduced the degree of reactive gliosis in the DS brain, increased the ability of the synaptosomes to generate ATP, and reduced endoplasmic reticulum stress. H2S levels in the brain of DS mice were higher than in wild-type mice, but, unexpectedly, protein persulfidation was decreased. Many of the above alterations were more pronounced in the female DS mice. There was a significant dysregulation of metabolism in the brain of DS mice, which affected amino acid, carbohydrate, lipid, endocannabinoid, and nucleotide metabolites; some of these alterations were reversed by treatment of the mice with the CBS inhibitor. Thus, the CBS/H2S pathway contributes to the pathogenesis of neurological dysfunction in DS in the current animal model.
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Affiliation(s)
- Theodora Panagaki
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Lucia Janickova
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Dunja Petrovic
- Leibniz-Institut Für Analytische Wissenschaften-ISAS-E.V., Dortmund, Germany
| | - Karim Zuhra
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Tamás Ditrói
- Department of Molecular Immunology and Toxicology and the National Tumor Biology Laboratory, National Institute of Oncology, Budapest, Hungary
| | - Eszter P Jurányi
- Department of Molecular Immunology and Toxicology and the National Tumor Biology Laboratory, National Institute of Oncology, Budapest, Hungary
- Doctoral School of Semmelweis University, Semmelweis University, Budapest, Hungary
| | - Olivier Bremer
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Kelly Ascenção
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Thilo M Philipp
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Péter Nagy
- Department of Molecular Immunology and Toxicology and the National Tumor Biology Laboratory, National Institute of Oncology, Budapest, Hungary
- Department of Anatomy and Histology, HUN-REN-UVMB Laboratory of Redox Biology Research Group, University of Veterinary Medicine, Budapest, Hungary
- Chemistry Institute, University of Debrecen, Debrecen, Hungary
| | - Milos R Filipovic
- Leibniz-Institut Für Analytische Wissenschaften-ISAS-E.V., Dortmund, Germany
| | - Csaba Szabo
- Chair of Pharmacology, Section of Science and Medicine, University of Fribourg, Fribourg, Switzerland.
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3
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Wang X, Yamaguchi N. Cause or effect: Probing the roles of epigenetics in plant development and environmental responses. CURRENT OPINION IN PLANT BIOLOGY 2024; 81:102569. [PMID: 38833828 DOI: 10.1016/j.pbi.2024.102569] [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: 01/25/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 06/06/2024]
Abstract
Epigenetic modifications are inheritable, reversible changes that control gene expression without altering the DNA sequence itself. Recent advances in epigenetic and sequencing technologies have revealed key regulatory regions in genes with multiple epigenetic changes. However, causal associations between epigenetic changes and physiological events have rarely been examined. Epigenome editing enables alterations to the epigenome without changing the underlying DNA sequence. Modifying epigenetic information in plants has important implications for causality assessment of the epigenome. Here, we briefly review tools for selectively interrogating the epigenome. We highlight promising research on site-specific DNA methylation and histone modifications and propose future research directions to more deeply investigate epigenetic regulation, including cause-and-effect relationships between epigenetic modifications and the development/environmental responses of Arabidopsis thaliana.
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Affiliation(s)
- Xuejing Wang
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama, Ikoma, Nara, 630-0192, Japan
| | - Nobutoshi Yamaguchi
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama, Ikoma, Nara, 630-0192, Japan.
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4
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Piana D, Iavarone F, De Paolis E, Daniele G, Parisella F, Minucci A, Greco V, Urbani A. Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges. Int J Mol Sci 2024; 25:8830. [PMID: 39201516 PMCID: PMC11354793 DOI: 10.3390/ijms25168830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 09/02/2024] Open
Abstract
Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.
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Affiliation(s)
- Diletta Piana
- Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (D.P.); (F.I.); (F.P.)
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
| | - Federica Iavarone
- Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (D.P.); (F.I.); (F.P.)
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
| | - Elisa De Paolis
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
- Departmental Unit of Molecular and Genomic Diagnostics, Genomics Core Facility, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Gennaro Daniele
- Phase 1 Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Federico Parisella
- Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (D.P.); (F.I.); (F.P.)
| | - Angelo Minucci
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
- Departmental Unit of Molecular and Genomic Diagnostics, Genomics Core Facility, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Viviana Greco
- Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (D.P.); (F.I.); (F.P.)
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
| | - Andrea Urbani
- Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (D.P.); (F.I.); (F.P.)
- Departmen Unity of Chemistry, Biochemistry and Clinical Molecular Biology, Department of Diagnostic and Laboratory Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (E.D.P.); (A.M.)
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5
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Benesch MGK, Cherkassky L, Nurkin SJ. Multi-Omic Analysis: A Possible Platform Toward Personalized and Adaptable Cancer Treatment. Ann Surg Oncol 2024; 31:4831-4833. [PMID: 38777897 DOI: 10.1245/s10434-024-15449-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024]
Affiliation(s)
- Matthew G K Benesch
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Leonid Cherkassky
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Steven J Nurkin
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
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6
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Thiele M, Villesen IF, Niu L, Johansen S, Sulek K, Nishijima S, Espen LV, Keller M, Israelsen M, Suvitaival T, Zawadzki AD, Juel HB, Brol MJ, Stinson SE, Huang Y, Silva MCA, Kuhn M, Anastasiadou E, Leeming DJ, Karsdal M, Matthijnssens J, Arumugam M, Dalgaard LT, Legido-Quigley C, Mann M, Trebicka J, Bork P, Jensen LJ, Hansen T, Krag A. Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases. J Hepatol 2024; 81:345-359. [PMID: 38552880 DOI: 10.1016/j.jhep.2024.03.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/16/2024] [Accepted: 03/19/2024] [Indexed: 07/26/2024]
Abstract
The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.
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Affiliation(s)
- Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ida Falk Villesen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Stine Johansen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
| | | | - Suguru Nishijima
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lore Van Espen
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Marisa Keller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mads Israelsen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | | | - Helene Bæk Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maximilian Joseph Brol
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maria Camilla Alvarez Silva
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Diana Julie Leeming
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Morten Karsdal
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Jelle Matthijnssens
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jonel Trebicka
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark.
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7
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Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024:kxae028. [PMID: 39074174 DOI: 10.1093/biostatistics/kxae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.
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Affiliation(s)
- Rong Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06511, CT, United States
| | - Shaodong Xu
- Center for Applied Statistics and School of Statistics, Renmin University of China, 59 Zhongguancun Street, 100872, Beijing, China
| | - Yang Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, 59 Zhongguancun Street, 100872, Beijing, China
| | - Zuojian Tang
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - Di Feng
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - James Cai
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, 06877, CT, United States
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06511, CT, United States
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8
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2024; 42:1133-1149. [PMID: 37679543 PMCID: PMC11252085 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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9
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Hutchison WJ, Keyes TJ, Crowell HL, Serizay J, Soneson C, Davis ES, Sato N, Moses L, Tarlinton B, Nahid AA, Kosmac M, Clayssen Q, Yuan V, Mu W, Park JE, Mamede I, Ryu MH, Axisa PP, Paiz P, Poon CL, Tang M, Gottardo R, Morgan M, Lee S, Lawrence M, Hicks SC, Nolan GP, Davis KL, Papenfuss AT, Love MI, Mangiola S. The tidyomics ecosystem: enhancing omic data analyses. Nat Methods 2024; 21:1166-1170. [PMID: 38877315 DOI: 10.1038/s41592-024-02299-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 05/05/2024] [Indexed: 06/16/2024]
Abstract
The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.
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Affiliation(s)
- William J Hutchison
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
| | - Timothy J Keyes
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Helena L Crowell
- University of Zurich, Zurich, Switzerland
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Jacques Serizay
- Unité Régulation Spatiale des Génomes, Institut Pasteur, CNRS UMR3525, Paris, France
| | - Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Eric S Davis
- Bioinformatics and Computational Biology Program, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noriaki Sato
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Lambda Moses
- California Institute of Technology, Pasadena, CA, USA
| | - Boyd Tarlinton
- Queensland Department of Agriculture and Fisheries, Brisbane, Queensland, Australia
| | - Abdullah A Nahid
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | | | | | - Victor Yuan
- Department of Statistics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Wancen Mu
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Ji-Eun Park
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Izabela Mamede
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Min Hyung Ryu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Pierre-Paul Axisa
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Toulouse, France
| | - Paulina Paiz
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Chi-Lam Poon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ming Tang
- Immunitas Therapeutics, Waltham, MA, USA
| | - Raphael Gottardo
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- University of Lausanne, Lausanne, Switzerland
- Lausanne University Hospital, Lausanne, Switzerland
| | - Martin Morgan
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Stuart Lee
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, USA
| | - Michael Lawrence
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Anthony T Papenfuss
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.
| | - Michael I Love
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Genetics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
| | - Stefano Mangiola
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.
- South Australian immunoGENomics Cancer Institute, The University of Adelaide, Adelaide, South Australia, Australia.
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10
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Holbrook-Smith D, Trouillon J, Sauer U. Metabolomics and Microbial Metabolism: Toward a Systematic Understanding. Annu Rev Biophys 2024; 53:41-64. [PMID: 38109374 DOI: 10.1146/annurev-biophys-030722-021957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.
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Affiliation(s)
| | - Julian Trouillon
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland;
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11
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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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12
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Hutchison WJ, Keyes TJ, Crowell HL, Serizay J, Soneson C, Davis ES, Sato N, Moses L, Tarlinton B, Nahid AA, Kosmac M, Clayssen Q, Yuan V, Mu W, Park JE, Mamede I, Ryu MH, Axisa PP, Paiz P, Poon CL, Tang M, Gottardo R, Morgan M, Lee S, Lawrence M, Hicks SC, Nolan GP, Davis KL, Papenfuss AT, Love MI, Mangiola S. The tidyomics ecosystem: Enhancing omic data analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.10.557072. [PMID: 38826347 PMCID: PMC11142095 DOI: 10.1101/2023.09.10.557072] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.
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Affiliation(s)
- William J. Hutchison
- Walter and Eliza Hall Institute of Medical Research, Division of Bioinformatics, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3052, Australia
| | - Timothy J. Keyes
- Stanford University School of Medicine, Department of Biomedical Data Science, USA
- Stanford University School of Medicine, Department of Pediatrics, USA
| | | | - Helena L. Crowell
- University of Zurich, Switzerland
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jacques Serizay
- Institut Pasteur, CNRS UMR3525, Unité Régulation Spatiale des Génomes, F-75015, Paris, France
| | - Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Eric S. Davis
- Bioinformatics and Computational Biology Program, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noriaki Sato
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Japan
| | | | - Boyd Tarlinton
- Queensland Department of Agriculture and Fisheries, Australia
| | - Abdullah A. Nahid
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | | | | | - Victor Yuan
- Department of Statistics, The University of British Columbia, Canada
| | - Wancen Mu
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Ji-Eun Park
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Izabela Mamede
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Min Hyung Ryu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, USA
- Department of Medicine, Harvard Medical School, USA
| | - Pierre-Paul Axisa
- Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Paulina Paiz
- Stanford University School of Medicine, Department of Biomedical Data Science, USA
| | - Chi-Lam Poon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | | | - Raphael Gottardo
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- University of Lausanne, Switzerland
- Lausanne University Hospital
| | | | - Stuart Lee
- Genentech, Department of Bioinformatics and Computational Biology, USA
| | - Michael Lawrence
- Genentech, Department of Bioinformatics and Computational Biology, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, USA
- Department of Biomedical Engineering, Johns Hopkins University, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA
| | - Garry P. Nolan
- Stanford University School of Medicine, Department of Pathology, USA
| | - Kara L. Davis
- Stanford University School of Medicine, Department of Pediatrics, USA
| | - Anthony T. Papenfuss
- Walter and Eliza Hall Institute of Medical Research, Division of Bioinformatics, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3052, Australia
| | - Michael I. Love
- Biostatistics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Genetics Department, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Stefano Mangiola
- Walter and Eliza Hall Institute of Medical Research, Division of Bioinformatics, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3052, Australia
- The University of Adelaide, South Australian immunoGENomics Cancer Institute, Adelaide, South Australia, Australia
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13
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Liu X, Tao Y, Cai Z, Bao P, Ma H, Li K, Li M, Zhu Y, Lu ZJ. Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data. Bioinformatics 2024; 40:btae316. [PMID: 38741230 PMCID: PMC11139513 DOI: 10.1093/bioinformatics/btae316] [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: 12/20/2023] [Revised: 03/29/2024] [Accepted: 05/11/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. RESULTS To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. AVAILABILITY AND IMPLEMENTATION Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.
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Affiliation(s)
- Xiaofan Liu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Yuhuan Tao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Zilin Cai
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Pengfei Bao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Hongli Ma
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Kexing Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Mengtao Li
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), MST State Key Laboratory of Complex Severe and Rare Diseases, MOE Key Laboratory of Rheumatology and Clinical Immunology, Beijing 100730, China
| | - Yunping Zhu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zhi John Lu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
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14
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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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15
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Foreman AL, Warth B, Hessel EVS, Price EJ, Schymanski EL, Cantelli G, Parkinson H, Hecht H, Klánová J, Vlaanderen J, Hilscherova K, Vrijheid M, Vineis P, Araujo R, Barouki R, Vermeulen R, Lanone S, Brunak S, Sebert S, Karjalainen T. Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7256-7269. [PMID: 38641325 PMCID: PMC11064223 DOI: 10.1021/acs.est.3c07961] [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: 09/25/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.
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Affiliation(s)
- Amy L. Foreman
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, University
of Vienna, 1090 Vienna, Austria
| | - Ellen V. S. Hessel
- National
Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine, University
of Luxembourg, 6 avenue
du Swing, L-4367 Belvaux, Luxembourg
| | - Gaia Cantelli
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helen Parkinson
- European
Molecular Biology Laboratory & European Bioinformatics Institute
(EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, U.K.
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Jelle Vlaanderen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Klara Hilscherova
- RECETOX,
Faculty of Science, Masaryk University, Kotlarska 2, Brno 60200, Czech Republic
| | - Martine Vrijheid
- Institute
for Global Health (ISGlobal), Barcelona
Biomedical Research Park (PRBB), Doctor Aiguader, 88, 08003 Barcelona, Spain
- Universitat
Pompeu Fabra, Carrer
de la Mercè, 12, Ciutat Vella, 08002 Barcelona, Spain
- Centro de Investigación Biomédica en Red
Epidemiología
y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5. Pebellón 11, Planta 0, 28029 Madrid, Spain
| | - Paolo Vineis
- Department
of Epidemiology and Biostatistics, School of Public Health, Imperial College, London SW7 2AZ, U.K.
| | - Rita Araujo
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
| | | | - Roel Vermeulen
- Institute
for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands
| | - Sophie Lanone
- Univ Paris Est Creteil, INSERM, IMRB, F-94010 Creteil, France
| | - Søren Brunak
- Novo
Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Blegdamsvej 3B, 2200 København, Denmark
| | - Sylvain Sebert
- Research
Unit of Population Health, University of
Oulu, P.O. Box 8000, FI-90014 Oulu, Finland
| | - Tuomo Karjalainen
- European Commission, DG Research and Innovation, Sq. Frère-Orban 8, 1000 Bruxelles, Belgium
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16
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Berleant JD, Banal JL, Rao DK, Bathe M. Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.12.24305660. [PMID: 38699348 PMCID: PMC11064994 DOI: 10.1101/2024.04.12.24305660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.
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Affiliation(s)
- Joseph D. Berleant
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - James L. Banal
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Present address: Cache DNA, Inc. 733 Industrial Rd., San Carlos, CA 94070 USA
| | | | - Mark Bathe
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139 USA
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17
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Jiang H, Chai ZX, Chen XY, Zhang CF, Zhu Y, Ji QM, Xin JW. Yak genome database: a multi-omics analysis platform. BMC Genomics 2024; 25:346. [PMID: 38580907 PMCID: PMC10998334 DOI: 10.1186/s12864-024-10274-6] [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: 10/30/2023] [Accepted: 03/31/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND The yak (Bos grunniens) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data. DESCRIPTION The Yak Genome Database ( http://yakgenomics.com/ ) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database. CONCLUSION This comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.
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Affiliation(s)
- Hui Jiang
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China
| | - Zhi-Xin Chai
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, 610041, Chengdu, Sichuan, China
| | - Xiao-Ying Chen
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China
| | - Cheng-Fu Zhang
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China
| | - Yong Zhu
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China
| | - Qiu-Mei Ji
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China.
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China.
| | - Jin-Wei Xin
- State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China.
- Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China.
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18
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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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19
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Zhang Z, Zhang Q, Yang H, Cui L, Qian H. Mining strategies for isolating plastic-degrading microorganisms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 346:123572. [PMID: 38369095 DOI: 10.1016/j.envpol.2024.123572] [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: 12/27/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 02/20/2024]
Abstract
Plastic waste is a growing global pollutant. Plastic degradation by microorganisms has captured attention as an earth-friendly tactic. Although the mechanisms of plastic degradation by bacteria, fungi, and algae have been explored over the past decade, a large knowledge gap still exists regarding the identification, sorting, and cultivation of efficient plastic degraders, primarily because of their uncultivability. Advances in sequencing techniques and bioinformatics have enabled the identification of microbial degraders and related enzymes and genes involved in plastic biodegradation. In this review, we provide an outline of the situation of plastic degradation and summarize the methods for effective microbial identification using multidisciplinary techniques such as multiomics, meta-analysis, and spectroscopy. This review introduces new strategies for controlling plastic pollution in an environmentally friendly manner. Using this information, highly efficient and colonizing plastic degraders can be mined via targeted sorting and cultivation. In addition, based on the recognized rules and plastic degraders, we can perform an in-depth analysis of the associated degradation mechanism, metabolic features, and interactions.
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Affiliation(s)
- Ziyao Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, PR China
| | - Qi Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, PR China
| | - Huihui Yang
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, PR China
| | - Li Cui
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, PR China
| | - Haifeng Qian
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, PR China.
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20
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Zhao Z, Zobolas J, Zucknick M, Aittokallio T. Tutorial on survival modeling with applications to omics data. Bioinformatics 2024; 40:btae132. [PMID: 38445722 PMCID: PMC10973942 DOI: 10.1093/bioinformatics/btae132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/22/2024] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
MOTIVATION Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes. RESULTS We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally. AVAILABILITY AND IMPLEMENTATION A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.
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Affiliation(s)
- Zhi Zhao
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo 0372, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo 0310, Norway
| | - John Zobolas
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo 0372, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo 0310, Norway
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo 0372, Norway
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Research Support Services, Oslo University Hospital, Oslo 0372, Norway
| | - Tero Aittokallio
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo 0372, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo 0310, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki FI-00014, Finland
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21
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Lin A, Torres CM, Hobbs EC, Bardhan J, Aley SB, Spencer CT, Taylor KL, Chiang T. Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures. Health Secur 2024; 22:130-139. [PMID: 38483337 PMCID: PMC11044874 DOI: 10.1089/hs.2023.0076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024] Open
Affiliation(s)
- Andy Lin
- Andy Lin, PhD, is a Linus Pauling Distinguished Postdoctoral Fellow; in the National Security Directorate, Pacific Northwest National Laboratory, Seattle, WA
| | - Cameron M. Torres
- Cameron M. Torres is a Graduate Research Assistant and Wieland Fellow, Department of Biological Sciences; at the University of Texas at El Paso, El Paso, TX
| | - Errett C. Hobbs
- Errett C. Hobbs, PhD, is a Data Scientist; in the National Security Directorate, Pacific Northwest National Laboratory, Seattle, WA
| | - Jaydeep Bardhan
- Jaydeep Bardhan, PhD, is a Research Line Manager, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA
| | - Stephen B. Aley
- Stephen B. Aley, PhD, is a Professor, Biological Sciences, and an Associate Vice President for Research, Sponsored Projects; at the University of Texas at El Paso, El Paso, TX
| | - Charles T. Spencer
- Charles T. Spencer, PhD, is an Associate Professor, Biological Sciences, and Edward and Barbara Brown Egbert Endowed Chair of the Department of Biological Sciences; at the University of Texas at El Paso, El Paso, TX
| | - Karen L. Taylor
- Karen L. Taylor, MS, is a Research Line Manager; in the National Security Directorate, Pacific Northwest National Laboratory, Seattle, WA
| | - Tony Chiang
- Tony Chiang, PhD, is a Data Scientist; in the National Security Directorate, Pacific Northwest National Laboratory, Seattle, WA
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22
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Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med 2024; 43:983-1002. [PMID: 38146838 DOI: 10.1002/sim.9953] [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: 04/21/2022] [Revised: 10/06/2023] [Accepted: 10/24/2023] [Indexed: 12/27/2023]
Abstract
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.
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Affiliation(s)
- Himel Mallick
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, 10065, New York, USA
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Anupreet Porwal
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Satabdi Saha
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Piyali Basak
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Vladimir Svetnik
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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23
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Luo H, Liang H, Liu H, Fan Z, Wei Y, Yao X, Cong S. TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction. Int J Mol Sci 2024; 25:1655. [PMID: 38338932 PMCID: PMC10855161 DOI: 10.3390/ijms25031655] [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: 12/29/2023] [Revised: 01/20/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.
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Affiliation(s)
- Haoran Luo
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Zhoujie Fan
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
| | - Yanhui Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Shan Cong
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
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24
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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25
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Hédou J, Marić I, Bellan G, Einhaus J, Gaudillière DK, Ladant FX, Verdonk F, Stelzer IA, Feyaerts D, Tsai AS, Ganio EA, Sabayev M, Gillard J, Amar J, Cambriel A, Oskotsky TT, Roldan A, Golob JL, Sirota M, Bonham TA, Sato M, Diop M, Durand X, Angst MS, Stevenson DK, Aghaeepour N, Montanari A, Gaudillière B. Discovery of sparse, reliable omic biomarkers with Stabl. Nat Biotechnol 2024:10.1038/s41587-023-02033-x. [PMID: 38168992 PMCID: PMC11217152 DOI: 10.1038/s41587-023-02033-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024]
Abstract
Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .
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Affiliation(s)
- Julien Hédou
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Grégoire Bellan
- Télécom Paris, Institut Polytechnique de Paris, Paris, France
| | - Jakob Einhaus
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Dyani K Gaudillière
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | | | - Franck Verdonk
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Maximilian Sabayev
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Joshua Gillard
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jonas Amar
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Amelie Cambriel
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Alennie Roldan
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan L Golob
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas A Bonham
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Masaki Sato
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Maïgane Diop
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Xavier Durand
- École Polytechnique, Institut Polytechnique de Paris, Paris, France
| | - Martin S Angst
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrea Montanari
- Department of Statistics, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
- Department of Pediatrics, Stanford University, Stanford, CA, USA.
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26
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Reghupaty SC, Dall NR, Svensson KJ. Hallmarks of the metabolic secretome. Trends Endocrinol Metab 2024; 35:49-61. [PMID: 37845120 PMCID: PMC10841501 DOI: 10.1016/j.tem.2023.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023]
Abstract
The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.
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Affiliation(s)
- Saranya C Reghupaty
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Nicholas R Dall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Katrin J Svensson
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA.
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27
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Yu L, Yan H. DNA-based computation for multiple biomarkers. Nat Biomed Eng 2023; 7:1535-1536. [PMID: 38097810 DOI: 10.1038/s41551-023-01161-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Affiliation(s)
- Lu Yu
- Center for Molecular Design and Biomimetics, Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Hao Yan
- Center for Molecular Design and Biomimetics, Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
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28
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Wang Q, Zhang J, Liu Z, Duan Y, Li C. Integrative approaches based on genomic techniques in the functional studies on enhancers. Brief Bioinform 2023; 25:bbad442. [PMID: 38048082 PMCID: PMC10694556 DOI: 10.1093/bib/bbad442] [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: 08/28/2023] [Revised: 10/22/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.
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Affiliation(s)
- Qilin Wang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Junyou Zhang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Zhaoshuo Liu
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Yingying Duan
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chunyan Li
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing 100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
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Aparicio A, Sun Z, Gold DR, Litonjua AA, Weiss ST, Lee-Sarwar K, Liu YY. Genotype-microbiome-metabolome associations in early childhood, and their link to BMI and childhood obesity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.13.23298467. [PMID: 38014043 PMCID: PMC10680902 DOI: 10.1101/2023.11.13.23298467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The influence of genotype on defining the human gut microbiome has been extensively studied, but definite conclusions have not yet been found. To fill this knowledge gap, we leverage data from children enrolled in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) from 6 months to 8 years old. We focus on a pool of 12 genes previously found to be associated with the gut microbiome in independent studies, establishing a Bonferroni corrected significance level of p-value < 2.29 × 10 -6 . We identified significant associations between SNPs in the FHIT gene (known to be associated with obesity and type 2 diabetes) and obesity-related microbiome features, and the children's BMI through their childhood. Based on these associations, we defined a set of SNPs of interest and a set of taxa of interest. Taking a multi-omics approach, we integrated plasma metabolome data into our analysis and found simultaneous associations among children's BMI, the SNPs of interest, and the taxa of interest, involving amino acids, lipids, nucleotides, and xenobiotics. Using our association results, we constructed a quadripartite graph where each disjoint node set represents SNPs in the FHIT gene, microbial taxa, plasma metabolites, or BMI measurements. Network analysis led to the discovery of patterns that identify several genetic variants, microbial taxa and metabolites as new potential markers for obesity, type 2 diabetes, or insulin resistance risk.
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30
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Yang J, Liu Y, Shang J, Chen Q, Chen Q, Ren L, Zhang N, Yu Y, Li Z, Song Y, Yang S, Scherer A, Tong W, Hong H, Xiao W, Shi L, Zheng Y. The Quartet Data Portal: integration of community-wide resources for multiomics quality control. Genome Biol 2023; 24:245. [PMID: 37884999 PMCID: PMC10601216 DOI: 10.1186/s13059-023-03091-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.
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Affiliation(s)
- Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yueqiang Song
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shengpeng Yang
- Intelligent Storage, Alibaba Cloud, Alibaba Group, Hangzhou, Zhejiang, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncological Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
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Downing T, Angelopoulos N. A primer on correlation-based dimension reduction methods for multi-omics analysis. J R Soc Interface 2023; 20:20230344. [PMID: 37817584 PMCID: PMC10565429 DOI: 10.1098/rsif.2023.0344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.
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Affiliation(s)
- Tim Downing
- Pirbright Institute, Pirbright, Surrey, UK
- Department of Biotechnology, Dublin City University, Dublin, Ireland
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32
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Ishida H, John U, Murray SA, Bhattacharya D, Chan CX. Developing model systems for dinoflagellates in the post-genomic era. JOURNAL OF PHYCOLOGY 2023; 59:799-808. [PMID: 37657822 DOI: 10.1111/jpy.13386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 09/03/2023]
Abstract
Dinoflagellates are a diverse group of eukaryotic microbes that are ubiquitous in aquatic environments. Largely photosynthetic, they encompass symbiotic, parasitic, and free-living lineages with a broad spectrum of trophism. Many free-living taxa can produce bioactive secondary metabolites such as biotoxins, some of which cause harmful algal blooms. In contrast, most symbiotic species are crucial for sustaining coral reef health. The year 2023 marked a decade since the first genome data of dinoflagellates became available. The growing genome-scale resources for these taxa are highlighting their remarkable evolutionary and genomic complexities. Here, we discuss the prospect of developing dinoflagellate models using the criteria of accessibility, tractability, resources, research support, and promise. Moving forward in the post-genomic era, we argue for the development of fit-to-purpose models that tailor to specific biological contexts, and that a one-size-fits-all model is inadequate for encapsulating the complex biology, ecology, and evolutionary history of dinoflagellates.
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Affiliation(s)
- Hisatake Ishida
- School of Chemistry and Molecular Biosciences, Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland, Australia
| | - Uwe John
- Alfred-Wegener-Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
- Helmholtz Institute for Functional Marine Biodiversity, Oldenburg, Germany
| | - Shauna A Murray
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales, Australia
| | - Debashish Bhattacharya
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, USA
| | - Cheong Xin Chan
- School of Chemistry and Molecular Biosciences, Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland, Australia
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33
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Arıkan M, Muth T. Integrated multi-omics analyses of microbial communities: a review of the current state and future directions. Mol Omics 2023; 19:607-623. [PMID: 37417894 DOI: 10.1039/d3mo00089c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.
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Affiliation(s)
- Muzaffer Arıkan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.
- Department of Medical Biology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
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34
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Azimi A, Fernandez-Peñas P. Molecular Classifiers in Skin Cancers: Challenges and Promises. Cancers (Basel) 2023; 15:4463. [PMID: 37760432 PMCID: PMC10526380 DOI: 10.3390/cancers15184463] [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: 04/23/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Skin cancers are common and heterogenous malignancies affecting up to two in three Australians before age 70. Despite recent developments in diagnosis and therapeutic strategies, the mortality rate and costs associated with managing patients with skin cancers remain high. The lack of well-defined clinical and histopathological features makes their diagnosis and classification difficult in some cases and the prognostication difficult in most skin cancers. Recent advancements in large-scale "omics" studies, including genomics, transcriptomics, proteomics, metabolomics and imaging-omics, have provided invaluable information about the molecular and visual landscape of skin cancers. On many occasions, it has refined tumor classification and has improved prognostication and therapeutic stratification, leading to improved patient outcomes. Therefore, this paper reviews the recent advancements in omics approaches and appraises their limitations and potential for better classification and stratification of skin cancers.
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Affiliation(s)
- Ali Azimi
- Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Department of Dermatology, Westmead Hospital, Westmead, NSW 2145, Australia
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
| | - Pablo Fernandez-Peñas
- Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Department of Dermatology, Westmead Hospital, Westmead, NSW 2145, Australia
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
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35
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Blutt SE, Coarfa C, Neu J, Pammi M. Multiomic Investigations into Lung Health and Disease. Microorganisms 2023; 11:2116. [PMID: 37630676 PMCID: PMC10459661 DOI: 10.3390/microorganisms11082116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
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Affiliation(s)
- Sarah E. Blutt
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA;
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Cristian Coarfa
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Josef Neu
- Department of Pediatrics, Section of Neonatology, University of Florida, Gainesville, FL 32611, USA;
| | - Mohan Pammi
- Department of Pediatrics, Section of Neonatology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX 77030, USA
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36
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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37
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Maiorino E, Loscalzo J. Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping. Arterioscler Thromb Vasc Biol 2023; 43:1111-1123. [PMID: 37226730 PMCID: PMC10330619 DOI: 10.1161/atvbaha.122.318892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.
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Affiliation(s)
- Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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38
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Wijayawardene NN, Boonyuen N, Ranaweera CB, de Zoysa HKS, Padmathilake RE, Nifla F, Dai DQ, Liu Y, Suwannarach N, Kumla J, Bamunuarachchige TC, Chen HH. OMICS and Other Advanced Technologies in Mycological Applications. J Fungi (Basel) 2023; 9:688. [PMID: 37367624 PMCID: PMC10302638 DOI: 10.3390/jof9060688] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/06/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023] Open
Abstract
Fungi play many roles in different ecosystems. The precise identification of fungi is important in different aspects. Historically, they were identified based on morphological characteristics, but technological advancements such as polymerase chain reaction (PCR) and DNA sequencing now enable more accurate identification and taxonomy, and higher-level classifications. However, some species, referred to as "dark taxa", lack distinct physical features that makes their identification challenging. High-throughput sequencing and metagenomics of environmental samples provide a solution to identifying new lineages of fungi. This paper discusses different approaches to taxonomy, including PCR amplification and sequencing of rDNA, multi-loci phylogenetic analyses, and the importance of various omics (large-scale molecular) techniques for understanding fungal applications. The use of proteomics, transcriptomics, metatranscriptomics, metabolomics, and interactomics provides a comprehensive understanding of fungi. These advanced technologies are critical for expanding the knowledge of the Kingdom of Fungi, including its impact on food safety and security, edible mushrooms foodomics, fungal secondary metabolites, mycotoxin-producing fungi, and biomedical and therapeutic applications, including antifungal drugs and drug resistance, and fungal omics data for novel drug development. The paper also highlights the importance of exploring fungi from extreme environments and understudied areas to identify novel lineages in the fungal dark taxa.
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Affiliation(s)
- Nalin N. Wijayawardene
- Centre for Yunnan Plateau Biological Resources Protection and Utilization, College of Biological Resource and Food Engineering, Qujing Normal University, Qujing 655011, China;
- Department of Bioprocess Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka; (H.K.S.d.Z.); (F.N.); (T.C.B.)
- Section of Genetics, Institute for Research and Development in Health and Social Care, No: 393/3, Lily Avenue, Off Robert Gunawardane Mawatha, Battaramulla 10120, Sri Lanka
| | - Nattawut Boonyuen
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand;
| | - Chathuranga B. Ranaweera
- Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University Sri Lanka, Kandawala Road, Rathmalana 10390, Sri Lanka;
| | - Heethaka K. S. de Zoysa
- Department of Bioprocess Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka; (H.K.S.d.Z.); (F.N.); (T.C.B.)
| | - Rasanie E. Padmathilake
- Department of Plant Sciences, Faculty of Agriculture, Rajarata University of Sri Lanka, Pulliyankulama, Anuradhapura 50000, Sri Lanka;
| | - Faarah Nifla
- Department of Bioprocess Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka; (H.K.S.d.Z.); (F.N.); (T.C.B.)
| | - Dong-Qin Dai
- Centre for Yunnan Plateau Biological Resources Protection and Utilization, College of Biological Resource and Food Engineering, Qujing Normal University, Qujing 655011, China;
| | - Yanxia Liu
- Guizhou Academy of Tobacco Science, No.29, Longtanba Road, Guanshanhu District, Guiyang 550000, China;
| | - Nakarin Suwannarach
- Research Center of Microbial Diversity and Sustainable Utilization, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; (N.S.); (J.K.)
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Jaturong Kumla
- Research Center of Microbial Diversity and Sustainable Utilization, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; (N.S.); (J.K.)
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Thushara C. Bamunuarachchige
- Department of Bioprocess Technology, Faculty of Technology, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka; (H.K.S.d.Z.); (F.N.); (T.C.B.)
| | - Huan-Huan Chen
- Centre for Yunnan Plateau Biological Resources Protection and Utilization, College of Biological Resource and Food Engineering, Qujing Normal University, Qujing 655011, China;
- Key Laboratory of Insect-Pollinator Biology of Ministry of Agriculture and Rural Affairs, Institute of Agricultural Research, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Yin F, Zhao H, Lu S, Shen J, Li M, Mao X, Li F, Shi J, Li J, Dong B, Xue W, Zuo X, Yang X, Fan C. DNA-framework-based multidimensional molecular classifiers for cancer diagnosis. NATURE NANOTECHNOLOGY 2023; 18:677-686. [PMID: 36973399 DOI: 10.1038/s41565-023-01348-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.
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Affiliation(s)
- Fangfei Yin
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Haipei Zhao
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shasha Lu
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Juwen Shen
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China
| | - Min Li
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiuhai Mao
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Fan Li
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jiye Shi
- Division of Physical Biology, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
| | - Jiang Li
- Division of Physical Biology, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China
- The Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
| | - Baijun Dong
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Xue
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolei Zuo
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Xiurong Yang
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China
| | - Chunhai Fan
- Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
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40
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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41
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Sun C, Schuman E. A multi-omics view of neuronal subcellular protein synthesis. Curr Opin Neurobiol 2023; 80:102705. [PMID: 36913750 DOI: 10.1016/j.conb.2023.102705] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 03/13/2023]
Abstract
While it has long been known that protein synthesis is necessary for long-term memory in the brain, the logistics of neuronal protein synthesis is complicated by the extensive subcellular compartmentalization of the neuron. Local protein synthesis solves many of the logistic problems posed by the extreme complexity of dendritic and axonal arbors and the huge number of synapses. Here we review recent multi-omic and quantitative studies that elaborate a systems view of decentralized neuronal protein synthesis. We highlight recent insights from the transcriptomic, translatomic, and proteomic levels, discuss the nuanced logic of local protein synthesis for different protein features, and list the missing information needed to build a comprehensive logistic model for neuronal protein supply.
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Affiliation(s)
- Chao Sun
- Max Planck Institute for Brain Research, Frankfurt, Germany; Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Denmark; Aarhus University, Department of Molecular Biology and Genetics, Universitetsbyen 81, 8000 Aarhus C, Denmark. https://twitter.com/LukeChaoSun
| | - Erin Schuman
- Max Planck Institute for Brain Research, Frankfurt, Germany.
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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Updates in neuroendocrine neoplasms: From mechanisms to the clinic. ANNALES D'ENDOCRINOLOGIE 2023; 84:291-297. [PMID: 36690074 DOI: 10.1016/j.ando.2022.12.424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 12/04/2022] [Indexed: 01/22/2023]
Abstract
Scientific advances constantly improve our understanding of the mechanisms underlying tumorigenesis, allowing us now to analyze cancer in a more precise manner and to identify at an earlier stage the tumors that have greater risk of aggressive behavior. Understanding neuroendocrine neoplasms at molecular level has enabled increasingly targeted treatments, with safety and efficacy validated in large randomized trials. Moreover, the first studies of targeted therapies after molecular profiling of neuroendocrine neoplasms have shown encouraging results, allowing us to foresee ever more personalized medical treatments in the future. This literature review aims to summarize recent advances in the study of neuroendocrine neoplasms and to show how identification of new mechanisms underlying tumorigenesis can be of benefit in clinical practice.
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Duperron S, Foucault P, Duval C, Goto M, Gallet A, Colas S, Marie B. Multi-omics analyses from a single sample: prior metabolite extraction does not alter the 16S rRNA-based characterization of prokaryotic community in a diversity of sample types. FEMS Microbiol Lett 2023; 370:fnad125. [PMID: 37996396 DOI: 10.1093/femsle/fnad125] [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: 07/18/2023] [Revised: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
Massive sequencing of the 16S rRNA gene has become a standard first step to describe and compare microbial communities from various samples. Parallel analysis of high numbers of samples makes it relevant to the statistical testing of the influence of natural or experimental factors and variables. However, these descriptions fail to document changes in community or ecosystem functioning. Nontargeted metabolomics are a suitable tool to bridge this gap, yet extraction protocols are different. In this study, prokaryotic community compositions are documented by 16S rRNA gene sequencing after direct DNA extraction or after metabolites extraction followed by DNA extraction. Results obtained using the V3-V4 region on nonaxenic cultures of cyanobacteria, lake water column, biofilm, and gut of wild and lab-reared fish indicate that prior extraction of metabolites does not influence the obtained image of prokaryotic communities. This validates sequential extraction of metabolites followed by DNA as a way to combine 16S rRNA sequencing with metabolome characterization from a single sample. This approach has the potential to complement community structure characterization with a proxy of their functioning, without the uncertainties associated with the use of separate samples.
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Affiliation(s)
- Sébastien Duperron
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
| | - Pierre Foucault
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
- UMR7618 iEES-Paris, Sorbonne Université, 4 place Jussieu, 75005 Paris, France
| | - Charlotte Duval
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
| | - Midoli Goto
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
| | - Alison Gallet
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
| | - Simon Colas
- Université de Pau et des Pays de l'Adour, E2S-UPPA, CNRS, IPREM, 2 Av. du Président Pierre Angot, 64053 Pau, France
| | - Benjamin Marie
- UMR7245 Molécules de Communication et Adaptation des Micro-Organismes, Muséum National d'Histoire Naturelle, CNRS, 12 rue Buffon, 75005 Paris, France
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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. [PMID: 36420153 PMCID: PMC9674886 DOI: 10.1016/j.csbj.2022.11.008] [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: 06/06/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.
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Affiliation(s)
- Yanlin Wang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Shi Tang
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region
| | - Ruimin Ma
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Ibrahim Zamit
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
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46
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Brombacher E, Hackenberg M, Kreutz C, Binder H, Treppner M. The performance of deep generative models for learning joint embeddings of single-cell multi-omics data. Front Mol Biosci 2022; 9:962644. [PMID: 36387277 PMCID: PMC9643784 DOI: 10.3389/fmolb.2022.962644] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 10/12/2022] [Indexed: 11/07/2023] Open
Abstract
Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.
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Affiliation(s)
- Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM) University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signaling Studies (CIBSS) University of Freiburg, Freiburg, Germany
- Faculty of Biology University of Freiburg, Freiburg, Germany
| | - Maren Hackenberg
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signaling Studies (CIBSS) University of Freiburg, Freiburg, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling University of Freiburg, Freiburg, Germany
| | - Martin Treppner
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling University of Freiburg, Freiburg, Germany
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47
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Chen YA, Allendes Osorio RS, Mizuguchi K. TargetMine 2022: a new vision into drug target analysis. Bioinformatics 2022; 38:4454-4456. [PMID: 35894632 PMCID: PMC9477527 DOI: 10.1093/bioinformatics/btac507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/08/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY We introduce the newest version of TargetMine, which includes the addition of new visualization options; integration of previously disaggregated functionality; and the migration of the front-end to the newly available Bluegenes service. AVAILABILITY AND IMPLEMENTATION TargeteMine is accessible online at https://targetmine.mizuguchilab.org/bluegenes. Users do not need to register to use the software. Source code for the different components listed in the article is available from TargetMine's organizational account at http://github.com/targetmine. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi-An Chen
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
| | - Rodolfo S Allendes Osorio
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
- Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
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Povilaitis SC, Chakraborty A, Kirkpatrick LM, Downey RD, Hauger SB, Eberlin LS. Identifying Clinically Relevant Bacteria Directly from Culture and Clinical Samples with a Handheld Mass Spectrometry Probe. Clin Chem 2022; 68:1459-1470. [DOI: 10.1093/clinchem/hvac147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 07/11/2022] [Indexed: 11/13/2022]
Abstract
Abstract
Background
Rapid identification of bacteria is critical to prevent antimicrobial resistance and ensure positive patient outcomes. We have developed the MasSpec Pen, a handheld mass spectrometry-based device that enables rapid analysis of biological samples. Here, we evaluated the MasSpec Pen for identification of bacteria from culture and clinical samples.
Methods
A total of 247 molecular profiles were obtained from 43 well-characterized strains of 8 bacteria species that are clinically relevant to osteoarticular infections, including Staphylococcus aureus, Group A and B Streptococcus, and Kingella kingae, using the MasSpec Pen coupled to a high-resolution mass spectrometer. The molecular profiles were used to generate statistical classifiers based on metabolites that were predictive of Gram stain category, genus, and species. Then, we directly analyzed samples from 4 patients, including surgical specimens and clinical isolates, and used the classifiers to predict the etiologic agent.
Results
High accuracies were achieved for all levels of classification with a mean accuracy of 93.3% considering training and validation sets. Several biomolecules were detected at varied abundances between classes, many of which were selected as predictive features in the classifiers including glycerophospholipids and quorum-sensing molecules. The classifiers also enabled correct identification of Gram stain type and genus of the etiologic agent from 3 surgical specimens and all classification levels for clinical specimen isolates.
Conclusions
The MasSpec Pen enables identification of several bacteria at different taxonomic levels in seconds from cultured samples and has potential for culture-independent identification of bacteria directly from clinical samples based on the detection of metabolic species.
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Affiliation(s)
- Sydney C Povilaitis
- Department of Chemistry, The University of Texas at Austin , Austin, TX 78712 , USA
| | - Ashish Chakraborty
- Department of Chemistry, The University of Texas at Austin , Austin, TX 78712 , USA
| | - Lindsey M Kirkpatrick
- Department of Pediatrics, Division of Pediatric Infectious Diseases, J.W. Riley Hospital for Children, Indiana University School of Medicine , Indianapolis, IN 46202 , USA
| | - Rachel D Downey
- Department of Pediatric Infectious Diseases, Dell Children's Medical Group , Austin, TX 78723 , USA
| | - Sarmistha B Hauger
- Department of Pediatrics, Dell Medical School, The University of Texas at Austin , Austin, TX 78712 , USA
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine , Houston, TX 77030 , USA
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Ke M, Elshenawy B, Sheldon H, Arora A, Buffa FM. Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity. Bioessays 2022; 44:e2200084. [PMID: 36068142 DOI: 10.1002/bies.202200084] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
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Affiliation(s)
- May Ke
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Badran Elshenawy
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Helen Sheldon
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Anjali Arora
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Francesca M Buffa
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.,Department of Computing Sciences, Bocconi University, Milano, Italy.,Institute for Data Science and Analytics, Bocconi University, Milano, Italy
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Kondratyeva L, Alekseenko I, Chernov I, Sverdlov E. Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism. BIOLOGY 2022; 11:1208. [PMID: 36009835 PMCID: PMC9404739 DOI: 10.3390/biology11081208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/03/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022]
Abstract
In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.
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Affiliation(s)
- Liya Kondratyeva
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
| | - Irina Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
- Institute of Molecular Genetics of National Research Centre “Kurchatov Institute”, Moscow 123182, Russia
| | - Igor Chernov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
| | - Eugene Sverdlov
- Institute of Molecular Genetics of National Research Centre “Kurchatov Institute”, Moscow 123182, Russia
- Kurchatov Center for Genome Research, National Research Center “Kurchatov Institute”, Moscow 123182, Russia
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