Modelling metabolite flux
between the pea aphid and
its bacterial endosymbiont
Buchnera
Sandy Macdonald, Gavin Thomas, Angela Douglas
Overview
• Background
• Buchnera network
• Improved Buchnera network
• Buchnera/bacteriocyte network
• Discussion points
Insect-bacterial symbioses
• Many insects of the orders Hemiptera,
Hymenoptera and Coleoptera form
obligate symbioses with bacteria
• Blochamannia / carpenter ants
• Wigglesworthia / tsetse flies
• Buchnera / aphids
Insect-bacterial symbioses
•
•
•
•
•
Hallmark of insect endosymbionts is
extreme genome reduction and high AT
Blochmannia – 0.70 Mb, 72.6% AT
Wigglesworthia – 0.69 Mb, 77.5% AT
Buchnera – 0.64 Mb, 79.9% AT
Carsonella – 0.16 Mb, 83.4% AT
E. coli K12, 4.64 Mb
Buchnera aphidicola, 0.62 Mb
Wigglesworthia glossinidia, 0.70 Mb
Carsonella ruddii, 0.16 Mb
Hodgkinia cicadicola, 0.14 Mb
Aphid/Buchnera symbiosis
•
•
•
Obligate symbiosis
Metabolic interdependence
Aphid provides nutrients for Buchnera,
Buchnera provides essential AAs for aphid
nutrients
essential AAs
Buchnera
Aphid
Buchnera metabolic model
• iGT196
fragile metabolic network adapted for
• “A
cooperation in the symbiotic bacterium
Buchnera aphidicola”. Thomas et al, 2009.
BMC Systems Biology 3:24.
• 196 genes, 263 reactions, 240 metabolites
gaps in pathways, isolated
• Several
reactions
Buchnera metabolic network
Buchnera metabolic model provides
a foundation for investigating
genomic and metabolic
complementarity of symbiosis
Control of symbiosis
• Does the Buchnera have any control over the
symbiosis in terms of EAA output?
• Experimental data suggest that different aphid
clones respond differently to exclusion of
individual EAAs from their diets
• Is this due to genetic differences in the
Buchnera in these aphid clones?
• Can different profiles of substrates provided by
the aphid result in the differing EAA profiles?
Aphid metabolic model
• "Genome sequence of the pea aphid
Acyrthosiphon pisum." International
Aphid Genomics Consortium, 2010.
PLoS Biology 8:e100303.
• 464 Mb, 34,604 genes, 29.6% GC
• 2,010 (5.8%) genes manually annotated
Princeton, 2008
Barcelona, 2009
Aphid metabolic model
• Buchnera housed in specialised cells
called bacteriocytes
• Gaps in pathways encoded on aphid
genome, and enriched in bacteriocyte
transcriptome
• Also transcriptomes of 3 other aphid
tissues: fat body, gut, embryo.
Aphid metabolic model
• Began with missing reactions encoded
on aphid genome
• iSM197. "Genetic and metabolic
determinants of nutritional phenotype in
an insect-bacterial symbiosis."
Macdonad et al, 2011. Molecular
Ecology, in press.
Buchnera metabolic network
Aphid metabolic model
• Next stage was to use transcript data for
4 aphid tissues to build a multicompartmental model with genes
enriched in each tissue.
• For bacteriocyte: 454 data, 2x Illumina
data and proteome data.
• Currently being built. Bacteriocyte
compartment already constructed.
Hansen and Moran Illumina
Cornell Illumina
BAC
BODY
FOLD
BAC
BODY
FOLD
Proteome NSAF
2.4.2.3
709.19
49.97
14.19
230.69
24.22
9.52
5.23E-04
phosphoserine phosphatase
3.1.3.3
218.57
22.71
9.62
354.74
46.03
7.71
1.15E-03
ACYPI001461
glutamine synthetase
6.3.1.2
2.14
8594.73
963.82
8.92
3724.62
1203.82
3.09
3.72E-03
ACYPI000593
cystathionine gamma-lyase
4.4.1.1
4.51
363.50
50.55
7.19
595.31
79.22
7.51
2.15E-03
ACYPI005215
asparaginase
3.5.1.1
509.65
80.67
6.32
1246.22
77.85
16.01
1.26E-02
ACYPI003109
adenine phosphoribosyltransferase
2.4.2.7
196.11
36.04
5.44
706.11
29.06
24.29
1.38E-03
ACYPI004747
dihydropyrimidine dehydrogenase (NADP+)
1.3.1.2
338.00
65.64
5.15
220.31
22.60
9.75
5.33E-03
ACYPI002694
fructose-bisphosphatase
3.1.3.11
2.16
870.86
184.83
4.71
256.12
87.89
2.91
1.34E-03
ACYPI003436
adenine phosphoribosyltransferase
2.4.2.7
5.62
575.27
124.29
4.63
1516.33
214.01
7.09
2.03E-03
ACYPI007803
tryptophan 5-monooxygenase
1.14.16.4
11.80
793.43
183.91
4.31
869.32
62.33
13.95
7.24E-03
ACYPI001738
3-hydroxyacyl-CoA dehydrogenase
1.1.1.35
4.49
249.21
59.17
4.21
191.43
62.24
3.08
1.14E-03
ACYPI009480
ornithine aminotransferase
2.6.1.13
8.63
1217.01
331.68
3.67
5603.42
531.70
10.54
1.58E-02
ACYPI001203
aminopeptidase N
3.4.11.2
20.66
88.93
25.32
3.51
248.19
9.56
25.97
1.25E-03
ACYPI002795
aminomethyltransferase (GCS)
2.1.2.10
201.05
63.03
3.19
520.46
46.33
11.23
6.94E-04
ACYPI002452
5'-nucleotidase
3.1.3.5
101.64
33.20
3.06
231.64
22.87
10.13
6.30E-04
ACYPI006909
6,7-dihydropteridine reductase
1.5.1.34
7.51
358.56
122.02
2.94
593.19
101.36
5.85
2.21E-03
ACYPI005060
glycine dehydrogenase (GCS)
1.4.4.2
12.62
223.86
77.75
2.88
646.66
54.62
11.84
2.92E-03
ACYPI010114
PRASC synthase
6.3.2.6
3.08
264.75
94.40
2.80
977.97
169.26
5.78
3.96E-03
ACYPI004666
phosphoserine transaminase
2.6.1.52
279.99
108.50
2.58
722.28
118.43
6.10
2.41E-03
ACYPI006288
ribose-phosphate diphosphokinase
2.7.6.1
8.89
483.43
194.47
2.49
853.28
138.10
6.18
1.89E-03
ACYPI008372
branched-chain-amino-acid transaminase
2.6.1.42
6.86
216.87
96.71
2.24
535.92
113.36
4.73
1.87E-03
ACYPI001614
ribose-phosphate diphosphokinase
2.7.6.1
80.55
40.30
2.00
158.91
37.18
4.27
1.14E-03
Gene
Enzyme
EC
ACYPI007710
uridine phosphorylase
ACYPI000304
454
12.75
AKG
12.8
TCA
CYCLE
16.8
94.6
ac CoA
38.2
8.7
ACYPI000901
gln
6.5
2-ob
6.5
glu 3mop
8.7
94.6
ACYPI004666,
ACYPI000304
ser
12
13
.1
ade
glc
AMP
ACYPI008222
purine
synthesis
6.7
pantothenate
synthesis
phe
synthesis
3mop
91.7
ACYPI003436, ACYPI003109,
ACYPI001890
0.7
2-ob
glc
100
Buchnera
arg
synthesis
ACYPI000593, ACYPI008755,
ACYPI000919, ACYPI000433
cyst
3
4.6
2.9
gsn
GMP
IMP
g6p
ACYPI002452 ACYPI004360, ACYPI010114,
ACYPI006177 ACYPI001614,
ACYPI006288
35.2
asp
ACYPI008372
ile
ala-B
phpyr
3mob
tyr
ala-B
phe
dhbpt thbpt AKG
phpyr
glu
pterin synthesis
ACYPI005172, ACYPI007090,
ACYPI003467
ura
uri
33.7
gsh
ACYPI004747, ACYPI007710
ACYPI002925,
ACYPI005060, ACYPI003488
ACYPI003581,
ACYPI009177,
ACYPI002795,
cys
glu
ACYPI009705
ACYPI000044
ACYPI006909
bacteriocyte
7x10-4
as
n
uri
45.0
-4
glu 4mop,
ACYPI005215
10
ACYPI007803
ACYPI008372
8
asn
7x
leu,
val
41.
3.2
6.4
AKG
6.5
4.6
AKG
1.8
cyst
0
0.
ACYPI008106
7
orn
pro
8.2
ptrc
GLUCONEOACYPI006664, GENESIS
3-pg
7.4
pro
AKG
.9
glu
.3
fum
20
glu
NH
12
succ
ACYPI001461
x2
ACYPI002180,
ACYPI009480
ac
.3
ACYPI000303
AKG
4.0
AKG
NH3
27.1
gly
cgly
gsh
ACYPI001203 ACYPI003904
mlthf,
nadh,
CO2
thf,
nad
How we build our models
• Buchnera model built by stripping down E.
coli model, because its genome is a subset
of E. coli
• SBML
• Biomass reaction based on E. coli, but
incorporating EAAs for aphid and cofactor
constraints
• Pathway by pathway, with crutches
How we build our models
• Analysed using COBRA toolbox and
Matlab
• For iSM197 and the aphid model we
built the networks in CellDesigner,
parsed the SBML using Perl, and
analysed them using COBRA
• Now also using SurreyFBA software.
Good because it doesn't rely on Matlab
Models only have to be as
complicated as you need them to be.
Depends how hard it is to get
interesting data from your system.
How well characterised is your
system?
Discussion points
1. Buchnera. Perfect for FBA.
• Well determined functions of > 90% of
genes, experimentally in E. coli
• Little or no transcriptional regulation
• Relatively small network. 263 reactions
• Factory. What goes in comes out.
• Transporters?
2. SBML
• Differences in metabolite, reaction
names. Palsson IDs, MetaCyc IDs,
proprietary IDs.
• Specification of external metabolites.
• Parsing of SBML by software
• Is SBML specification strict enough?
• Does everyone conform with MIRIAM?
3. Redox balancing
• iGT196 model of Buchnera had a large
(artifactual) overflow of arginine to
redox balance NADP/NADPH
• We got rid of this by adding a NADP/
NADPH exchange reaction
• Is a fudge better than an artifact?
Buchnera metabolic network
3. Redox balancing
• Wigglesworthia metabolic model
• Had similar redox balance problem
• Model used glutamate as major carbon
source, but couldn't balance NADP/H
• Proline to glutamate via 1-pyrroline-5carboxylate regenerates NADPH
• Proline is major energy source for the tsetse
Summary
• Symbiosis much more complicated that first
thought. Not just EAAs.
• Transcriptome enrichment informs model
building.
• Important to have strict standards for
models, and software.
• Redox balancing is important and can
reveal interesting biology.
Thanks.
York
Gavin Thomas
Peter Ashton
Ruth Hunter
Cornell
Angela Douglas
George Lin
Calum Russell
Lyon
Hubert Charle
Stefano Cole
Augusto Vello