Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels
Abstract Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of met...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2020-10-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20209649 |
| _version_ | 1797281795078619136 |
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| author | Lutfu Safak Yilmaz Xuhang Li Shivani Nanda Bennett Fox Frank Schroeder Albertha JM Walhout |
| author_facet | Lutfu Safak Yilmaz Xuhang Li Shivani Nanda Bennett Fox Frank Schroeder Albertha JM Walhout |
| author_sort | Lutfu Safak Yilmaz |
| collection | DOAJ |
| description | Abstract Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue‐relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single‐cell RNA‐sequencing (scRNA‐seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA‐seq continues to provide higher‐resolution gene expression data. |
| first_indexed | 2024-03-07T17:02:36Z |
| format | Article |
| id | doaj.art-1a6ba4b98cde4fe7916e99378d764e28 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T17:02:36Z |
| publishDate | 2020-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-1a6ba4b98cde4fe7916e99378d764e282024-03-03T03:14:10ZengSpringer NatureMolecular Systems Biology1744-42922020-10-011610n/an/a10.15252/msb.20209649Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levelsLutfu Safak Yilmaz0Xuhang Li1Shivani Nanda2Bennett Fox3Frank Schroeder4Albertha JM Walhout5Program in Systems Biology Program in Molecular Medicine University of Massachusetts Medical School Worcester MA USAProgram in Systems Biology Program in Molecular Medicine University of Massachusetts Medical School Worcester MA USAProgram in Systems Biology Program in Molecular Medicine University of Massachusetts Medical School Worcester MA USABoyce Thompson Institute Department of Chemistry and Chemical Biology Cornell University Ithaca NY USABoyce Thompson Institute Department of Chemistry and Chemical Biology Cornell University Ithaca NY USAProgram in Systems Biology Program in Molecular Medicine University of Massachusetts Medical School Worcester MA USAAbstract Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue‐relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single‐cell RNA‐sequencing (scRNA‐seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA‐seq continues to provide higher‐resolution gene expression data.https://doi.org/10.15252/msb.20209649Caenorhabditis elegansdata integrationmetabolic networksingle‐cell RNA‐seqtissue metabolism |
| spellingShingle | Lutfu Safak Yilmaz Xuhang Li Shivani Nanda Bennett Fox Frank Schroeder Albertha JM Walhout Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels Molecular Systems Biology Caenorhabditis elegans data integration metabolic network single‐cell RNA‐seq tissue metabolism |
| title | Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels |
| title_full | Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels |
| title_fullStr | Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels |
| title_full_unstemmed | Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels |
| title_short | Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels |
| title_sort | modeling tissue relevant caenorhabditis elegans metabolism at network pathway reaction and metabolite levels |
| topic | Caenorhabditis elegans data integration metabolic network single‐cell RNA‐seq tissue metabolism |
| url | https://doi.org/10.15252/msb.20209649 |
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