Abstract
Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal–microbe symbioses utilizing low-nutrient diets.
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Data availability
All models have been provided in three formats—SBML (.xml), MATLAB (.mat), and Excel (.xls)—and deposited in GitHub (https://github.com/na423/symbiosis_Warburg). SBML files of the models have also been submitted to the BioModels database [53] with the following identifiers: MODEL1908040002, MODEL1908040003, and MODEL1908040004.
References
Buchner P. Endoymbiosis of animals with plant microorganisms. Chichester, United Kingdom: John Wiley and Sons; 1965.
McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36.
Douglas AE. Multiorganismal insects: diversity and function of resident microorganisms. Annu Rev Entomol. 2015;60:17–34.
McCutcheon JP, Moran NA. Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol. 2012;10:13–26.
Mattson WJ Jr. Herbivory in relation to plant nitrogen content. Annu Rev Ecol Syst. 1980;11:119–61.
Brodbeck BV, Mizell RF, Andersen PC. Physiological and behavioral adaptations of three species of leafhoppers in response to the dilute nutrient content of xylem fluid. J Insect Physiol. 1993;39:73–81.
Redak RA, Purcell AH, Lopes JRS, Blua MJ, Mizell RF, Andersen PC. The biology of xylem fluid feeding insect vectors of xylella fastidiosa and their relation to disease epidemiology. Annu Rev Entomol. 2004;49:243–70.
Bell-Roberts L, Douglas AE, Werner GD. Match and mismatch between dietary switches and microbial partners in plant sap-feeding insects. Proc R Soc B. 2019;286:20190065.
Douglas AE. How multi-partner endosymbioses function. Nat Rev Microbiol. 2016;14:731–43.
Koga R, Bennett GM, Cryan JR, Moran NA. Evolutionary replacement of obligate symbionts in an ancient and diverse insect lineage. Environ Microbiol. 2013;15:2073–81.
McCutcheon JP, McDonald BR, Moran NA. Origin of an alternative genetic code in the extremely small and GC–rich genome of a bacterial symbiont. PLos Genet. 2009;5:e1000565.
McCutcheon JP, Moran NA. Functional convergence in reduced genomes of bacterial symbionts spanning 200 My of evolution. Genome Biol Evol. 2010;2:708–18.
Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, et al. Metabolic complementarity and genomics of the dual bacterial symbiosis of sharpshooters. PLoS Biol. 2006;4:e188.
Moran NA. Accelerated evolution and Muller’s rachet in endosymbiotic bacteria. Proc Natl Acad Sci USA. 1996;93:2873–8.
Ankrah NY, Chouaia B, Douglas AE. The cost of metabolic interactions in symbioses between insects and bacteria with reduced genomes. mBio. 2018;9:e01433–18.
Harvey E, Heys J, Gedeon T. Quantifying the effects of the division of labor in metabolic pathways. J Theo Biol. 2014;360:222–42.
Roell GW, Zha J, Carr RR, Koffas MA, Fong SS, Tang YJ. Engineering microbial consortia by division of labor. Micro Cell Fact. 2019;18:35.
Thommes M, Wang T, Zhao Q, Paschalidis IC, Segrè D. Designing metabolic division of labor in microbial communities. mSyst. 2019;4:e00263–18.
Moran NA, Tran P, Gerardo NM. Symbiosis and insect diversification: an ancient symbiont of sap-feeding insects from the bacterial phylum Bacteroidetes. Appl Environ Microbiol. 2005;71:8802–10.
Jiang Z, Jones DH, Khuri S, Tsinoremas NF, Wyss T, Jander G, Wilson AC. Comparative analysis of genome sequences from four strains of the Buchnera aphidicola Mp endosymbion of the green peach aphid, Myzus persicae. BMC Genom. 2013a;14:917.
Jiang ZF, Xia F, Johnson KW, Brown CD, Bartom E, Tuteja JH, Stevens R, Grossman RL, Brumin M, White KP, Ghanim M. Comparison of the genome sequences of “Candidatus Portiera aleyrodidarum” primary endosymbionts of the whitefly Bemisia tabaci B and Q biotypes. Appl Environ Microbiol. 2013b;79:1757–9.
Moran NA, McLaughlin HJ, Sorek R. The dynamics and time scale of ongoing genomic erosion in symbiotic bacteria. Science. 2009;323:379–82.
Thompson SN. Trehalose—the insect ‘blood’sugar. Adv Insect Physiol. 2003;31:85.
Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotech. 2010;28:245–8.
Hall RJ, Flanagan LA, Bottery MJ, Springthorpe V, Thorpe S, Darby AC, et al. A tale of three species: adaptation of Sodalis glossinidius to tsetse biology, Wigglesworthia metabolism, and host diet. MBio. 2019;10:e02106–18.
Xu X, Zarecki R, Medina S, Ofaim S, Liu X, Chen C, et al. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. ISME J. 2019;13:494–508.
Medlock GL, Carey MA, McDuffie DG, Mundy MB, Giallourou N, Swann JR, et al. Inferring metabolic mechanisms of interaction within a defined gut microbiota. Cell Syst. 2018;7:245–7.
Zimmermann J, Obeng N, Yang W, Pees B, Petersen C, Waschina S, et al. The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans. ISME J. 2020;14:26–38.
Magnúsdóttir S, Thiele I. Modeling metabolism of the human gut microbiome. Curr Opin Biotechnol. 2018;51:90–6.
Russell CW, Bouvaine S, Newell PD, Douglas AE. Shared metabolic pathways in a coevolved insect-bacterial symbiosis. Appl Environ Microbiol. 2013;79:6117–23.
Aristilde L, Reed ML, Wilkes RA, Youngster T, Kukurugya MA, Katz V, et al. Glyphosate-induced specific and widespread perturbations in the metabolome of soil Pseudomonas species. Front Environ Sci. 2017;5:34.
Clasquin MF, Melamud E, Rabinowitz JD. LC‐MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinf. 2012;37:14.11. 11–14.11. 23.
Melamud E, Vastag L, Rabinowitz JD. Metabolomic analysis and visualization engine for LC− MS data. Anal Chem. 2010;82:9818–26.
Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0—making metabolomics more meaningful. Nuc Acids Res. 2015;43:W251–W257.
Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Nat Protoc. 2019;14:639.
Noor E, Flamholz A, Bar-Even A, Davidi D, Milo R, Liebermeister W. The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization. PLoS Comp Biol. 2016;12:e1005167.
Noor E, Flamholz A, Bar-Even A, Davidi D, Milo R, Liebermeister W. The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization. PLoS Comp Biol. 2016;12:e1005167.
Tennessen JM, Baker KD, Lam G, Evans J, Thummel CS. The Drosophila estrogen-related receptor directs a metabolic switch that supports developmental growth. Cell Metab. 2011;13:139–48.
Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–33.
Tennessen JM, Bertagnolli NM, Evans J, Sieber MH, Cox J, Thummel CS. Coordinated metabolic transitions during Drosophila embryogenesis and the onset of aerobic glycolysis. G3. 2014;4:839–50.
Harrison JF, Greenlee KJ, Verberk WC. Functional hypoxia in insects: definition, assessment, and consequences for physiology, ecology, and evolution. Annu Rev Entomol. 2018;63:303–25.
O’Neill LAJ, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016;16:553–65.
Pearce EL, Pearce EJ. Metabolic pathways in immune cell activation and quiescence. Immunity. 2013;38:633–43.
Liberti MV, Locasale JW. The Warburg effect: how does it benefit cancer cells? Trends Biochem Sci. 2016;41:211–8.
Wellen KE, Thompson CB. Cellular metabolic stress: considering how cells respond to nutrient excess. Mol Cell. 2010;40:323–32.
Clasadonte J, Scemes E, Wang Z, Boison D, Haydon PG. Connexin 43-mediated astroglial metabolic networks contribute to the regulation of the sleep-wake cycle. Neuron. 2017;95:1365–80.
Mächler P, Wyss MT, Elsayed M, Stobart J, Gutierrez R, von Faber-Castell A, Baeza-Lehnert F. In vivo evidence for a lactate gradient from astrocytes to neurons. Cell Metab. 2016;23:94–102.
Magistretti PJ, Allaman I. A cellular perspective on brain energy metabolism and functional imaging. Neuron. 2015;86:883–901.
Pellerin L, Pellegri G, Bittar PG, Charnay Y, Bouras C, Martin J-L, et al. Evidence supporting the existence of an activity-dependent astrocyte-neuron lactate shuttle. Dev Neurosci. 1998;20:291–9.
Dubouchaud H, Butterfield GE, Wolfel EE, Bergman BC, Brooks GA. Endurance training, expression, and physiology of LDH, MCT1, and MCT4 in human skeletal muscle. Am J Physiol-Endocrin Metab. 2000;278:E571–9.
Gladden L. Lactate metabolism: a new paradigm for the third millennium. J Physiol. 2004;558:5–30.
Bennett GM, Moran NA. Heritable symbiosis: the advantages and perils of an evolutionary rabbit hole. Proc Natl Acad Sci USA. 2015;112:10169–76.
Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, et al. BioModels: ten-year anniversary. Nuc Acids Res. 2015;43:D542–8.
Acknowledgements
We thank Seung Ho Chung, Lu Liu, Frances Blow and Alyssa Bost for assistance with insect dissections, Jason Dombroskie (Cornell University Insect Collection) for assistance with insect identification, and Brandon Barker (Aristotle Cloud Federation) for assistance with virtual machine image development, made possible by National Science Foundation grant ACI-1541215. This study was funded by NSF grant IOS-1354743 awarded to A.E.D., and a NSF CAREER grant (CBET-1653092) awarded to L.A. Graduate support for RAW was also provided by the NSF Graduate Research Fellowship Program (DGE-1650441).
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NYDA, LA, and AED designed research; NYDA, FZ, DZ, and TK performed research; RAW and LA performed mass spectrometry analysis; NYDA created the models and performed model analysis, NYDA analyzed data; NYDA and AED wrote the first draft of the paper; paper revisions made by all authors.
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Ankrah, N.Y.D., Wilkes, R.A., Zhang, F.Q. et al. Syntrophic splitting of central carbon metabolism in host cells bearing functionally different symbiotic bacteria. ISME J 14, 1982–1993 (2020). https://doi.org/10.1038/s41396-020-0661-z
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DOI: https://doi.org/10.1038/s41396-020-0661-z
