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Zare F, Fleming RMT. Integration of proteomic data with genome-scale metabolic models: A methodological overview. Protein Sci 2024; 33:e5150. [PMID: 39275997 PMCID: PMC11400636 DOI: 10.1002/pro.5150] [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: 01/23/2024] [Revised: 06/29/2024] [Accepted: 08/06/2024] [Indexed: 09/16/2024]
Abstract
The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.
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Affiliation(s)
- Farid Zare
- School of Medicine, University of Galway, Galway, Ireland
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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 PMCID: PMC11404131 DOI: 10.1016/j.xpro.2024.103291] [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: 04/24/2024] [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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Mardinoglu A, Palsson BØ. Genome-scale models in human metabologenomics. Nat Rev Genet 2024:10.1038/s41576-024-00768-0. [PMID: 39300314 DOI: 10.1038/s41576-024-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 09/22/2024]
Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
| | - Bernhard Ø Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
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4
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Narasimha SM, Malpani T, Mohite OS, Nath JS, Raman K. Understanding flux switching in metabolic networks through an analysis of synthetic lethals. NPJ Syst Biol Appl 2024; 10:104. [PMID: 39289347 PMCID: PMC11408705 DOI: 10.1038/s41540-024-00426-5] [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: 03/19/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024] Open
Abstract
Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.
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Affiliation(s)
- Sowmya Manojna Narasimha
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Neuroscience Graduate Program, University of California San Diego, San Diego, CA, 92092, USA
| | - Tanisha Malpani
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
| | - Omkar S Mohite
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark
| | - J Saketha Nath
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Hyderabad, Hyderabad, 502 284, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
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5
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Sarı FZ, Çakır T. Deciphering Antibiotic-Targeted Metabolic Pathways in Acinetobacter baumannii: Insights from Transcriptomics and Genome-Scale Metabolic Modeling. Life (Basel) 2024; 14:1102. [PMID: 39337886 PMCID: PMC11433532 DOI: 10.3390/life14091102] [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: 08/09/2024] [Accepted: 08/29/2024] [Indexed: 09/30/2024] Open
Abstract
In the ongoing battle against antibiotic-resistant infections, Acinetobacter baumannii has emerged as a critical pathogen in healthcare settings. To understand its response to antibiotic-induced stress, we integrated transcriptomic data from various antibiotics (amikacin sulfate, ciprofloxacin, polymyxin-B, and meropenem) with metabolic modeling techniques. Key metabolic pathways, including arginine and proline metabolism, glycine-serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, and propanoate metabolism, were significantly impacted by all four antibiotics across multiple strains. Specifically, biotin metabolism was consistently down-regulated under polymyxin-B treatment, while fatty acid metabolism was perturbed under amikacin sulfate. Ciprofloxacin induced up-regulation in glycerophospholipid metabolism. Validation with an independent dataset focusing on colistin treatment confirmed alterations in fatty acid degradation, elongation, and arginine metabolism. By harmonizing genetic data with metabolic modeling and a metabolite-centric approach, our findings offer insights into the intricate adaptations of A. baumannii under antibiotic pressure, suggesting more effective strategies to combat antibiotic-resistant infections.
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Affiliation(s)
- Fatma Zehra Sarı
- Institute of Biotechnology, Gebze Technical University, Gebze 41400, Kocaeli, Türkiye
| | - Tunahan Çakır
- Institute of Biotechnology, Gebze Technical University, Gebze 41400, Kocaeli, Türkiye
- Department of Bioengineering, Gebze Technical University, Gebze 41400, Kocaeli, Türkiye
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6
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Park SY, Choi DH, Song J, Lakshmanan M, Richelle A, Yoon S, Kontoravdi C, Lewis NE, Lee DY. Driving towards digital biomanufacturing by CHO genome-scale models. Trends Biotechnol 2024; 42:1192-1203. [PMID: 38548556 DOI: 10.1016/j.tibtech.2024.03.001] [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: 01/01/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/20/2024]
Abstract
Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Jinsung Song
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Meiyappan Lakshmanan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Anne Richelle
- Sartorius Corporate Research, Avenue Ariane 5, 1200 Brussels, Belgium
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01850, USA
| | - Cleo Kontoravdi
- Department of Chemical Engineering and Chemical Technology, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.
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7
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Lüleci HB, Jones A, Çakır T. Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease. Eur J Neurosci 2024; 60:4922-4936. [PMID: 39072881 DOI: 10.1111/ejn.16482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 05/14/2024] [Accepted: 07/13/2024] [Indexed: 07/30/2024]
Abstract
Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.
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Affiliation(s)
| | - Attila Jones
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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8
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, 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 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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9
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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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10
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Chowdhury NB, Pokorzynski N, Rucks EA, Ouellette SP, Carabeo RA, Saha R. Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence. mSystems 2024; 9:e0071724. [PMID: 38940523 PMCID: PMC11323709 DOI: 10.1128/msystems.00717-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024] Open
Abstract
Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence. IMPORTANCE This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Nick Pokorzynski
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Elizabeth A. Rucks
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Scot P. Ouellette
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Rey A. Carabeo
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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Li B, Srivastava S, Shaikh M, Mereddy G, Garcia MR, Shah A, Ofori-Anyinam N, Chu T, Cheney N, Yang JH. Bioenergetic stress potentiates antimicrobial resistance and persistence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603336. [PMID: 39026737 PMCID: PMC11257553 DOI: 10.1101/2024.07.12.603336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Antimicrobial resistance (AMR) is a global health crisis and there is an urgent need to better understand AMR mechanisms. Antibiotic treatment alters several aspects of bacterial physiology, including increased ATP utilization, carbon metabolism, and reactive oxygen species (ROS) formation. However, how the "bioenergetic stress" induced by increased ATP utilization affects treatment outcomes is unknown. Here we utilized a synthetic biology approach to study the direct effects of bioenergetic stress on antibiotic efficacy. We engineered a genetic system that constitutively hydrolyzes ATP or NADH in Escherichia coli. We found that bioenergetic stress potentiates AMR evolution via enhanced ROS production, mutagenic break repair, and transcription-coupled repair. We also find that bioenergetic stress potentiates antimicrobial persistence via potentiated stringent response activation. We propose a unifying model that antibiotic-induced antimicrobial resistance and persistence is caused by antibiotic-induced. This has important implications for preventing or curbing the spread of AMR infections.
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Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
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Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
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13
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Wang FS, Zhang HX. Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling. Molecules 2024; 29:2594. [PMID: 38893469 PMCID: PMC11173608 DOI: 10.3390/molecules29112594] [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: 04/12/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.
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Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan;
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14
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Soommat P, Raethong N, Ruengsang R, Thananusak R, Laomettachit T, Laoteng K, Saithong T, Vongsangnak W. Light-Exposed Metabolic Responses of Cordyceps militaris through Transcriptome-Integrated Genome-Scale Modeling. BIOLOGY 2024; 13:139. [PMID: 38534409 DOI: 10.3390/biology13030139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/28/2024]
Abstract
The genome-scale metabolic model (GSMM) of Cordyceps militaris provides a comprehensive basis of carbon assimilation for cell growth and metabolite production. However, the model with a simple mass balance concept shows limited capability to probe the metabolic responses of C. militaris under light exposure. This study, therefore, employed the transcriptome-integrated GSMM approach to extend the investigation of C. militaris's metabolism under light conditions. Through the gene inactivity moderated by metabolism and expression (GIMME) framework, the iPS1474-tiGSMM model was furnished with the transcriptome data, thus providing a simulation that described reasonably well the metabolic responses underlying the phenotypic observation of C. militaris under the particular light conditions. The iPS1474-tiGSMM obviously showed an improved prediction of metabolic fluxes in correlation with the expressed genes involved in the cordycepin and carotenoid biosynthetic pathways under the sucrose culturing conditions. Further analysis of reporter metabolites suggested that the central carbon, purine, and fatty acid metabolisms towards carotenoid biosynthesis were the predominant metabolic processes responsible in light conditions. This finding highlights the key responsive processes enabling the acclimatization of C. militaris metabolism in varying light conditions. This study provides a valuable perspective on manipulating metabolic genes and fluxes towards the target metabolite production of C. militaris.
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Affiliation(s)
- Panyawarin Soommat
- Genetic Engineering and Bioinformatics Program, Graduate School, Kasetsart University, Bangkok 10900, Thailand
| | - Nachon Raethong
- Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand
| | - Ratchaprapa Ruengsang
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Roypim Thananusak
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
| | - Teeraphan Laomettachit
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Kobkul Laoteng
- Industrial Bioprocess Technology Research Team, Functional Ingredients and Food Innovation Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
- Center for Agricultural Systems Biology (CASB), Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok 10150, Thailand
| | - Wanwipa Vongsangnak
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
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15
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Morrissey J, Strain B, Kontoravdi C. Flux Balance Analysis of Mammalian Cell Systems. Methods Mol Biol 2024; 2774:119-134. [PMID: 38441762 DOI: 10.1007/978-1-0716-3718-0_9] [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] [Indexed: 03/07/2024]
Abstract
Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.
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Affiliation(s)
- James Morrissey
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London, UK.
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16
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Eames A, Chandrasekaran S. Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth. PNAS NEXUS 2024; 3:pgae013. [PMID: 38292544 PMCID: PMC10825626 DOI: 10.1093/pnasnexus/pgae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 12/28/2023] [Indexed: 02/01/2024]
Abstract
Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.
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Affiliation(s)
- Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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17
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Chowdhury NB, Pokorzynski N, Rucks EA, Ouellette SP, Carabeo RA, Saha R. Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572198. [PMID: 38187683 PMCID: PMC10769294 DOI: 10.1101/2023.12.18.572198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrient-sufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL's stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68508, USA
| | - Nick Pokorzynski
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Elizabeth A. Rucks
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Scot P. Ouellette
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Rey A. Carabeo
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68508, USA
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18
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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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19
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Carter EL, Constantinidou C, Alam MT. Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. Brief Bioinform 2023; 25:bbad439. [PMID: 38048080 PMCID: PMC10694557 DOI: 10.1093/bib/bbad439] [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: 07/24/2023] [Revised: 09/21/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.
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Affiliation(s)
- Elena Lucy Carter
- Warwick Medical School, University of Warwick, Coventry, CV4 7HL, UK
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20
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Arora G, Banerjee M, Langthasa J, Bhat R, Chatterjee S. Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer. iScience 2023; 26:108081. [PMID: 37876796 PMCID: PMC10590820 DOI: 10.1016/j.isci.2023.108081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/05/2023] [Accepted: 09/25/2023] [Indexed: 10/26/2023] Open
Abstract
The formation of spheroids during epithelial ovarian cancer progression is correlated with peritoneal metastasis, disease recurrence, and poor prognosis. Although metastasis has been demonstrated to be driven by metabolic changes in transformed cells, mechanistic associations between metabolism and phenotypic transitions remain ill-explored. We performed quantitative proteomics to identify protein signatures associated with three distinct phenotypic morphologies (2D monolayers and two geometrically distinct three-dimensional spheroidal states) of the high-grade serous ovarian cancer line OVCAR-3. We obtained disease-driving phenotype-specific metabolic reaction modules and elucidated gene knockout strategies to reduce metabolic alterations that could drive phenotypic transitions. Exploring the DrugBank database, we identified and evaluated drugs that could impair such transitions and, hence, cancer progression. Finally, we experimentally validated our predictions by confirming the ability of one of our predicted drugs, the neuraminidase inhibitor oseltamivir, to inhibit spheroidogenesis in three ovarian cancer cell lines without any cytotoxic effects on untransformed stromal mesothelia.
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Affiliation(s)
- Garhima Arora
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
| | - Mallar Banerjee
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Jimpi Langthasa
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
| | - Ramray Bhat
- Developmental Biology and Genetics, Indian Institute of Science, Bangalore 560012, India
- BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
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21
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Georgouli K, Yeom JS, Blake RC, Navid A. Multi-scale models of whole cells: progress and challenges. Front Cell Dev Biol 2023; 11:1260507. [PMID: 38020904 PMCID: PMC10661945 DOI: 10.3389/fcell.2023.1260507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.
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Affiliation(s)
- Konstantia Georgouli
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jae-Seung Yeom
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Robert C. Blake
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Ali Navid
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
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22
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Casini I, McCubbin T, Esquivel-Elizondo S, Luque GG, Evseeva D, Fink C, Beblawy S, Youngblut ND, Aristilde L, Huson DH, Dräger A, Ley RE, Marcellin E, Angenent LT, Molitor B. An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter. iScience 2023; 26:108016. [PMID: 37854702 PMCID: PMC10579436 DOI: 10.1016/j.isci.2023.108016] [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: 07/27/2023] [Revised: 08/29/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.
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Affiliation(s)
- Isabella Casini
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Sofia Esquivel-Elizondo
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Guillermo G. Luque
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Daria Evseeva
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
| | - Christian Fink
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Sebastian Beblawy
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Nicholas D. Youngblut
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Ludmilla Aristilde
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Daniel H. Huson
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Andreas Dräger
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Ruth E. Ley
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Largus T. Angenent
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
- AG Angenent, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Department of Biological and Chemical Engineering, Aarhus University, Gustav Wieds Vej 10D, 8000 Aarhus C, Denmark
- The Novo Nordisk Foundation CO2 Research Center (CORC), Aarhus University, Gustav Wieds Vej 10C, 8000 Aarhus C, Denmark
| | - Bastian Molitor
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
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23
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Ofori-Anyinam N, Hamblin M, Coldren ML, Li B, Mereddy G, Shaikh M, Shah A, Ranu N, Lu S, Blainey PC, Ma S, Collins JJ, Yang JH. KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.17.562707. [PMID: 37905073 PMCID: PMC10614911 DOI: 10.1101/2023.10.17.562707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQ's efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis . We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.
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24
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Gonçalves DM, Henriques R, Costa RS. Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches. Comput Struct Biotechnol J 2023; 21:4960-4973. [PMID: 37876626 PMCID: PMC10590844 DOI: 10.1016/j.csbj.2023.10.002] [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: 07/25/2023] [Revised: 10/01/2023] [Accepted: 10/01/2023] [Indexed: 10/26/2023] Open
Abstract
The accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository[1].
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Affiliation(s)
- Daniel M. Gonçalves
- INESC-ID, Rua Alves Redol, 9, Lisbon, 1000-029, Portugal
- Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon, 1049-001, Portugal
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, 2829-516, Portugal
| | - Rui Henriques
- INESC-ID, Rua Alves Redol, 9, Lisbon, 1000-029, Portugal
- Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon, 1049-001, Portugal
| | - Rafael S. Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, 2829-516, Portugal
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25
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Chu SW, Wang FS. Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart. BMC Bioinformatics 2023; 24:364. [PMID: 37759157 PMCID: PMC10537911 DOI: 10.1186/s12859-023-05487-7] [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: 06/24/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco's modified eagle medium (DMEM) and Ham's medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham's medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.
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Affiliation(s)
- Sz-Wei Chu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan.
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26
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Eng T, Banerjee D, Menasalvas J, Chen Y, Gin J, Choudhary H, Baidoo E, Chen JH, Ekman A, Kakumanu R, Diercks YL, Codik A, Larabell C, Gladden J, Simmons BA, Keasling JD, Petzold CJ, Mukhopadhyay A. Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering. Cell Rep 2023; 42:113087. [PMID: 37665664 DOI: 10.1016/j.celrep.2023.113087] [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: 04/25/2023] [Revised: 07/10/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.
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Affiliation(s)
- Thomas Eng
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Javier Menasalvas
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yan Chen
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jennifer Gin
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Hemant Choudhary
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biomanufacturing and Biomaterials Department, Sandia National Laboratories, Livermore, CA, USA
| | - Edward Baidoo
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jian Hua Chen
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Axel Ekman
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ramu Kakumanu
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yuzhong Liu Diercks
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Alex Codik
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Carolyn Larabell
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - John Gladden
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biomanufacturing and Biomaterials Department, Sandia National Laboratories, Livermore, CA, USA
| | - Blake A Simmons
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jay D Keasling
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; QB3 Institute, University of California, Berkeley, 5885 Hollis Street, 4th Floor, Emeryville, CA 94608, USA; Department of Chemical & Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, 2970 Horsholm, Denmark; Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Christopher J Petzold
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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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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Gelbach PE, Finley SD. Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. iScience 2023; 26:107569. [PMID: 37664588 PMCID: PMC10474475 DOI: 10.1016/j.isci.2023.107569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/24/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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29
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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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: 13] [Impact Index Per Article: 13.0] [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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Suyama H, Luu LDW, Zhong L, Raftery MJ, Lan R. Integrating proteomic data with metabolic modeling provides insight into key pathways of Bordetella pertussis biofilms. Front Microbiol 2023; 14:1169870. [PMID: 37601354 PMCID: PMC10435875 DOI: 10.3389/fmicb.2023.1169870] [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: 02/20/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Pertussis, commonly known as whooping cough is a severe respiratory disease caused by the bacterium, Bordetella pertussis. Despite widespread vaccination, pertussis resurgence has been observed globally. The development of the current acellular vaccine (ACV) has been based on planktonic studies. However, recent studies have shown that B. pertussis readily forms biofilms. A better understanding of B. pertussis biofilms is important for developing novel vaccines that can target all aspects of B. pertussis infection. This study compared the proteomic expression of biofilm and planktonic B. pertussis cells to identify key changes between the conditions. Major differences were identified in virulence factors including an upregulation of toxins (adenylate cyclase toxin and dermonecrotic toxin) and downregulation of pertactin and type III secretion system proteins in biofilm cells. To further dissect metabolic pathways that are altered during the biofilm lifestyle, the proteomic data was then incorporated into a genome scale metabolic model using the Integrative Metabolic Analysis Tool (iMAT). The generated models predicted that planktonic cells utilised the glyoxylate shunt while biofilm cells completed the full tricarboxylic acid cycle. Differences in processing aspartate, arginine and alanine were identified as well as unique export of valine out of biofilm cells which may have a role in inter-bacterial communication and regulation. Finally, increased polyhydroxybutyrate accumulation and superoxide dismutase activity in biofilm cells may contribute to increased persistence during infection. Taken together, this study modeled major proteomic and metabolic changes that occur in biofilm cells which helps lay the groundwork for further understanding B. pertussis pathogenesis.
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Affiliation(s)
- Hiroki Suyama
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Laurence Don Wai Luu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Ling Zhong
- Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia
| | - Mark J. Raftery
- Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia
| | - Ruiting Lan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
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32
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Qiu S, Yang A, Zeng H. Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook. PLoS Comput Biol 2023; 19:e1011391. [PMID: 37619239 PMCID: PMC10449171 DOI: 10.1371/journal.pcbi.1011391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023] Open
Abstract
In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.
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Affiliation(s)
- Sizhe Qiu
- School of Food and Health, Beijing Technology and Business University, Bejing, China
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Hong Zeng
- School of Food and Health, Beijing Technology and Business University, Bejing, China
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33
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Watanabe K, Wilmanski T, Baloni P, Robinson M, Garcia GG, Hoopmann MR, Midha MK, Baxter DH, Maes M, Morrone SR, Crebs KM, Kapil C, Kusebauch U, Wiedrick J, Lapidus J, Pflieger L, Lausted C, Roach JC, Glusman G, Cummings SR, Schork NJ, Price ND, Hood L, Miller RA, Moritz RL, Rappaport N. Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers. Commun Biol 2023; 6:768. [PMID: 37481675 PMCID: PMC10363145 DOI: 10.1038/s42003-023-05128-y] [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: 11/24/2022] [Accepted: 07/10/2023] [Indexed: 07/24/2023] Open
Abstract
Aging manifests as progressive deteriorations in homeostasis, requiring systems-level perspectives to investigate the gradual molecular dysregulation of underlying biological processes. Here, we report systemic changes in the molecular regulation of biological processes under multiple lifespan-extending interventions. Differential Rank Conservation (DIRAC) analyses of mouse liver proteomics and transcriptomics data show that mechanistically distinct lifespan-extending interventions (acarbose, 17α-estradiol, rapamycin, and calorie restriction) generally tighten the regulation of biological modules. These tightening patterns are similar across the interventions, particularly in processes such as fatty acid oxidation, immune response, and stress response. Differences in DIRAC patterns between proteins and transcripts highlight specific modules which may be tightened via augmented cap-independent translation. Moreover, the systemic shifts in fatty acid metabolism are supported through integrated analysis of liver transcriptomics data with a mouse genome-scale metabolic model. Our findings highlight the power of systems-level approaches for identifying and characterizing the biological processes involved in aging and longevity.
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Affiliation(s)
| | | | - Priyanka Baloni
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | | | - Gonzalo G Garcia
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | | | | | | | - Michal Maes
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, USA
| | | | - Jack Wiedrick
- Oregon Health and Science University, Portland, OR, USA
| | - Jodi Lapidus
- Oregon Health and Science University, Portland, OR, USA
| | - Lance Pflieger
- Institute for Systems Biology, Seattle, WA, USA
- Phenome Health, Seattle, WA, USA
| | | | | | | | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Nicholas J Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Department of Population Sciences and Molecular and Cell Biology, The City of Hope National Medical Center, Duarte, CA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA.
- Phenome Health, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
| | - Richard A Miller
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
- University of Michigan Geriatrics Center, Ann Arbor, MI, USA
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34
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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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35
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Roell G, Schenk C, Anthony WE, Carr RR, Ponukumati A, Kim J, Akhmatskaya E, Foston M, Dantas G, Moon TS, Tang YJ, García Martín H. A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism. ACS Synth Biol 2023; 12:1632-1644. [PMID: 37186551 PMCID: PMC10278598 DOI: 10.1021/acssynbio.2c00618] [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: 11/16/2022] [Indexed: 05/17/2023]
Abstract
Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.
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Affiliation(s)
- Garrett
W. Roell
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Christina Schenk
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
| | - Winston E. Anthony
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
| | - Rhiannon R. Carr
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aditya Ponukumati
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Joonhoon Kim
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Elena Akhmatskaya
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- IKERBASQUE,
Basque Foundation for Science, Bilbao 48009, Spain
| | - Marcus Foston
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Gautam Dantas
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Biomedical Engineering, Washington University
in St. Louis, St Louis, Missouri 63130, United States
- Department
of Molecular Microbiology, Washington University
in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Pediatrics, Washington University School
of Medicine in St Louis, St Louis, Missouri 63110, United States
| | - Tae Seok Moon
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Yinjie J. Tang
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Hector García Martín
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
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36
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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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37
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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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38
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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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39
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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
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40
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Gelbach PE, Finley SD. Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.532000. [PMID: 36993493 PMCID: PMC10052244 DOI: 10.1101/2023.03.09.532000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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41
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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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42
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Strain B, Morrissey J, Antonakoudis A, Kontoravdi C. Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems. Comput Struct Biotechnol J 2023; 21:1543-1549. [PMID: 36879884 PMCID: PMC9984296 DOI: 10.1016/j.csbj.2023.02.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.
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Affiliation(s)
- Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - James Morrissey
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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43
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Lüleci HB, Uzuner D, Çakır T, Thambisetty M. Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics. Methods Mol Biol 2023; 2561:173-189. [PMID: 36399270 DOI: 10.1007/978-1-0716-2655-9_9] [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] [Indexed: 06/16/2023]
Abstract
Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.
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Affiliation(s)
- Hatice Büşra Lüleci
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Dilara Uzuner
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA.
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44
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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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45
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School 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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46
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Orbach SM, Brooks MD, Zhang Y, Campit SE, Bushnell GG, Decker JT, Rebernick RJ, Chandrasekaran S, Wicha MS, Jeruss JS, Shea LD. Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer. Clin Exp Metastasis 2022; 39:865-881. [PMID: 36002598 PMCID: PMC9643644 DOI: 10.1007/s10585-022-10185-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/28/2022] [Indexed: 11/29/2022]
Abstract
Microenvironmental changes in the early metastatic niche may be exploited to identify therapeutic targets to inhibit secondary tumor formation and improve disease outcomes. We dissected the developing lung metastatic niche in a model of metastatic, triple-negative breast cancer using single-cell RNA-sequencing. Lungs were extracted from mice at 7-, 14-, or 21 days after tumor inoculation corresponding to the pre-metastatic, micro-metastatic, and metastatic niche, respectively. The progression of the metastatic niche was marked by an increase in neutrophil infiltration (5% of cells at day 0 to 81% of cells at day 21) and signaling pathways corresponding to the hallmarks of cancer. Importantly, the pre-metastatic and early metastatic niche were composed of immune cells with an anti-cancer phenotype not traditionally associated with metastatic disease. As expected, the metastatic niche exhibited pro-cancer phenotypes. The transition from anti-cancer to pro-cancer phenotypes was directly associated with neutrophil and monocyte behaviors at these time points. Predicted metabolic, transcription factor, and receptor-ligand signaling suggested that changes in the neutrophils likely induced the transitions in the other immune cells. Conditioned medium generated by cells extracted from the pre-metastatic niche successfully inhibited tumor cell proliferation and migration in vitro and the in vivo depletion of pre-metastatic neutrophils and monocytes worsened survival outcomes, thus validating the anti-cancer phenotype of the developing niche. Genes associated with the early anti-cancer response could act as biomarkers that could serve as targets for the treatment of early metastatic disease. Such therapies have the potential to revolutionize clinical outcomes in metastatic breast cancer.
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Affiliation(s)
- Sophia M Orbach
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Michael D Brooks
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Yining Zhang
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Scott E Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA
| | - Grace G Bushnell
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Joseph T Decker
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ryan J Rebernick
- Medical Science Training Program, University of Michigan, Ann Arbor, MI, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Max S Wicha
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Jacqueline S Jeruss
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Lonnie D Shea
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
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47
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Karakurt HU, Pir P. In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways. Integr Biol (Camb) 2022:zyac012. [PMID: 36241207 DOI: 10.1093/intbio/zyac012] [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: 02/04/2022] [Revised: 05/31/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.
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Affiliation(s)
- Hamza Umut Karakurt
- Gebze Technical University, Department of Bioengineering, 41400, Kocaeli, Turkey
| | - Pınar Pir
- Gebze Technical University, Department of Bioengineering, 41400, Kocaeli, Turkey
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48
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [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: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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49
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Baloni P, Arnold M, Buitrago L, Nho K, Moreno H, Huynh K, Brauner B, Louie G, Kueider-Paisley A, Suhre K, Saykin AJ, Ekroos K, Meikle PJ, Hood L, Price ND, Doraiswamy PM, Funk CC, Hernández AI, Kastenmüller G, Baillie R, Han X, Kaddurah-Daouk R. Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer's disease. Commun Biol 2022; 5:1074. [PMID: 36209301 PMCID: PMC9547905 DOI: 10.1038/s42003-022-04011-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 09/20/2022] [Indexed: 11/09/2022] Open
Abstract
Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment.
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Affiliation(s)
- Priyanka Baloni
- Institute for Systems Biology, Seattle, WA, USA
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA
| | - Luna Buitrago
- Department of Neurology/Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Research Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Herman Moreno
- Department of Neurology/Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Kevin Huynh
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Barbara Brauner
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Gregory Louie
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA
| | - Alexandra Kueider-Paisley
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO 24144, Doha, Qatar
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Esbo, Finland
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA
| | | | - P Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA, USA
| | - A Iván Hernández
- Department of Pathology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Xianlin Han
- University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, Durham, NC, USA.
- Department of Medicine, Duke University, Durham, NC, USA.
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
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50
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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