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Wu S, Qu Z, Chen D, Wu H, Caiyin Q, Qiao J. Deciphering and designing microbial communities by genome-scale metabolic modelling. Comput Struct Biotechnol J 2024; 23:1990-2000. [PMID: 38765607 PMCID: PMC11098673 DOI: 10.1016/j.csbj.2024.04.055] [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: 02/03/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/22/2024] Open
Abstract
Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Zheping Qu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Hao Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
| | - Qinggele Caiyin
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Shaoxing Research Institute of Tianjin University, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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2
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Beura S, Das AK, Ghosh A. Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data. STAR Protoc 2024; 5:103291. [PMID: 39235936 DOI: 10.1016/j.xpro.2024.103291] [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: 04/24/2024] [Revised: 05/27/2024] [Accepted: 08/14/2024] [Indexed: 09/07/2024] Open
Abstract
Deciphering the functional differences between diseased and healthy cells requires understanding the alterations in biochemical flux patterns. We present a genome-scale differential flux analysis (GS-DFA) protocol to elucidate these metabolic disparities by integrating condition-specific gene expression data into the human genome-scale metabolic model (humanGEM). In this protocol, we describe the steps to normalize and integrate data into the humanGEM and analyze differential flux across the biochemical network between diseased and healthy cells. For complete details on the use and execution of this protocol, please refer to Nanda et al.1.
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Affiliation(s)
- Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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3
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Tiersma JF, Evers B, Bakker BM, Reijngoud DJ, de Bruyn M, de Jong S, Jalving M. Targeting tumour metabolism in melanoma to enhance response to immune checkpoint inhibition: A balancing act. Cancer Treat Rev 2024; 129:102802. [PMID: 39029155 DOI: 10.1016/j.ctrv.2024.102802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/08/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
Immune checkpoint inhibition has transformed the treatment landscape of advanced melanoma and long-term survival of patients is now possible. However, at least half of the patients do not benefit sufficiently. Metabolic reprogramming is a hallmark of cancer cells and may contribute to both tumour growth and immune evasion by the tumour. Preclinical studies have indeed demonstrated that modulating tumour metabolism can reduce tumour growth while improving the functionality of immune cells. Since metabolic pathways are commonly shared between immune and tumour cells, it is essential to understand how modulating tumour metabolism in patients influences the intricate balance of pro-and anti-tumour immune effects in the tumour microenvironment. The key question is whether modulating tumour metabolism can inhibit tumour cell growth as well as facilitate an anti-tumour immune response. Here, we review current knowledge on the effect of tumour metabolism on the immune response in melanoma. We summarise metabolic pathways in melanoma and non-cancerous cells in the tumour microenvironment and discuss models and techniques available to study the metabolic-immune interaction. Finally, we discuss clinical use of these techniques to improve our understanding of how metabolic interventions can tip the balance towards a favourable, immune permissive microenvironment in melanoma patients.
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Affiliation(s)
- J F Tiersma
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - B Evers
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - B M Bakker
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - D J Reijngoud
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - M de Bruyn
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - S de Jong
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - M Jalving
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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4
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Cortese N, Procopio A, Merola A, Zaffino P, Cosentino C. Applications of genome-scale metabolic models to the study of human diseases: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108397. [PMID: 39232376 DOI: 10.1016/j.cmpb.2024.108397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/25/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND OBJECTIVES Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.
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Affiliation(s)
- Nicola Cortese
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy.
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Meeson KE, Schwartz JM. Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer. NPJ Syst Biol Appl 2024; 10:96. [PMID: 39181893 PMCID: PMC11344801 DOI: 10.1038/s41540-024-00418-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/05/2024] [Indexed: 08/27/2024] Open
Abstract
Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.
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Affiliation(s)
- Kate E Meeson
- School of Biological Sciences, University of Manchester, Manchester, UK
| | - Jean-Marc Schwartz
- School of Biological Sciences, University of Manchester, Manchester, UK.
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6
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Guil F, García R, García JM. Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities. Sci Rep 2024; 14:17265. [PMID: 39068208 PMCID: PMC11283532 DOI: 10.1038/s41598-024-68073-8] [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: 04/23/2024] [Accepted: 07/19/2024] [Indexed: 07/30/2024] Open
Abstract
Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study's success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities.
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Affiliation(s)
- Francisco Guil
- Facultad de Informática, Universidad de Murcia, Murcia, Spain.
| | - Raquel García
- Instituto Murciano de Investigación Biosanitaria (IMIB), Murcia, Spain
| | - José M García
- Facultad de Informática, Universidad de Murcia, Murcia, Spain
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Jin H, Zhang C, Nagenborg J, Juhasz P, Ruder AV, Sikkink CJJM, Mees BME, Waring O, Sluimer JC, Neumann D, Goossens P, Donners MMPC, Mardinoglu A, Biessen EAL. Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability. Cardiovasc Diabetol 2024; 23:240. [PMID: 38978031 PMCID: PMC11232311 DOI: 10.1186/s12933-024-02339-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/26/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. METHODS In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. RESULTS Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. CONCLUSIONS This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.
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Affiliation(s)
- Han Jin
- Central Laboratory, Tianjin Medical University General Hospital, Tianjin, China
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden
| | - Cheng Zhang
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden
| | - Jan Nagenborg
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | | | - Adele V Ruder
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | | | - Barend M E Mees
- Department of Surgery, Maastricht UMC+, Maastricht, the Netherlands
| | - Olivia Waring
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Judith C Sluimer
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland
| | - Dietbert Neumann
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Pieter Goossens
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Marjo M P C Donners
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Adil Mardinoglu
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
| | - Erik A L Biessen
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands.
- Institute for Molecular Cardiovascular Research, RWTH Aachen University, Aachen, Germany.
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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Leonidou N, Ostyn L, Coenye T, Crabbé A, Dräger A. Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities. Microbiol Spectr 2024; 12:e0400623. [PMID: 38652457 PMCID: PMC11237427 DOI: 10.1128/spectrum.04006-23] [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: 11/28/2023] [Accepted: 03/20/2024] [Indexed: 04/25/2024] Open
Abstract
Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Lisa Ostyn
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Tom Coenye
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Aurélie Crabbé
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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11
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Zehetner L, Széliová D, Kraus B, Hernandez Bort JA, Zanghellini J. Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Comput Biol 2024; 20:e1012236. [PMID: 38913731 PMCID: PMC11226097 DOI: 10.1371/journal.pcbi.1012236] [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: 12/09/2023] [Revised: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
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Affiliation(s)
- Leopold Zehetner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Diana Széliová
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Barbara Kraus
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Juan A. Hernandez Bort
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
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12
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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13
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Wu S, Ren W, Hong J, Yang Y, Lu Y. Ablation of histone methyltransferase Suv39h2 in hepatocytes attenuates NASH in mice. Life Sci 2024; 343:122524. [PMID: 38401627 DOI: 10.1016/j.lfs.2024.122524] [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: 01/09/2024] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 02/26/2024]
Abstract
AIMS Non-alcoholic steatohepatitis (NASH) is characterized by aberrant lipid metabolism in hepatocytes. We investigated the involvement of a histone H3K9 methyltransferase Suv39h2 in the pathogenesis of NASH. METHODS AND MATERIALS NASH is induced by feeding the mice with a high-fat high-carbohydrate (HFHC) diet or a high-fat choline-deficient amino acid defined (HFD-CDAA) diet. The Suv39h2f/f mice were crossbred with the Alb-Cre mice to specifically delete Suv39h2 in hepatocytes. KEY FINDINGS Ablation of Suv39h2 in hepatocytes improved insulin sensitivity of the mice fed either the HFHC diet or the CDAA-HFD diet. Importantly, Suv39h2 deletion significantly ameliorated NAFLD as evidenced by reduced lipid accumulation, inflammation, and fibrosis in the liver. RNA-seq uncovered Vanin-1 (Vnn1) as a novel transcriptional target for Suv39h2. Mechanistically, Suv39h2 repressed Vnn1 transcription in hepatocytes exposed to free fatty acids. Consistently, Vanin-1 knockdown normalized lipid accumulation in Suv39h2-null hepatocytes. Importantly, a significant correlation between Suv39h2, Vanin-1, and hepatic triglyceride levels was identified in NASH patients. SIGNIFICANCE Our study uncovers a novel mechanism whereby Suv39h2 may contribute to NASH pathogenesis and suggests that targeting the Suv39h2-Vanin-1 axis may yield novel therapeutic solutions against NASH.
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Affiliation(s)
- Shiqiang Wu
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Wenjing Ren
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Jiameng Hong
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Yuyu Yang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, China.
| | - Yunjie Lu
- Suzhou Medical College, Soochow University, Suzhou, China; Department of Hepatobiliary and Pancreatic Surgery, the third Affiliated Hospital of Soochow University, Changzhou, China; Africa Hepatopancreatobiliary Cancer Consortium, Mayo Clinic, Jacksonville, USA.
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14
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Sertbas M, Ulgen KO. Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies. ACS OMEGA 2024; 9:15535-15546. [PMID: 38585079 PMCID: PMC10993323 DOI: 10.1021/acsomega.4c00392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.
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Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
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15
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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16
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Gupta A, Das D, Taneja R. Targeting Dysregulated Lipid Metabolism in Cancer with Pharmacological Inhibitors. Cancers (Basel) 2024; 16:1313. [PMID: 38610991 PMCID: PMC11010992 DOI: 10.3390/cancers16071313] [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: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024] Open
Abstract
Metabolic plasticity is recognised as a hallmark of cancer cells, enabling adaptation to microenvironmental changes throughout tumour progression. A dysregulated lipid metabolism plays a pivotal role in promoting oncogenesis. Oncogenic signalling pathways, such as PI3K/AKT/mTOR, JAK/STAT, Hippo, and NF-kB, intersect with the lipid metabolism to drive tumour progression. Furthermore, altered lipid signalling in the tumour microenvironment contributes to immune dysfunction, exacerbating oncogenesis. This review examines the role of lipid metabolism in tumour initiation, invasion, metastasis, and cancer stem cell maintenance. We highlight cybernetic networks in lipid metabolism to uncover avenues for cancer diagnostics, prognostics, and therapeutics.
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Affiliation(s)
| | | | - Reshma Taneja
- Department of Physiology, Healthy Longevity and NUS Centre for Cancer Research Translation Research Program, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 2 Medical Drive, MD9, Singapore 117593, Singapore
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17
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Olaverri-Mendizabal D, Valcárcel LV, Barrena N, Rodríguez CJ, Planes FJ. Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism. Brief Bioinform 2024; 25:bbae115. [PMID: 38546323 PMCID: PMC10976907 DOI: 10.1093/bib/bbae115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.
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Affiliation(s)
- Danel Olaverri-Mendizabal
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Campus Universitario, Pamplona 31080, Spain
| | - Naroa Barrena
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Carlos J Rodríguez
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, University of Navarra, Manuel de Lardizábal 13, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Campus Universitario, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Campus Universitario, Pamplona 31080, Spain
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18
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Lee G, Lee SM, Lee S, Jeong CW, Song H, Lee SY, Yun H, Koh Y, Kim HU. Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data. Genome Biol 2024; 25:66. [PMID: 38468344 PMCID: PMC11290261 DOI: 10.1186/s13059-024-03208-8] [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: 01/15/2023] [Accepted: 02/28/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. RESULTS Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. CONCLUSIONS Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.
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Affiliation(s)
- GaRyoung Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Mi Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sungyoung Lee
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Chang Wook Jeong
- Department of Urology, Seoul National University College of Medicine, and Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Hyojin Song
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
- Graduate School of Engineering Biology, BioProcess Engineering Research Center, and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea
| | - Hongseok Yun
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
| | - Youngil Koh
- Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Graduate School of Engineering Biology, BioProcess Engineering Research Center, and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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19
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Chen Y, Gustafsson J, Tafur Rangel A, Anton M, Domenzain I, Kittikunapong C, Li F, Yuan L, Nielsen J, Kerkhoven EJ. Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0. Nat Protoc 2024; 19:629-667. [PMID: 38238583 DOI: 10.1038/s41596-023-00931-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 10/13/2023] [Indexed: 03/10/2024]
Abstract
Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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Affiliation(s)
- Yu Chen
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Johan Gustafsson
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Albert Tafur Rangel
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, Lyngby, Denmark
| | - Mihail Anton
- Department of Life Sciences, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, Sweden
| | - Iván Domenzain
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Feiran Li
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Le Yuan
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
- BioInnovation Institute, Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, Lyngby, Denmark.
- SciLifeLab, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
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20
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Liu W, Wang H, Zhao Q, Tao C, Qu W, Hou Y, Huang R, Sun Z, Zhu G, Jiang X, Fang Y, Gao J, Wu X, Yang Z, Ping R, Chen J, Yang R, Chu T, Zhou J, Fan J, Tang Z, Yang D, Shi Y. Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma. Cancer Commun (Lond) 2024; 44:226-250. [PMID: 38143235 PMCID: PMC10876206 DOI: 10.1002/cac2.12513] [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: 07/04/2023] [Revised: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 12/26/2023] Open
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA. METHODS We performed integrated multiomics analysis in 116 iCCA samples, including whole-exome sequencing, bulk RNA-sequencing and proteome analysis. Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, colony formation assay, RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells. RESULTS Three metabolic subtypes (S1-S3) with subtype-specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune-suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT-rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype-specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen-activated protein kinase (MAPK) signaling. CONCLUSION Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.
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Affiliation(s)
- Weiren Liu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Huqiang Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Qianfu Zhao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Chenyang Tao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Weifeng Qu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Yushan Hou
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Run Huang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zimei Sun
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Guiqi Zhu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Xifei Jiang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Yuan Fang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jun Gao
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Xiaoling Wu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zhixiang Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Rongyu Ping
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Jiafeng Chen
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Rui Yang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Tianhao Chu
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jian Zhou
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Jia Fan
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Zheng Tang
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
| | - Dong Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of LifeomicsBeijingP. R. China
| | - Yinghong Shi
- Department of Liver Surgery and TransplantationLiver Cancer Institute, Zhongshan HospitalFudan UniversityKey Laboratory of Carcinogenesis and Cancer Invasion of Ministry of EducationShanghaiP. R. China
- Research Unit of Liver cancer Recurrence and Metastasis, Chinese Academy of Medical SciencesBeijingP. R. China
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21
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Masson HO, Samoudi M, Robinson CM, Kuo CC, Weiss L, Shams Ud Doha K, Campos A, Tejwani V, Dahodwala H, Menard P, Voldborg BG, Robasky B, Sharfstein ST, Lewis NE. Inferring secretory and metabolic pathway activity from omic data with secCellFie. Metab Eng 2024; 81:273-285. [PMID: 38145748 PMCID: PMC11177574 DOI: 10.1016/j.ymben.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 11/29/2023] [Accepted: 12/14/2023] [Indexed: 12/27/2023]
Abstract
Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Affiliation(s)
- Helen O Masson
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | | | | | - Chih-Chung Kuo
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Linus Weiss
- Department of Biochemistry, Eberhard Karls University of Tübingen, Germany
| | - Km Shams Ud Doha
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Alex Campos
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vijay Tejwani
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Hussain Dahodwala
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Patrice Menard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Bjorn G Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark; National Biologics Facility, Technical University of Denmark, Lyngby, Denmark
| | | | - Susan T Sharfstein
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Nathan E Lewis
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA; Department of Pediatrics, UC San Diego, La Jolla, CA, USA.
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22
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Gwynne PJ, Stocks KLK, Karozichian ES, Pandit A, Hu LT. Metabolic modeling predicts unique drug targets in Borrelia burgdorferi. mSystems 2023; 8:e0083523. [PMID: 37855615 PMCID: PMC10734484 DOI: 10.1128/msystems.00835-23] [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: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 10/20/2023] Open
Abstract
IMPORTANCE Lyme disease is often treated using long courses of antibiotics, which can cause side effects for patients and risks the evolution of antimicrobial resistance. Narrow-spectrum antimicrobials would reduce these risks, but their development has been slow because the Lyme disease bacterium, Borrelia burgdorferi, is difficult to work with in the laboratory. To accelerate the drug discovery pipeline, we developed a computational model of B. burgdorferi's metabolism and used it to predict essential enzymatic reactions whose inhibition prevented growth in silico. These predictions were validated using small-molecule enzyme inhibitors, several of which were shown to have specific activity against B. burgdorferi. Although the specific compounds used are not suitable for clinical use, we aim to use them as lead compounds to develop optimized drugs targeting the pathways discovered here.
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Affiliation(s)
- Peter J. Gwynne
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Kee-Lee K. Stocks
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Elysse S. Karozichian
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Aarya Pandit
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
| | - Linden T. Hu
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA
- Tufts Lyme Disease Initiative, Tufts University, Boston, Massachusetts, USA
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23
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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [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: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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24
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Chen K, Wang F. Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes. FEBS Open Bio 2023; 13:2172-2186. [PMID: 37734920 PMCID: PMC10699103 DOI: 10.1002/2211-5463.13710] [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: 06/21/2023] [Revised: 08/09/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.
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Affiliation(s)
- Ke‐Lin Chen
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
| | - Feng‐Sheng Wang
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
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25
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Sridhar S, Bhalla P, Kullu J, Veerapaneni S, Sahoo S, Bhatt N, Suraishkumar GK. A reactive species reactions module for integration into genome-scale metabolic models for improved insights: Application to cancer. Metab Eng 2023; 80:78-93. [PMID: 37689259 DOI: 10.1016/j.ymben.2023.08.006] [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: 04/03/2023] [Revised: 08/07/2023] [Accepted: 08/28/2023] [Indexed: 09/11/2023]
Abstract
Reactive species (RS) play significant roles in many disease contexts. Despite their crucial roles in diseases including cancer, the RS are not adequately modeled in the genome-scale metabolic (GSM) models, which are used to understand cell metabolism in disease contexts. We have developed a scalable RS reactions module that can be integrated with any Recon 3D-derived human metabolic model, or after fine-tuning, with any metabolic model. With RS-integration, the GSM models of three cancers (basal-like triple negative breast cancer (TNBC), high grade serous ovarian carcinoma (HGSOC) and colorectal cancer (CRC)) built from Recon 3D, precisely highlighted the increases/decreases in fluxes (dysregulation) occurring in important pathways of these cancers. These dysregulations were not prominent in the standard cancer models without the RS module. Further, the results from these RS-integrated cancer GSM models suggest the following decreasing order in the ease of ferroptosis-targeting to treat the cancers: TNBC > HGSOC > CRC.
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Affiliation(s)
- Subasree Sridhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India; Centre for Integrative Biology and Systems medicinE, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Prerna Bhalla
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Justin Kullu
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Sriya Veerapaneni
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Swagatika Sahoo
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Nirav Bhatt
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India; Centre for Integrative Biology and Systems medicinE, Indian Institute of Technology Madras, Chennai, 600 036, India; Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, 600 036, India.
| | - G K Suraishkumar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India.
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26
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Yang F, Hilakivi-Clarke L, Shaha A, Wang Y, Wang X, Deng Y, Lai J, Kang N. Metabolic reprogramming and its clinical implication for liver cancer. Hepatology 2023; 78:1602-1624. [PMID: 36626639 PMCID: PMC10315435 DOI: 10.1097/hep.0000000000000005] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/28/2022] [Indexed: 01/12/2023]
Abstract
Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated β-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.
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Affiliation(s)
- Flora Yang
- BA/MD Joint Admission Scholars Program, University of Minnesota, Minneapolis, Minnesota
| | - Leena Hilakivi-Clarke
- Food Science and Nutrition Section, The Hormel Institute, University of Minnesota, Austin, Minnesota
| | - Aurpita Shaha
- Tumor Microenvironment and Metastasis Section, the Hormel Institute, University of Minnesota, Austin, Minnesota
| | - Yuanguo Wang
- Tumor Microenvironment and Metastasis Section, the Hormel Institute, University of Minnesota, Austin, Minnesota
| | - Xianghu Wang
- Tumor Microenvironment and Metastasis Section, the Hormel Institute, University of Minnesota, Austin, Minnesota
| | - Yibin Deng
- Department of Urology, Masonic Cancer Center, The University of Minnesota Medical School, Minneapolis, Minnesota
| | - Jinping Lai
- Department of Pathology and Laboratory Medicine, Kaiser Permanente Sacramento Medical Center, Sacramento, California
| | - Ningling Kang
- Tumor Microenvironment and Metastasis Section, the Hormel Institute, University of Minnesota, Austin, Minnesota
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27
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Zangene E, Marashi SA, Montazeri H. SL-scan identifies synthetic lethal interactions in cancer using metabolic networks. Sci Rep 2023; 13:15763. [PMID: 37737478 PMCID: PMC10516981 DOI: 10.1038/s41598-023-42992-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023] Open
Abstract
Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies.
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Affiliation(s)
- Ehsan Zangene
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
| | - Hesam Montazeri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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28
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Ambikan AT, Elaldi N, Svensson-Akusjärvi S, Bagci B, Pektas AN, Hewson R, Bagci G, Arasli M, Appelberg S, Mardinoglu A, Sood V, Végvári Á, Benfeitas R, Gupta S, Cetin I, Mirazimi A, Neogi U. Systems-level temporal immune-metabolic profile in Crimean-Congo hemorrhagic fever virus infection. Proc Natl Acad Sci U S A 2023; 120:e2304722120. [PMID: 37669378 PMCID: PMC10500270 DOI: 10.1073/pnas.2304722120] [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: 03/22/2023] [Accepted: 07/21/2023] [Indexed: 09/07/2023] Open
Abstract
Crimean-Congo hemorrhagic fever (CCHF) caused by CCHF virus (CCHFV) is one of the epidemic-prone diseases prioritized by the World Health Organisation as public health emergency with an urgent need for accelerated research. The trajectory of host response against CCHFV is multifarious and remains unknown. Here, we reported the temporal spectrum of pathogenesis following the CCHFV infection using genome-wide blood transcriptomics analysis followed by advanced systems biology analysis, temporal immune-pathogenic alterations, and context-specific progressive and postinfection genome-scale metabolic models (GSMM) on samples collected during the acute (T0), early convalescent (T1), and convalescent-phase (T2). The interplay between the retinoic acid-inducible gene-I-like/nucleotide-binding oligomerization domain-like receptor and tumor necrosis factor signaling governed the trajectory of antiviral immune responses. The rearrangement of intracellular metabolic fluxes toward the amino acid metabolism and metabolic shift toward oxidative phosphorylation and fatty acid oxidation during acute CCHFV infection determine the pathogenicity. The upregulation of the tricarboxylic acid cycle during CCHFV infection, compared to the noninfected healthy control and between the severity groups, indicated an increased energy demand and cellular stress. The upregulation of glycolysis and pyruvate metabolism potentiated energy generation through alternative pathways associated with the severity of the infection. The downregulation of metabolic processes at the convalescent phase identified by blood cell transcriptomics and single-cell type proteomics of five immune cells (CD4+ and CD8+ T cells, CD14+ monocytes, B cells, and NK cells) potentially leads to metabolic rewiring through the recovery due to hyperactivity during the acute phase leading to post-viral fatigue syndrome.
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Affiliation(s)
- Anoop T. Ambikan
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
| | - Nazif Elaldi
- Department of Infectious Diseases and Clinical Microbiology, Medical Faculty, Sivas Cumhuriyet University, Sivas58140, Turkey
| | - Sara Svensson-Akusjärvi
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
| | - Binnur Bagci
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Sivas Cumhuriyet University, Sivas, Turkey
| | - Ayse Nur Pektas
- Cumhuriyet University Advanced Technology Application and Research Center, Sivas Cumhuriyet University, Sivas58140, Turkey
| | - Roger Hewson
- United Kingdom Health Security Agency, Porton Down, Salisbury, WiltshireSP4 0JG, United Kingdom
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, LondonWC1E 7HT, United Kingdom
| | - Gokhan Bagci
- Department of Biochemistry, Faculty of Medicine, Altinbas University, Istanbul34147, Turkey
| | - Mehmet Arasli
- Department of Immunology, Medical Faculty, Bulent Ecevit University, Zonguldak67600, Turkey
| | - Sofia Appelberg
- Public Health Agency of Sweden, Solna, Stockholm-17165, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Kungliga Tekniska Högskolan–Royal Institute of Technology, Stockholm-17121, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Vikas Sood
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
- Department of Biochemistry, School of Chemical and Life Sciences, Jamia Hamdard University, Delhi110062, India
| | - Ákos Végvári
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm-17177, Sweden
| | - Rui Benfeitas
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
| | - Soham Gupta
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
| | - Ilhan Cetin
- Department of Public Health, Medical Faculty, Sivas Cumhuriyet University, Sivas 58140, Turkey
| | - Ali Mirazimi
- Public Health Agency of Sweden, Solna, Stockholm-17165, Sweden
- Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Stockholm-14152, Sweden
- National Veterinary Institute, Uppsala-75189, Sweden
| | - Ujjwal Neogi
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Stockholm-14152, Sweden
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29
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Cunha E, Silva M, Chaves I, Demirci H, Lagoa DR, Lima D, Rocha M, Rocha I, Dias O. The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber. PLoS Comput Biol 2023; 19:e1011499. [PMID: 37729340 PMCID: PMC10545120 DOI: 10.1371/journal.pcbi.1011499] [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: 05/09/2023] [Revised: 10/02/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.
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Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Silva
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
- iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal
| | - Huseyin Demirci
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- SnT/University of Luxembourg, Luxembourg
| | | | - Diogo Lima
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Avenida da República, Quinta do Marquês, Oeiras, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
- LABBELS–Associate Laboratory, Braga, Guimarães, Portugal
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30
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Huang Y, Mohanty V, Dede M, Tsai K, Daher M, Li L, Rezvani K, Chen K. Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nat Commun 2023; 14:4883. [PMID: 37573313 PMCID: PMC10423258 DOI: 10.1038/s41467-023-40457-w] [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] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/26/2023] [Indexed: 08/14/2023] Open
Abstract
Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.
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Affiliation(s)
- Yuefan Huang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, 77030, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kyle Tsai
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Li Li
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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31
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Xiao Y, Yu TJ, Xu Y, Ding R, Wang YP, Jiang YZ, Shao ZM. Emerging therapies in cancer metabolism. Cell Metab 2023; 35:1283-1303. [PMID: 37557070 DOI: 10.1016/j.cmet.2023.07.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/20/2023] [Accepted: 07/17/2023] [Indexed: 08/11/2023]
Abstract
Metabolic reprogramming in cancer is not only a biological hallmark but also reveals treatment vulnerabilities. Numerous metabolic molecules have shown promise as treatment targets to impede tumor progression in preclinical studies, with some advancing to clinical trials. However, the intricacy and adaptability of metabolic networks hinder the effectiveness of metabolic therapies. This review summarizes the metabolic targets for cancer treatment and provides an overview of the current status of clinical trials targeting cancer metabolism. Additionally, we decipher crucial factors that limit the efficacy of metabolism-based therapies and propose future directions. With advances in integrating multi-omics, single-cell, and spatial technologies, as well as the ability to track metabolic adaptation more precisely and dynamically, clinicians can personalize metabolic therapies for improved cancer treatment.
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Affiliation(s)
- Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Tian-Jian Yu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ying Xu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Rui Ding
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi-Ping Wang
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201620, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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32
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [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: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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33
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Gomez Romero S, Boyle N. Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction. Compr Rev Food Sci Food Saf 2023; 22:3422-3443. [PMID: 37306528 DOI: 10.1111/1541-4337.13193] [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: 02/10/2023] [Revised: 05/07/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023]
Abstract
The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.
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Affiliation(s)
- Sandra Gomez Romero
- Quantitative Biosciences and Engineering, Colorado School of Mines, Golden, Colorado, USA
| | - Nanette Boyle
- Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA
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34
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Molversmyr H, Øyås O, Rotnes F, Vik JO. Extracting functionally accurate context-specific models of Atlantic salmon metabolism. NPJ Syst Biol Appl 2023; 9:19. [PMID: 37244928 DOI: 10.1038/s41540-023-00280-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: 01/18/2023] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.
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Affiliation(s)
- Håvard Molversmyr
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
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35
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Jalili M, Scharm M, Wolkenhauer O, Salehzadeh-Yazdi A. Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models. NPJ Syst Biol Appl 2023; 9:15. [PMID: 37210409 DOI: 10.1038/s41540-023-00281-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/10/2023] [Indexed: 05/22/2023] Open
Abstract
Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch University, Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch, South Africa
- Leibniz Institute for Food Systems Biology at the Technical University Munich, Freising, Germany
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36
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Masson HO, Samoudi M, Robinson CM, Kuo CC, Weiss L, Doha KSU, Campos A, Tejwani V, Dahodwala H, Menard P, Voldborg BG, Sharfstein ST, Lewis NE. Inferring secretory and metabolic pathway activity from omic data with secCellFie. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.04.539316. [PMID: 37205389 PMCID: PMC10187314 DOI: 10.1101/2023.05.04.539316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Affiliation(s)
- Helen O. Masson
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | | | | | - Chih-Chung Kuo
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Linus Weiss
- Department of Biochemistry, Eberhard Karls University of Tübingen, Germany
| | - Km Shams Ud Doha
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Alex Campos
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vijay Tejwani
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Hussain Dahodwala
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
- Present address: National Institute for Innovation in Manufacturing Biopharmaceuticals, Newark, Delaware, USA
| | - Patrice Menard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Bjorn G. Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- National Biologics Facility, Technical University of Denmark, Lyngby, Denmark
| | - Susan T. Sharfstein
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Nathan E. Lewis
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
- Department of Pediatrics, UC San Diego, La Jolla, CA, USA
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37
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Dhakal B, Tomita Y, Drew P, Price T, Maddern G, Smith E, Fenix K. Perhexiline: Old Drug, New Tricks? A Summary of Its Anti-Cancer Effects. Molecules 2023; 28:molecules28083624. [PMID: 37110858 PMCID: PMC10145508 DOI: 10.3390/molecules28083624] [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: 03/21/2023] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Cancer metabolic plasticity, including changes in fatty acid metabolism utilisation, is now widely appreciated as a key driver for cancer cell growth, survival and malignancy. Hence, cancer metabolic pathways have been the focus of much recent drug development. Perhexiline is a prophylactic antianginal drug known to act by inhibiting carnitine palmitoyltransferase 1 (CPT1) and 2 (CPT2), mitochondrial enzymes critical for fatty acid metabolism. In this review, we discuss the growing evidence that perhexiline has potent anti-cancer properties when tested as a monotherapy or in combination with traditional chemotherapeutics. We review the CPT1/2 dependent and independent mechanisms of its anti-cancer activities. Finally, we speculate on the clinical feasibility and utility of repurposing perhexiline as an anti-cancer agent, its limitations including known side effects and its potential added benefit of limiting cardiotoxicity induced by other chemotherapeutics.
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Affiliation(s)
- Bimala Dhakal
- Discipline of Surgery, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
| | - Yoko Tomita
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
- Medical Oncology, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Paul Drew
- Discipline of Surgery, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
| | - Timothy Price
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
- Medical Oncology, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Guy Maddern
- Discipline of Surgery, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
| | - Eric Smith
- Discipline of Surgery, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
- Medical Oncology, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
| | - Kevin Fenix
- Discipline of Surgery, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA 5011, Australia
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38
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Walakira A, Skubic C, Nadižar N, Rozman D, Režen T, Mraz M, Moškon M. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Comput Biol Med 2023; 159:106957. [PMID: 37116239 DOI: 10.1016/j.compbiomed.2023.106957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Cene Skubic
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Nadižar
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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Lee G, Lee SM, Kim HU. A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis. Metab Eng 2023; 77:283-293. [PMID: 37075858 DOI: 10.1016/j.ymben.2023.04.008] [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: 02/04/2023] [Revised: 03/20/2023] [Accepted: 04/12/2023] [Indexed: 04/21/2023]
Abstract
Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.
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Affiliation(s)
- GaRyoung Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang Mi Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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40
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Bedia C, Dalmau N, Nielsen LK, Tauler R, Marín de Mas I. A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells. Proteomes 2023; 11:proteomes11020011. [PMID: 37092452 PMCID: PMC10123692 DOI: 10.3390/proteomes11020011] [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: 02/10/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.
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Affiliation(s)
- Carmen Bedia
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Nuria Dalmau
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Lars K Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Romà Tauler
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Rigshospitalet, Denmark
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41
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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
- Corresponding author
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Kim S, Nam Y, Kim MJ, Kwon SH, Jeon J, Shin SJ, Park S, Chang S, Kim HU, Lee YY, Kim HS, Moon M. Proteomic analysis for the effects of non-saponin fraction with rich polysaccharide from Korean Red Ginseng on Alzheimer's disease in a mouse model. J Ginseng Res 2023; 47:302-310. [PMID: 36926613 PMCID: PMC10014184 DOI: 10.1016/j.jgr.2022.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/25/2022] [Accepted: 09/27/2022] [Indexed: 11/07/2022] Open
Abstract
Background The most common type of dementia, Alzheimer's disease (AD), is marked by the formation of extracellular amyloid beta (Aβ) plaques. The impairments of axons and synapses appear in the process of Aβ plaques formation, and this damage could cause neurodegeneration. We previously reported that non-saponin fraction with rich polysaccharide (NFP) from Korean Red Ginseng (KRG) showed neuroprotective effects in AD. However, precise molecular mechanism of the therapeutic effects of NFP from KRG in AD still remains elusive. Methods To investigate the therapeutic mechanisms of NFP from KRG on AD, we conducted proteomic analysis for frontal cortex from vehicle-treated wild-type, vehicle-treated 5XFAD mice, and NFP-treated 5XFAD mice by using nano-LC-ESI-MS/MS. Metabolic network analysis was additionally performed as the effects of NFP appeared to be associated with metabolism according to the proteome analysis. Results Starting from 5,470 proteins, 2,636 proteins were selected for hierarchical clustering analysis, and finally 111 proteins were further selected for protein-protein interaction network analysis. A series of these analyses revealed that proteins associated with synapse and mitochondria might be linked to the therapeutic mechanism of NFP. Subsequent metabolic network analysis via genome-scale metabolic models that represent the three mouse groups showed that there were significant changes in metabolic fluxes of mitochondrial carnitine shuttle pathway and mitochondrial beta-oxidation of polyunsaturated fatty acids. Conclusion Our results suggested that the therapeutic effects of NFP on AD were associated with synaptic- and mitochondrial-related pathways, and they provided targets for further rigorous studies on precise understanding of the molecular mechanism of NFP.
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Affiliation(s)
- Sujin Kim
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea.,Research Institute for Dementia Science, Konyang University, Daejeon, Republic of Korea
| | - Yunkwon Nam
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Min-Jeong Kim
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Seung-Hyun Kwon
- Veterans Medical Research Institute, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Soo Jung Shin
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Soyoon Park
- Department of Microbiology and Molecular Genetics, College of Biological Sciences, University of California, California, United States
| | - Sungjae Chang
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yong Yook Lee
- The Korean Ginseng Research Institute, Korea Ginseng Corporation, Daejeon, Republic of Korea
| | - Hak Su Kim
- Veterans Medical Research Institute, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Minho Moon
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea.,Research Institute for Dementia Science, Konyang University, Daejeon, Republic of Korea
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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Neuroinflammation, Energy and Sphingolipid Metabolism Biomarkers Are Revealed by Metabolic Modeling of Autistic Brains. Biomedicines 2023; 11:biomedicines11020583. [PMID: 36831124 PMCID: PMC9953696 DOI: 10.3390/biomedicines11020583] [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: 01/08/2023] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 02/18/2023] Open
Abstract
Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders generally characterized by repetitive behaviors and difficulties in communication and social behavior. Despite its heterogeneous nature, several metabolic dysregulations are prevalent in individuals with ASD. This work aims to understand ASD brain metabolism by constructing an ASD-specific prefrontal cortex genome-scale metabolic model (GEM) using transcriptomics data to decipher novel neuroinflammatory biomarkers. The healthy and ASD-specific models are compared via uniform sampling to identify ASD-exclusive metabolic features. Noticeably, the results of our simulations and those found in the literature are comparable, supporting the accuracy of our reconstructed ASD model. We identified that several oxidative stress, mitochondrial dysfunction, and inflammatory markers are elevated in ASD. While oxidative phosphorylation fluxes were similar for healthy and ASD-specific models, and the fluxes through the pathway were nearly undisturbed, the tricarboxylic acid (TCA) fluxes indicated disruptions in the pathway. Similarly, the secretions of mitochondrial dysfunction markers such as pyruvate are found to be higher, as well as the activities of oxidative stress marker enzymes like alanine and aspartate aminotransferases (ALT and AST) and glutathione-disulfide reductase (GSR). We also detected abnormalities in the sphingolipid metabolism, which has been implicated in many inflammatory and immune processes, but its relationship with ASD has not been thoroughly explored in the existing literature. We suggest that important sphingolipid metabolites, such as sphingosine-1-phosphate (S1P), ceramide, and glucosylceramide, may be promising biomarkers for the diagnosis of ASD and provide an opportunity for the adoption of early intervention for young children.
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data. Proc Natl Acad Sci U S A 2023; 120:e2217868120. [PMID: 36719923 PMCID: PMC9963017 DOI: 10.1073/pnas.2217868120] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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46
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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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47
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Cheng CT, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer. PLoS One 2023; 18:e0286032. [PMID: 37205704 DOI: 10.1371/journal.pone.0286032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/06/2023] [Indexed: 05/21/2023] Open
Abstract
Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry and Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - Chi-Ying F Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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48
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Shi T, Yu H, Blair RH. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0054. [PMID: 37988745 DOI: 10.1515/sagmb-2022-0054] [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: 11/05/2022] [Accepted: 09/28/2023] [Indexed: 11/23/2023]
Abstract
Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.
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Affiliation(s)
- Tiange Shi
- University at Buffalo, Biostatistics, Buffalo, USA
| | - Han Yu
- Roswell Park Comprehensive Cancer Center, Biostatistics and Bioinformatics, Buffalo, USA
| | - Rachael Hageman Blair
- University at Buffalo, Biostatistics, Institute for Artificial Intelligence and Data Science, Buffalo, USA
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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Magazzù G, Zampieri G, Angione C. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods. Comput Biol Med 2022; 151:106244. [PMID: 36343407 DOI: 10.1016/j.compbiomed.2022.106244] [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: 06/15/2022] [Revised: 10/07/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far. METHODS Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma. RESULTS AND CONCLUSIONS While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.
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Affiliation(s)
- Giuseppe Magazzù
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Department of Biology, University of Padova, Padova, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Centre for Digital Innovation, Teesside University, Middlesbrough, England, United Kingdom; National Horizons Centre, Teesside University, Darlington, England, United Kingdom.
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