Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea Mays

Overview

Journal Front Plant Sci

Date 2016 Jul 28

PMID 27462343

Citations 13

Authors

David Toubiana

Wentao Xue

Nengyi Zhang

Karl Kremling

Amit Gur

Shai Pilosof

Yves Gibon

Mark Stitt

Edward S Buckler

Alisdair R Fernie

Aaron Fait

Affiliations

Soon will be listed here.

Abstract

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H(2) tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H(2) scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2' is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

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References

Lipka A, Gore M, Magallanes-Lundback M, Mesberg A, Lin H, Tiede T . Genome-wide association study and pathway-level analysis of tocochromanol levels in maize grain. G3 (Bethesda). 2013; 3(8):1287-99. PMC: 3737168. DOI: 10.1534/g3.113.006148. View

Sulpice R, Trenkamp S, Steinfath M, Usadel B, Gibon Y, Witucka-Wall H . Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions. Plant Cell. 2010; 22(8):2872-93. PMC: 2947169. DOI: 10.1105/tpc.110.076653. View

Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A, Grieder C . Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci U S A. 2012; 109(23):8872-7. PMC: 3384205. DOI: 10.1073/pnas.1120813109. View

Larhlimi A, Blachon S, Selbig J, Nikoloski Z . Robustness of metabolic networks: a review of existing definitions. Biosystems. 2011; 106(1):1-8. DOI: 10.1016/j.biosystems.2011.06.002. View

Galili G, Hofgen R . Metabolic engineering of amino acids and storage proteins in plants. Metab Eng. 2002; 4(1):3-11. DOI: 10.1006/mben.2001.0203. View

DiTomaso J, Hart J, Kochian L . Transport kinetics and metabolism of exogenously applied putrescine in roots of intact maize seedlings. Plant Physiol. 1992; 98(2):611-20. PMC: 1080234. DOI: 10.1104/pp.98.2.611. View

Yu J, Buckler E . Genetic association mapping and genome organization of maize. Curr Opin Biotechnol. 2006; 17(2):155-60. DOI: 10.1016/j.copbio.2006.02.003. View

Verslues P, Lasky J, Juenger T, Liu T, Kumar M . Genome-wide association mapping combined with reverse genetics identifies new effectors of low water potential-induced proline accumulation in Arabidopsis. Plant Physiol. 2013; 164(1):144-59. PMC: 3875797. DOI: 10.1104/pp.113.224014. View

Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D . Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504. PMC: 403769. DOI: 10.1101/gr.1239303. View

10.

Klamt S, Haus U, Theis F . Hypergraphs and cellular networks. PLoS Comput Biol. 2009; 5(5):e1000385. PMC: 2673028. DOI: 10.1371/journal.pcbi.1000385. View

11.

Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer L . Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell. 2001; 13(1):11-29. PMC: 2652711. DOI: 10.1105/tpc.13.1.11. View

12.

Sweetlove L, Beard K, Nunes-Nesi A, Fernie A, Ratcliffe R . Not just a circle: flux modes in the plant TCA cycle. Trends Plant Sci. 2010; 15(8):462-70. DOI: 10.1016/j.tplants.2010.05.006. View

13.

Scossa F, Brotman Y, De Abreu E Lima F, Willmitzer L, Nikoloski Z, Tohge T . Genomics-based strategies for the use of natural variation in the improvement of crop metabolism. Plant Sci. 2015; 242:47-64. DOI: 10.1016/j.plantsci.2015.05.021. View

14.

Karp P, Ouzounis C, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahren D . Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 2005; 33(19):6083-9. PMC: 1266070. DOI: 10.1093/nar/gki892. View

15.

Toubiana D, Batushansky A, Tzfadia O, Scossa F, Khan A, Barak S . Combined correlation-based network and mQTL analyses efficiently identified loci for branched-chain amino acid, serine to threonine, and proline metabolism in tomato seeds. Plant J. 2014; 81(1):121-33. DOI: 10.1111/tpj.12717. View

16.

Kumar A, Suthers P, Maranas C . MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics. 2012; 13:6. PMC: 3277463. DOI: 10.1186/1471-2105-13-6. View

17.

Collakova E, Yen J, Senger R . Are we ready for genome-scale modeling in plants?. Plant Sci. 2012; 191-192:53-70. DOI: 10.1016/j.plantsci.2012.04.010. View

18.

Frary A, Nesbitt T, Grandillo S, Knaap E, Cong B, Liu J . fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000; 289(5476):85-8. DOI: 10.1126/science.289.5476.85. View

19.

Elowitz M, Levine A, Siggia E, Swain P . Stochastic gene expression in a single cell. Science. 2002; 297(5584):1183-6. DOI: 10.1126/science.1070919. View

20.

Giege P, Heazlewood J, Roessner-Tunali U, Millar A, Fernie A, Leaver C . Enzymes of glycolysis are functionally associated with the mitochondrion in Arabidopsis cells. Plant Cell. 2003; 15(9):2140-51. PMC: 181336. DOI: 10.1105/tpc.012500. View