Suppr超能文献

C4GEM,一个用于研究 C4 植物代谢的基因组规模代谢模型。

C4GEM, a genome-scale metabolic model to study C4 plant metabolism.

机构信息

Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland 4072, Australia.

出版信息

Plant Physiol. 2010 Dec;154(4):1871-85. doi: 10.1104/pp.110.166488. Epub 2010 Oct 25.

Abstract

Leaves of C(4) grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C(3) plants, where photosynthetic CO(2) fixation proceeds in the mesophyll (M), the fixation process in C(4) plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C(4) genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C(4) photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C(4) subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C(4) model have been associated with well-annotated C(4) species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C(4) photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C(4) photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C(4) subtypes. Effects of CO(2) leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS cells during photosynthesis and can be used to explore C(4) plant metabolism.

摘要

C(4) 类植物(如玉米[Zea mays]、甘蔗[Saccharum officinarum]和高粱[Sorghum bicolor])的叶片形成了经典的 Kranz 叶解剖结构。与 C(3) 植物不同,在 C(3)植物中,光合作用的 CO(2)固定发生在叶肉(M)中,而在 C(4)植物中,固定过程分布在两种细胞类型中,即 M 细胞和束鞘(BS)细胞。在这里,我们开发了一个用于研究 C(4)光合作用过程中 M 和 BS 细胞中通量分布的 C(4)基因组规模模型(C4GEM)。据我们所知,C4GEM 是第一个封装两种不同细胞类型之间代谢相互作用的大规模代谢模型。C4GEM 基于拟南芥(Arabidopsis thaliana)模型(AraGEM),但通过添加负责代表三种不同 C(4)亚型(NADP-ME[苹果酸酶]、NAD-ME 和磷酸烯醇丙酮酸羧激酶)的反应和转运蛋白进行了扩展。C4GEM 已经过验证,能够合成 47 种生物质成分,由 1588 个独特的反应、1755 个代谢物、83 个细胞器间转运蛋白和 29 个外部转运蛋白(包括通过胞间连丝的转运)组成。常见 C(4)模型中的反应与经过良好注释的 C(4)物种(NADP-ME 亚型)相关联:高粱中有 3557 个基因,玉米中有 11623 个基因,甘蔗中有 3881 个基因。高粱、玉米和甘蔗中分别有 131、135 和 156 个非基因分配的必需反应。通量平衡分析用于评估 C(4)光合作用过程中 M 和 BS 细胞的代谢活性。我们的模拟结果与叶绿体蛋白质组学研究一致,C4GEM 预测了经典的 C(4)光合作用途径及其在 M 和 BS 细胞器功能中的主要作用。该模型还突出了三种不同 C(4)亚型中围绕光系统 I 和光系统 II 的代谢活性差异。还探讨了 CO(2)泄漏的影响。C4GEM 是一种可行的框架,可用于在光合作用过程中模拟 M 和 BS 细胞之间的细胞合作,并可用于研究 C(4)植物代谢。

相似文献

1
C4GEM, a genome-scale metabolic model to study C4 plant metabolism.
Plant Physiol. 2010 Dec;154(4):1871-85. doi: 10.1104/pp.110.166488. Epub 2010 Oct 25.
3
Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism.
PLoS One. 2011;6(7):e21784. doi: 10.1371/journal.pone.0021784. Epub 2011 Jul 6.
4
The energy budget in C photosynthesis: insights from a cell-type-specific electron transport model.
New Phytol. 2018 May;218(3):986-998. doi: 10.1111/nph.15051. Epub 2018 Mar 9.
5
Cellular pattern of photosynthetic gene expression in developing maize leaves.
Genes Dev. 1988 Jan;2(1):106-15. doi: 10.1101/gad.2.1.106.
7
Consequences of C4 differentiation for chloroplast membrane proteomes in maize mesophyll and bundle sheath cells.
Mol Cell Proteomics. 2008 Sep;7(9):1609-38. doi: 10.1074/mcp.M800016-MCP200. Epub 2008 May 2.
8
Towards a dynamic photosynthesis model to guide yield improvement in C4 crops.
Plant J. 2021 Jul;107(2):343-359. doi: 10.1111/tpj.15365. Epub 2021 Aug 6.

引用本文的文献

1
In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.
FEMS Microbiol Rev. 2025 Jan 14;49. doi: 10.1093/femsre/fuaf030.
2
Unveiling organ-specific metabolism of .
Proc Natl Acad Sci U S A. 2025 Jul 22;122(29):e2503406122. doi: 10.1073/pnas.2503406122. Epub 2025 Jul 16.
3
New insights in metabolism modelling to decipher plant-microbe interactions.
New Phytol. 2025 May;246(4):1485-1493. doi: 10.1111/nph.70063. Epub 2025 Mar 21.
4
Design and construction of artificial metabolic pathways for the bioproduction of useful compounds.
Plant Biotechnol (Tokyo). 2024 Sep 25;41(3):261-266. doi: 10.5511/plantbiotechnology.24.0721c.
5
A Guide to Metabolic Network Modeling for Plant Biology.
Plants (Basel). 2025 Feb 6;14(3):484. doi: 10.3390/plants14030484.
6
A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation.
iScience. 2023 Nov 7;26(12):108400. doi: 10.1016/j.isci.2023.108400. eCollection 2023 Dec 15.
8
Extracting functionally accurate context-specific models of Atlantic salmon metabolism.
NPJ Syst Biol Appl. 2023 May 27;9(1):19. doi: 10.1038/s41540-023-00280-x.
9
Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling.
Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad186.
10
Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant Cultures.
ACS Omega. 2023 Jan 13;8(4):3586-3605. doi: 10.1021/acsomega.2c05819. eCollection 2023 Jan 31.

本文引用的文献

1
The evolution of C photosynthesis.
New Phytol. 2004 Feb;161(2):341-370. doi: 10.1111/j.1469-8137.2004.00974.x.
2
The predicted subcellular localisation of the sugarcane proteome.
Funct Plant Biol. 2009 Mar;36(3):242-250. doi: 10.1071/FP08252.
5
Toward design-based engineering of industrial microbes.
Curr Opin Microbiol. 2010 Jun;13(3):255-62. doi: 10.1016/j.mib.2010.02.001. Epub 2010 Mar 11.
6
Microbial systems engineering: first successes and the way ahead.
Bioessays. 2010 Apr;32(4):356-62. doi: 10.1002/bies.200900174.
7
Systems metabolic engineering: genome-scale models and beyond.
Biotechnol J. 2010 Jul;5(7):647-59. doi: 10.1002/biot.200900247.
9
A protocol for generating a high-quality genome-scale metabolic reconstruction.
Nat Protoc. 2010 Jan;5(1):93-121. doi: 10.1038/nprot.2009.203. Epub 2010 Jan 7.
10
AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis.
Plant Physiol. 2010 Feb;152(2):579-89. doi: 10.1104/pp.109.148817. Epub 2009 Dec 31.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验