Metabolic modelling of a bacterial/animal symbiosis. Sandy Macdonald and Gavin Thomas Department of Biology University of York Angela Douglas Department of Entomology Cornell University USA. Outline. The Buchnera / aphid symbiosis The Buchnera genome
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Sandy Macdonald and Gavin Thomas
Department of Biology
University of York
Department of Entomology
The pea aphid, Acyrthosiphon pisum.
Buchnera aphidicola sp. APS is the primary symbiont of the pea aphid
Gene loss and gene gain
Gene loss only
E. coli K-12
Buchnera sp. APS
The genome sequences of primary insect symbionts reveal clear descent from an ancestral -proteobacterium
Ancestor ~3.5 Mb
200 My ago…
The Buchnera genome has undergone reductive evolution by a series of deletion and inactivations of existing genes
Exploiting E. coli to rapidly produce a high quality metabolic reconstruction of Buchnera APS
The iJR904 metabolic model of E. coli K-12
Metabolic model which contains the reactions catalysed by 904 gene products from E. coli.
Removing isolated reactions
During the reductive evolution, some pathways are in the process of being lost and still have remnants left.
APS has a number of isolated enzymes, e.g. SerC, which were included in the original mapping.
Some pathways for EAAs are not complete from the in silico reconstructions, but are known to function in vivo. Infer promiscuous enzymes to fill these gaps.
Full gene complement for synthesis of 4 EAAs (histidine, tryptophan, threonine and lysine), as well as the non-essentials arginine, cysteine and glycine.
Also very short of transporters. Many have been inferred.
196 gene products
240 compounds (39% of iJR904)
263 reactions (27% of iJR904)
35% of reactions for EAA biosynthesis.
Red hexagon – high flux precursor Blue square - EAA
Red circle – low flux precursor Blue circle – biomass component
Grey triangle – inferred reaction
Thomas et al., (2009) BMC Systems Biology 3:24.
Analysis of the reconstruction using constraint-based modelling (flux balance analysis)
Flux balance analysis
The steady-state assumption states that for each metabolite the sum of the fluxes producing that metabolite is equal to the sum of the fluxes consuming that metabolite.
Running FBA with the Buchnera model
FBA is essentially an optimisation problem solved using linear programming, i.e. it finds the optimal route of fluxes through the network to get the desired output (the objective function).
Used reduced version of the ‘biomass’ reaction of E. coli as the objective function.
(0000050) ACP + (0000050) fmnh2 + (0000050) pnto-R + (0000050) gthrd + (0000050) thmpp + (0000050) sheme + (0000050) btn + (0000050) hemeO + (0203000) gtp + (0126000) ctp + (0136000) utp + (0027600) murein5p5p[p] + (0025400) dctp + (0176000) phe-L + (0241000) thr-L + (0054000) trp-L + (0326000) lys-L + (0146000) met-L + (0428000) leu-L + (0090000) his-L + (0276000) ile-L + (0281000) arg-L + (0087000) cys-L + (0582000) gly + (0402000) val-L + (0007000) spmd + (0000010) fad + (0025400) dgtp + (0024700) dttp + (0024700) datp + (45731800) atp + (0001000) amp + (0002150) nad + (0000050) nadh + (0000130) nadp + (0000400) nadph + (0000006) coa + (0000050) accoa + (0000003) succoa + (0730200) pi + (0730200) ppi
To make this a ‘symbiotic’ model the biomass reaction modified to include the exported EAA component.
An estimate of EAA export was obtained empirically using the pea aphid-Buchnera symbiosis reared on chemically defined diets. It varied among the amino acids, from 22% (histidine and tryptophan) to 50% (threonine) of the amount synthesised.
Model required significant tweaking to get it to function – had to build iteratively.
The Buchnera metabolic network is highly constrained
The primary constraint that is used to assess the output of the network are the uptake fluxes - 5 main precursors used by the network.
Optimal growth flux (5.21) is unusually only reached by essentially a single ‘solution’, i.e. a single distribution of internal fluxes, as judged by flux variability analysis.
The Buchnera metabolic network is fragile
Designed a qualitative experiment to demonstrate the principle that the aphid could manipulate the metabolic output of the bacterium just by changing the inputs.
If this network is ‘always on’ then this is a plausible way to control the symbiosis.
Possibly at the level of transporter activity in the bacteriocyte membrane.