1
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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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2
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Shannon CP, Lee AH, Tebbutt SJ, Singh A. A Commentary on Multi-omics Data Integration in Systems Vaccinology. J Mol Biol 2024; 436:168522. [PMID: 38458605 DOI: 10.1016/j.jmb.2024.168522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Affiliation(s)
| | - Amy Hy Lee
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
| | - Scott J Tebbutt
- PROOF Centre of Excellence, Vancouver, Canada; Department of Medicine, The University of British Columbia, Vancouver, Canada; Centre for Heart Lung Innovation, Vancouver, Canada
| | - Amrit Singh
- Centre for Heart Lung Innovation, Vancouver, Canada; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, Canada.
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3
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Vincent D, Reddy P, Isenegger D. Integrated Proteomics and Metabolomics of Safflower Petal Wilting and Seed Development. Biomolecules 2024; 14:414. [PMID: 38672431 PMCID: PMC11048707 DOI: 10.3390/biom14040414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist in variety and product development, has not yet been undertaken. In this study, an integrated proteome and metabolic analysis have shown a high complexity of lipophilic proteins and metabolites differentially expressed across organs and tissues during seed development and petal wilting. We demonstrated that these approaches successfully discriminated safflower reproductive organs and developmental stages with the identification of 2179 unique compounds and 3043 peptides matching 724 unique proteins. A comparison between cotyledon and husk tissues revealed the complementarity of using both technologies, with husks mostly featuring metabolites (99%), while cotyledons predominantly yielded peptides (90%). This provided a more complete picture of mechanisms discriminating the seed envelope from what it protected. Furthermore, we showed distinct molecular signatures of petal wilting and colour transition, seed growth, and maturation. We revealed the molecular makeup shift occurring during petal colour transition and wilting, as well as the importance of benzenoids, phenylpropanoids, flavonoids, and pigments. Finally, our study emphasizes that the biochemical mechanisms implicated in the growing and maturing of safflower seeds are complex and far-reaching, as evidenced by AraCyc, PaintOmics, and MetaboAnalyst mapping capabilities. This study provides a new resource for functional knowledge of safflower seed and potentially further enables the precision development of novel products and safflower varieties with biotechnology and molecular farming applications.
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Affiliation(s)
- Delphine Vincent
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC 3083, Australia; (P.R.); (D.I.)
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4
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Munk K, Ilina D, Ziemba L, Brader G, Molin EM. Holomics - a user-friendly R shiny application for multi-omics data integration and analysis. BMC Bioinformatics 2024; 25:93. [PMID: 38438871 PMCID: PMC10913680 DOI: 10.1186/s12859-024-05719-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.
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Affiliation(s)
- Katharina Munk
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Daria Ilina
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Lisa Ziemba
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Günter Brader
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Eva M Molin
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria.
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5
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RPJ, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol 2024; 20:e1011814. [PMID: 38527092 PMCID: PMC10994553 DOI: 10.1371/journal.pcbi.1011814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, Denver, Colorado, United States of America
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Rachel PJ Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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6
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Garcia E, Koh J, Wu X, Sarkhosh A, Liu T. Tissue-specific proteome profile analysis reveals regulatory and stress responsive networks in passion fruit during storage. Sci Rep 2024; 14:3564. [PMID: 38346991 PMCID: PMC10861471 DOI: 10.1038/s41598-024-52557-8] [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: 07/25/2023] [Accepted: 01/20/2024] [Indexed: 02/15/2024] Open
Abstract
Passiflora edulis, commonly known as passion fruit, is a crop with a fragrant aroma and refreshingly tropical flavor that is a valuable source of antioxidants. It offers a unique opportunity for growers because of its adaptability to tropical and subtropical climates. Passion fruit can be sold in the fresh market or used in value-added products, but its postharvest shelf life has not been well-researched, nor have superior cultivars been well-developed. Understanding the proteins expressed at the tissue level during the postharvest stage can help improve fruit quality and extend shelf life. In this study, we carried out comparative proteomics analysis on four passion fruit tissues, the epicarp, mesocarp, endocarp, and pulp, using multiplexed isobaric tandem mass tag (TMT) labeling quantitation. A total of 3352 proteins were identified, including 295 differentially expressed proteins (DEPs). Of these DEPs, 213 showed a fold increase greater than 1.45 (50 proteins) or a fold decrease less than 0.45 (163 proteins) with different patterns among tissue types. Among the DEPs, there were proteins expressed with functions in oxygen scavenging, lipid peroxidation, response to heat stress, and pathogen resistance. Thirty-six proteins were designated as hypothetical proteins were characterized for potential functions in immunity, cell structure, homeostasis, stress response, protein metabolism and miraculin biosynthesis. This research provides insight into tissue-specific pathways that can be further studied within fruit physiology and postharvest shelf life to aid in implementing effective plant breeding programs. Knowing the tissue-specific function of fruit is essential for improving fruit quality, developing new varieties, identifying health benefits, and optimizing processing techniques.
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Affiliation(s)
- Ellen Garcia
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA
| | - Jin Koh
- The Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, 32611, USA
| | - Xingbo Wu
- Department of Environmental Horticulture, Tropical Research and Education Center, University of Florida, Homestead, FL, 33031, USA
| | - Ali Sarkhosh
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
| | - Tie Liu
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
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7
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RP, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574780. [PMID: 38260498 PMCID: PMC10802464 DOI: 10.1101/2024.01.09.574780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Rachel Pj Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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8
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Mengelkoch S, Gassen J, Lev-Ari S, Alley JC, Schüssler-Fiorenza Rose SM, Snyder MP, Slavich GM. Multi-omics in stress and health research: study designs that will drive the field forward. Stress 2024; 27:2321610. [PMID: 38425100 PMCID: PMC11216062 DOI: 10.1080/10253890.2024.2321610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Jeffrey Gassen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Shahar Lev-Ari
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Health Promotion, Tel Aviv University, Tel Aviv, Israel
| | - Jenna C. Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M. Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
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9
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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [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: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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10
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Reitz CJ, Kuzmanov U, Gramolini AO. Multi-omic analyses and network biology in cardiovascular disease. Proteomics 2023; 23:e2200289. [PMID: 37691071 DOI: 10.1002/pmic.202200289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.
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Affiliation(s)
- Cristine J Reitz
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Anthony O Gramolini
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
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11
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Mengelkoch S, Miryam Schüssler-Fiorenza Rose S, Lautman Z, Alley JC, Roos LG, Ehlert B, Moriarity DP, Lancaster S, Snyder MP, Slavich GM. Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations. Brain Behav Immun 2023; 114:475-487. [PMID: 37543247 PMCID: PMC11195542 DOI: 10.1016/j.bbi.2023.07.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
| | | | - Ziv Lautman
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jenna C Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Lydia G Roos
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Benjamin Ehlert
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Daniel P Moriarity
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
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12
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Zhan C, Tang T, Wu E, Zhang Y, He M, Wu R, Bi C, Wang J, Zhang Y, Shen B. From multi-omics approaches to personalized medicine in myocardial infarction. Front Cardiovasc Med 2023; 10:1250340. [PMID: 37965091 PMCID: PMC10642346 DOI: 10.3389/fcvm.2023.1250340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.
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Affiliation(s)
- Chaoying Zhan
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Erman Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yuxin Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Mengqiao He
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Rongrong Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Cheng Bi
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiao Wang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yingbo Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Bairong Shen
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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13
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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14
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Gawron P, Hoksza D, Piñero J, Peña-Chilet M, Esteban-Medina M, Fernandez-Rueda JL, Colonna V, Smula E, Heirendt L, Ancien F, Groues V, Satagopam VP, Schneider R, Dopazo J, Furlong LI, Ostaszewski M. Visualization of automatically combined disease maps and pathway diagrams for rare diseases. FRONTIERS IN BIOINFORMATICS 2023; 3:1101505. [PMID: 37502697 PMCID: PMC10369067 DOI: 10.3389/fbinf.2023.1101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 07/29/2023] Open
Abstract
Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
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Affiliation(s)
- Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Prague, Czechia
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | | | | | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council of Italy, Naples, Rome
- Department of Genetics, Genomics and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ewa Smula
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - François Ancien
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Valentin Groues
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Joaquin Dopazo
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
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15
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Lee IC, Tumanov S, Wong JW, Stocker R, Ho JW. Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER. iScience 2023; 26:106881. [PMID: 37260745 PMCID: PMC10227420 DOI: 10.1016/j.isci.2023.106881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 03/13/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.
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Affiliation(s)
- Ian C.H. Lee
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Sergey Tumanov
- Heart Research Institute, 7 Eliza Street, Newtown, NSW 2042, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Jason W.H. Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre for PanorOmic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Roland Stocker
- Heart Research Institute, 7 Eliza Street, Newtown, NSW 2042, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Joshua W.K. Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- Centre for PanorOmic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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16
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Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)-The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
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17
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Voronezhskaya V, Volkova P, Bitarishvili S, Shesterikova E, Podlutskii M, Clement G, Meyer C, Duarte GT, Kudin M, Garbaruk D, Turchin L, Kazakova E. Multi-Omics Analysis of Vicia cracca Responses to Chronic Radiation Exposure in the Chernobyl Exclusion Zone. PLANTS (BASEL, SWITZERLAND) 2023; 12:2318. [PMID: 37375943 DOI: 10.3390/plants12122318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/09/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023]
Abstract
Our understanding of the long-term consequences of chronic ionising radiation for living organisms remains scarce. Modern molecular biology techniques are helpful tools for researching pollutant effects on biota. To reveal the molecular phenotype of plants growing under chronic radiation exposure, we sampled Vicia cracca L. plants in the Chernobyl exclusion zone and areas with normal radiation backgrounds. We performed a detailed analysis of soil and gene expression patterns and conducted coordinated multi-omics analyses of plant samples, including transcriptomics, proteomics, and metabolomics. Plants growing under chronic radiation exposure showed complex and multidirectional biological effects, including significant alterations in the metabolism and gene expression patterns of irradiated plants. We revealed profound changes in carbon metabolism, nitrogen reallocation, and photosynthesis. These plants showed signs of DNA damage, redox imbalance, and stress responses. The upregulation of histones, chaperones, peroxidases, and secondary metabolism was noted.
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Affiliation(s)
| | | | | | | | | | - Gilles Clement
- Institute Jean-Pierre Bourgin (IJPB), INRAE, AgroParisTech, Université Paris-Saclay, 78000 Versailles, France
| | - Christian Meyer
- Institute Jean-Pierre Bourgin (IJPB), INRAE, AgroParisTech, Université Paris-Saclay, 78000 Versailles, France
| | | | - Maksim Kudin
- Polesye State Radiation-Ecological Reserve, 247618 Khoiniki, Belarus
| | - Dmitrii Garbaruk
- Polesye State Radiation-Ecological Reserve, 247618 Khoiniki, Belarus
| | - Larisa Turchin
- Polesye State Radiation-Ecological Reserve, 247618 Khoiniki, Belarus
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18
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Lopez-Ibañez J, Pazos F, Chagoyen M. MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis. Nucleic Acids Res 2023:7161529. [PMID: 37178003 DOI: 10.1093/nar/gkad405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/17/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023] Open
Abstract
MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE3, accessible at http://csbg.cnb.csic.es/mbrole3. This new version contains updated annotations from previously included databases as well as a wide variety of new functional annotations, such as additional pathway databases and Gene Ontology terms. Of special relevance is the inclusion of a new category of annotations, 'indirect annotations', extracted from the scientific literature and from curated chemical-protein associations. The latter allows to analyze enriched annotations of the proteins known to interact with the set of chemical compounds of interest. Results are provided in the form of interactive tables, formatted data to download, and graphical plots.
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Affiliation(s)
- Javier Lopez-Ibañez
- Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain
| | - Florencio Pazos
- Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain
| | - Monica Chagoyen
- Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain
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19
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Wang RS, Maron BA, Loscalzo J. Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases. Arterioscler Thromb Vasc Biol 2023; 43:493-503. [PMID: 36794589 PMCID: PMC10038904 DOI: 10.1161/atvbaha.122.318731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.
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Affiliation(s)
- Rui-Sheng Wang
- Division of Cardiovascular Medicine
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Joseph Loscalzo
- Division of Cardiovascular Medicine
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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20
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Palshikar MG, Min X, Crystal A, Meng J, Hilchey SP, Zand MS, Thakar J. Executable Network Models of Integrated Multiomics Data. J Proteome Res 2023; 22:1546-1556. [PMID: 37000949 PMCID: PMC10167691 DOI: 10.1021/acs.jproteome.2c00730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.
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21
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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22
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Kurbatov I, Dolgalev G, Arzumanian V, Kiseleva O, Poverennaya E. The Knowns and Unknowns in Protein-Metabolite Interactions. Int J Mol Sci 2023; 24:ijms24044155. [PMID: 36835565 PMCID: PMC9964805 DOI: 10.3390/ijms24044155] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.
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Li H, Shi M, Ren K, Zhang L, Ye W, Zhang W, Cheng Y, Xia XQ. Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities. Bioinformatics 2022; 39:6865031. [PMID: 36458930 PMCID: PMC9825776 DOI: 10.1093/bioinformatics/btac777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/15/2022] [Accepted: 12/01/2022] [Indexed: 12/03/2022] Open
Abstract
SUMMARY With the continuous development of high-throughput sequencing technology, bioinformatic analysis of omics data plays an increasingly important role in life science research. Many R packages are widely used for omics analysis, such as DESeq2, clusterProfiler and STRINGdb. And some online tools based on them have been developed to free bench scientists from programming with these R packages. However, the charts generated by these tools are usually in a fixed, non-editable format and often fail to clearly demonstrate the details the researchers intend to express. To address these issues, we have created Visual Omics, an online tool for omics data analysis and scientific chart editing. Visual Omics integrates multiple omics analyses which include differential expression analysis, enrichment analysis, protein domain prediction and protein-protein interaction analysis with extensive graph presentations. It can also independently plot and customize basic charts that are involved in omics analysis, such as various PCA/PCoA plots, bar plots, box plots, heat maps, set intersection diagrams, bubble charts and volcano plots. A distinguishing feature of Visual Omics is that it allows users to perform one-stop omics data analyses without programming, iteratively explore the form and layout of graphs online and fine-tune parameters to generate charts that meet publication requirements. AVAILABILITY AND IMPLEMENTATION Visual Omics can be used at http://bioinfo.ihb.ac.cn/visomics. Source code can be downloaded at http://bioinfo.ihb.ac.cn/software/visomics/visomics-1.1.tar.gz. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Heng Li
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China,College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mijuan Shi
- To whom correspondence should be addressed. or
| | - Keyi Ren
- College of Fisheries and Life Science, Dalian Ocean University, Dalian 116023, China
| | - Lei Zhang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China,College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weidong Ye
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China,College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wanting Zhang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingyin Cheng
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China
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Yang L, Yang Y, Huang L, Cui X, Liu Y. From single- to multi-omics: future research trends in medicinal plants. Brief Bioinform 2022; 24:6840072. [PMID: 36416120 PMCID: PMC9851310 DOI: 10.1093/bib/bbac485] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/25/2022] Open
Abstract
Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.
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Affiliation(s)
- Lifang Yang
- Kunming University of Science and Technology, China
| | - Ye Yang
- Kunming University of Science and Technology, China
| | - Luqi Huang
- the academician of the Chinese Academy of Engineering, studies the development of traditional Chinese medicine, Chinese Academy of Chinese Medical Sciences, China
| | - Xiuming Cui
- Corresponding authors. X. M. Cui, Yunnan Provincial Key Laboratory of Panax notoginseng, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan 650500, China. E-mail: ; Y. Liu, Yunnan Provincial Key Laboratory of Panax notoginseng, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan 650500, China. E-mail:
| | - Yuan Liu
- Corresponding authors. X. M. Cui, Yunnan Provincial Key Laboratory of Panax notoginseng, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan 650500, China. E-mail: ; Y. Liu, Yunnan Provincial Key Laboratory of Panax notoginseng, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan 650500, China. E-mail:
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Lopez-Ortiz C, Edwards M, Natarajan P, Pacheco-Valenciana A, Nimmakayala P, Adjeroh DA, Sirbu C, Reddy UK. Peppers in Diet: Genome-Wide Transcriptome and Metabolome Changes in Drosophila melanogaster. Int J Mol Sci 2022; 23:9924. [PMID: 36077322 PMCID: PMC9455967 DOI: 10.3390/ijms23179924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
The habanero pepper (Capsicum chinense) is an increasingly important spice and vegetable crop worldwide because of its high capsaicin content and pungent flavor. Diets supplemented with the phytochemicals found in habanero peppers might cause shifts in an organism's metabolism and gene expression. Thus, understanding how these interactions occur can reveal the potential health effects associated with such changes. We performed transcriptomic and metabolomic analyses of Drosophila melanogaster adult flies reared on a habanero pepper diet. We found 539 genes/59 metabolites that were differentially expressed/accumulated in flies fed a pepper versus control diet. Transcriptome results indicated that olfactory sensitivity and behavioral responses to the pepper diet were mediated by olfactory and nutrient-related genes including gustatory receptors (Gr63a, Gr66a, and Gr89a), odorant receptors (Or23a, Or59a, Or82a, and Orco), and odorant-binding proteins (Obp28a, Obp83a, Obp83b, Obp93a, and Obp99a). Metabolome analysis revealed that campesterol, sitosterol, and sucrose were highly upregulated and azelaic acid, ethyl phosphoric acid, and citric acid were the major metabolites downregulated in response to the habanero pepper diet. Further investigation by integration analysis between transcriptome and metabolome data at gene pathway levels revealed six unique enriched pathways, including phenylalanine metabolism; insect hormone biosynthesis; pyrimidine metabolism; glyoxylate, and dicarboxylate metabolism; glycine, serine, threonine metabolism; and glycerolipid metabolism. In view of the transcriptome and metabolome findings, our comprehensive analysis of the response to a pepper diet in Drosophila have implications for exploring the molecular mechanism of pepper consumption.
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Affiliation(s)
- Carlos Lopez-Ortiz
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
| | - Mary Edwards
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
| | - Purushothaman Natarajan
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
| | - Armando Pacheco-Valenciana
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
| | - Padma Nimmakayala
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
| | - Donald A Adjeroh
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Cristian Sirbu
- Charleston Area Medical Center, Institute for Academic Medicine, Charleston, WV 25304, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University School of Medicine, Charleston Division, Charleston, WV 25304, USA
| | - Umesh K Reddy
- Department of Biology, Gus R. Douglass Institute, West Virginia State University, Institute, WV 25112, USA
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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Sauceda C, Bayne C, Sudqi K, Gonzalez A, Dulai PS, Knight R, Gonzalez DJ, Gonzalez CG. Stool multi-omics for the study of host-microbe interactions in inflammatory bowel disease. Gut Microbes 2022; 14:2154092. [PMID: 36503356 PMCID: PMC9746627 DOI: 10.1080/19490976.2022.2154092] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/04/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
Inflammatory Bowel Disease (IBD) is a chronic immune-mediated inflammatory disease of the gastrointestinal tract that is a growing public burden. Gut microbes and their interactions with hosts play a crucial role in disease pathogenesis and progression. These interactions are complex, spanning multiple physiological systems and data types, making comprehensive disease assessment difficult, and often overwhelming single-omic capabilities. Stool-based multi-omics is a promising approach for characterizing host-gut microbiome interactions using deep integration of technologies such as 16S rRNA sequencing, shotgun metagenomics, meta-transcriptomics, metabolomics, and metaproteomics. The wealth of information generated through multi-omic studies is poised to usher in advancements in IBD research and precision medicine. This review highlights historical and recent findings from stool-based muti-omic studies that have contributed to unraveling IBD's complexity. Finally, we discuss common pitfalls, issues, and limitations, and how future pipelines should address them to standardize multi-omics in IBD research and beyond.
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Affiliation(s)
- Consuelo Sauceda
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Charlie Bayne
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Khadijeh Sudqi
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Parambir S. Dulai
- Division of Gastroenterology and Hepatology, Northwestern University, Chicago, IL, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - David J. Gonzalez
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Carlos G. Gonzalez
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
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