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 Construction and analysis of the Buchnera metabolic model

Many insects have obligate bacterial symbionts Aphid symbionts Buchnera sp. APS – Acyrthosiphon pisum Buchnera sp. SG - Schizaphis graminum Buchnera sp. BP - Baizongia pistacea Ant symbionts Blochmannia floridanus Blochmannia pennsylvanicus These are primary symbionts There are also secondary symbionts which are not essential. Named after famous entomologists who first described these symbioses. Paul Buchner, Vincent Wigglesworth and Friedrich Blochmann. Tsetse fly symbionts Wigglesworthia glossinidia brevipalpis

Aphids – phloem sap feeding pests The pea aphid, Acyrthosiphon pisum. For experimental work we use a strain with no secondary symbionts. Primary symbiont is obligate provide ecological benefits, such as heat tolerance and resistance to parasitoids About 5000 different species Major crop pests. Restricted diet of phloem sap. All contain a primary symbiont and often a secondary symbiont.

Buchnera aphidicola sp. APS is the primary symbiont of the pea aphid The bacteria are located in specialised insect cells called bacteriocytes in their body cavity. They are surrounded by an aphid-derived bacteriocycte membrane. TEM of bacteriocyte cytoplasm, showing coccoid Buchnera. The Buchnera are unculturable Vertically transmitted to aphid offspring via the ovary. The function of the symbiosis is nutritional. Phloem sap poor in essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine). These are known to be provided by the symbiont.

The Buchnera APS genome Small - 0.64 Mb 607 genes (569 protein coding genes) AT rich - 73 % AT Almost 90% of the genes have known functions in E. coli K-12. Virtually no transcription regulation. No IS or phage elements Genome reannotated in BuchneraBASE. 2 new genes Many gene length changes Pseudogenes added to trace recent evolutionary history

The genome sequences of primary insect symbionts reveal clear descent from an ancestral -proteobacterium Ancestor ~3.5 Mb 200 My ago… Buchnera sp. APS 0.6 Mb Intracellular Gene loss only E. coli K-12 4.2 Mb Free-living Gene loss and gene gain No MreB so not shape.

The Buchnera genome has undergone reductive evolution by a series of deletion and inactivations of existing genes Initial large deletions removed large regions of the genome. Followed by smaller deletions and gene inactivation. Genomes are still relatively co-linear (syntenic).

The genome supports the proposed symbiotic function

Understanding the symbiosis 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. Central metabolism Built from biochemical/genetics literature for E. coli High quality reconstruction Contains 931 unique biochemical reactions Mass and charge balanced e.g. [c]h2o + sl26da --> 26dap-LL + succ [c]26dap-LL <==> 26dap-M [c]26dap-M + h --> co2 + lys-L

Construction of the metabolic network Method Map all orthologues between APS and K-12. Manually assign reaction codes for APS gene products. Store all relationships in BuchneraBASE. www.buchnera.org

Construction of the metabolic network (2) 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. 3 phosphoserine aminotranferase

Construction of the metabolic network (3) Missing reactions 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.

Carbon-skeleton based manual visualisation of iGT196 196 gene products 240 compounds (39% of iJR904) 263 reactions (27% of iJR904) 35% of reactions for EAA biosynthesis. Can see that many linear pathways, virtually no redundancy, nearly every pathway ends in a biomasss component. Asp derived – Thr, Lys and Ile Pyruvate derived – Val Leu (and Ile) PEP and erythrose 4 P – Phe and Trp PRPP – His Makes isoprenoides/terpenoids for hemeO and also for cell wall. Needs to take up protohaem IX. Key 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.

Understanding the symbiosis Analysis of the reconstruction using constraint-based modelling (flux balance analysis)

Flux balance analysis Exchange Intracellular Biomass 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. This is a steady state method Main constrainst are uptake fluxes Important to determine the biomass component.

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. A number of ‘dead end’ metabolites are produced in significant quantities in the 5.21 model by Buchnera Adenine by product of spermidine biosynthesis 5-methylribose by product of spermidine biosynthesis S-ribosylhomocysteine by product of cysteine biosynthesis Succinate by product of lysine biosynthesis

The Buchnera metabolic network is fragile Dogma is that biological networks have evolved to be robust, i.e. tolerant of some disruption. Deleted individual genes in silico and measured resulting growth flux. E. coli iJR904 serves as a control. Deletion of 84% of the genes in iGT196 led to a >99% decrease in growth flux (19% for iJR904). Similar results using linear minimisation of metabolic adjustment (linearMOMA). Things that could be removed were Can see the different here that liner MOMA made – we like it – it makes sense.

Can the aphid control the metabolic function of its symbiont? 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.