Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study
ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the appl...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2025-01-01
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| Series: | mSystems |
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| Online Access: | https://journals.asm.org/doi/10.1128/msystems.01105-24 |
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| author | M. A. Kulyashov R. Hamilton Y. Afshin S. K. Kolmykov T. S. Sokolova T. M. Khlebodarova M. G. Kalyuzhnaya I. R. Akberdin |
| author_facet | M. A. Kulyashov R. Hamilton Y. Afshin S. K. Kolmykov T. S. Sokolova T. M. Khlebodarova M. G. Kalyuzhnaya I. R. Akberdin |
| author_sort | M. A. Kulyashov |
| collection | DOAJ |
| description | ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20ZR, a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized iIA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme (fae1-2), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase (fdhAB) and fae homologs (fae1-2 and fae3) were investigated via mutagenesis. The phenotype of the fdhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20ZR mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes.IMPORTANCEThe interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20ZR. M. alcaliphilum 20ZR is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations. |
| format | Article |
| id | doaj-art-f0803f82a0144b7197d07bae46aab013 |
| institution | Kabale University |
| issn | 2379-5077 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj-art-f0803f82a0144b7197d07bae46aab0132025-01-21T14:00:28ZengAmerican Society for MicrobiologymSystems2379-50772025-01-0110110.1128/msystems.01105-24Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case studyM. A. Kulyashov0R. Hamilton1Y. Afshin2S. K. Kolmykov3T. S. Sokolova4T. M. Khlebodarova5M. G. Kalyuzhnaya6I. R. Akberdin7Department of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, RussiaDepartment of Biology and Viral Information Institute, San Diego State University, San Diego, California, USADepartment of Biology and Viral Information Institute, San Diego State University, San Diego, California, USADepartment of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, RussiaDepartment of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, RussiaDepartment of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, RussiaDepartment of Biology and Viral Information Institute, San Diego State University, San Diego, California, USADepartment of Computational Biology, Scientific Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, RussiaABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20ZR, a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized iIA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme (fae1-2), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase (fdhAB) and fae homologs (fae1-2 and fae3) were investigated via mutagenesis. The phenotype of the fdhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20ZR mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes.IMPORTANCEThe interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20ZR. M. alcaliphilum 20ZR is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.https://journals.asm.org/doi/10.1128/msystems.01105-24context-specific genome-scale metabolic modelingmethanotrophymethane-utilizing bacteriaMethylotuvimicrobium alcaliphilum 20ZR |
| spellingShingle | M. A. Kulyashov R. Hamilton Y. Afshin S. K. Kolmykov T. S. Sokolova T. M. Khlebodarova M. G. Kalyuzhnaya I. R. Akberdin Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study mSystems context-specific genome-scale metabolic modeling methanotrophy methane-utilizing bacteria Methylotuvimicrobium alcaliphilum 20ZR |
| title | Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study |
| title_full | Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study |
| title_fullStr | Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study |
| title_full_unstemmed | Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study |
| title_short | Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study |
| title_sort | modification and analysis of context specific genome scale metabolic models methane utilizing microbial chassis as a case study |
| topic | context-specific genome-scale metabolic modeling methanotrophy methane-utilizing bacteria Methylotuvimicrobium alcaliphilum 20ZR |
| url | https://journals.asm.org/doi/10.1128/msystems.01105-24 |
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