Logic modelling of metabolism: from individual networks to communities Workshop Modélisation du métabolisme - INRAE - Hal-Inria
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Logic modelling of metabolism:
from individual networks to
communities
Clémence Frioux
Workshop Modélisation du métabolisme - INRAE
November 19, 2021
clemence.frioux@inria.fr Inria Bordeaux Sud-Ouest – CNRS – INRAe – Univ. Bordeaux c_frioux
Picture from Orth et al, Nat. Biot. 2010
From network composition to constraint-based modelling
> protein_1 Acetaldehyde exchange Environmental conditions
ATGGAATCGCATAGCA… Acetaldehyde Flux analysis (FBA, FVA…)
> protein_2
Constraint-based modelling
Dihydroxyacetone phosphate
AATGCCTGATCGAATA… Sedoheptulose 7-phosphate
Triose-phosphate isomerase
Acetaldehyde reversible transport D-Xylulose 5-phosphate
D-Glucose exchange Ribulose 5-phosphate
Acetate exchange 3-epimerase
Fructose-bisphosphate aldolase
Transketolase
Functional annotation
Transaldolase Transketolase
Ribose-5-phosphate isomerase
O2 exchange
D-Glucose
Acetaldehyde
Glyceraldehyde 3-phosphate D-Ribulose 5-phosphate
Acetate
*! … *"
Glucose-6-phosphate isomerase CO2 exchange
O2 O2 Alpha-D-Ribose 5-phosphate CO2 CO2
D-Erythrose 4-phosphate
CO2 transporter via diffusion
D-Fructose 6-phosphateD-Fructose 1,6-bisphosphate
⋯ 0 ,!
D-Fructose D-glucose transport via PEP:Pyr
+1
D-Fructose exchange PTS
D-Glucose 6-phosphate
Fructose transport via PEP:Pyr 2-Oxogluterate dehydrogenase
PTS (f6p generating) 6-phospho-D-glucono-1,5-lactone
Acetaldehyde dehydrogenase
3-Phospho-D-glyceroyl Pyruvate dehydrogenase
NAD transhydrogenase L-Malate exchange
protein name EC-number GO KEGG …
phosphate (acetylating) Alcohol dehydrogenase
Glucose(ethanol)
6-phosphate
!= …
Fructose-bisphosphatase
⋮ ⋱ ⋮
Glyceraldehyde-3-phosphate dehydrogenase Phosphogluconate dehydrogenase
O2 transport diffusion Nicotinamide adenine Ethanol
dehydrogenase
dinucleotide CO2 CO2
3-Phospho-D-glycerate
Acetate Pyruvate Nicotinamide adenineMalic enzyme (NAD) L-Malate
Metabolic objective
Acetate reversible transport via
Phosphofructokinase
protein_1 pfkA 2.7.1.11 - K00850 …
⋯ +1 ,"
proton symport dinucleotide - reduced Nicotinamide adenine
Succinyl-CoA
Biomass Objective Function with
−1
Phosphoenolpyruvate dinucleotide
Malate phosphate
dehydrogenase
Phosphotransacetylase GAM Malic enzyme (NADP)
Acetyl phosphate Phosphoenolpyruvate carboxylase
Phosphoenolpyruvate Isocitrate dehydrogenase (NADP)
Acetyl-CoA L-Malate H+
Phosphoglycerate kinase Coenzyme Acarboxykinase Oxaloacetate
6-Phospho-D-gluconate
protein_2 eno 4.2.1.11 - K01689 …
Malate2-Oxoglutarate
synthase Nicotinamide adenine
NAD(P) transhydrogenase
Pyruvate kinase
D-lactate dehydrogenase dinucleotide phosphate -
Acetate kinase Malate transport reduced
6-phosphogluconolactonase
via proton
ATP C10H12N5O13P3 Phosphate symport (2 H)
Phosphoglycerate mutase O2 O2 Succinyl-CoA synthetase
… … … … … …
D-lactate exchange Pyruvate formate lyase
ADP C10H12N5O10P2Citrate synthase
(ADP-forming) Ethanol exchange
D-Lactate D-Lactate Ethanol reversible transport viaGlyoxylate Isocitrate
Pyruvate transport in via proton Glutamate synthase
proton(NADPH)
symport Ethanol
symport NADH dehydrogenase
Phosphoenolpyruvate synthase H2O H2O
(ubiquinone-8 & 3 protons)
L-Glutamine 2 oxoglutarate
D lactate transport via ATP
proton
synthase (four protons for Glutamate dehydrogenase (NADP)reversible
symport Enolase one ATP) transport via symport
Pyruvate H+
ATP maintenance requirement L-Glutamate Aconitase (half-reaction B, 2-Oxoglutarate
Fumarase
Formate Adenylate kinase Glutamine synthetase Isocitrate hydro-lyase)
L-glutamine transport via ABC
Pyruvate exchange system Citrate Isocitrate lyase
D-Glycerate 2-phosphate Cytochrome oxidase bd 2-Oxoglutarate exchange
Formate transport in via proton (ubiquinol-8: 2 protons) Aconitase (half-reaction A,
AMP C10H12N5O7P symport Succinate transport via proton Citrate hydro-lyase)
Phosphate
L glutamate transportreversible
via protontransport symport (2 H) Succinate
Glutaminase
via symport
symport reversible Cis-Aconitate
Succinate transport out via
proton antiport H2O transport via diffusion
Ubiquinol-8
Formate transport via diffusion Fumarate transport via proton
Fumarate
symport (2 H) Ubiquinone-8
H+ exchange
Formate Ammonium Succinate dehydrogenase
(irreversible)
L-Glutamine
Fumarate reductase
L-Glutamate Succinate H2O H2O
Formate exchange
Phosphate
H2O exchange
Fumarate
Metabolic network
L-Glutamate exchange
L-Glutamine exchange
Succinate exchange
Ammonia reversible transport
Function
reconstruction
Phosphate exchange
Fumarate exchange
Carbohydrate degradation
Lipid synthesis
& curation
Ammonium
Vitamin biosynthesis
Antibiotic resistance Ammonia exchange
… …
Metabolic constraint-based modelling necessitates
1 curated metabolic networks
A rise in non-optimal use-cases
Non-model organisms
• Limited experimentations
• Unknown biomass composition
Cheaper sequencing
Metagenomics
Large-scale community modelling
• Impossible manual curation
• Uncertainty on/missing functions
• Scalability of optimisations-based simulation
Needed alternatives for metabolic screening,
complexity reduction, hypothesis generation
2
Boolean abstraction of metabolic producibility
RinA RinB
Network expansion [Ebenhöh et al 2004]
A B F
“and” condition à Scope of seeds
checked recursively à Discrete modelling
R2 R1 R3
C D G
!"#$% &, ( = * +! ,
R4 R5 !
where +" = ( and
E H +!#$ = +! ∪ $4#56"7! 4 ∈ 9 4%:"7:;7! 4 ⊆ +!
Qualitative simulation of metabolic producibility ignoring stoichiometric
coefficients and w/o objective function
3 [Kruse et al 2008] [Handorf et al 2005]
A flexible implementation system [Gebser et al 2012]
• Answer Set Programming Problem
• Logic paradigm Modeling Solution
• Knowledge representation & reasoning Logic
program Interpreting
• Optimisation Stable
Grounder Solver
models
4
A flexible implementation system [Gebser et al 2012]
• Answer Set Programming Problem
• Logic paradigm Modeling Solution
• Knowledge representation & reasoning Logic
program Interpreting
• Optimisation Stable
Grounder Solver
models
reaction(“r1”).
reactant(“A”, “r1”).
product(“B”, “r1”). A r1 B
% r2 D
reaction(“r2”).
reactant(“B”, “r1”). C
reactant(“C”, “r1”).
product(“D”, “r1”).
%
seed(“A”).
seed(“C”).
%
scope(M) :- seed(M).
5 scope(M) :- product(M,R); reaction(R); scope(N) : reactant(N,R).
A flexible implementation system [Gebser et al 2012]
• Answer Set Programming Problem
• Logic paradigm Modeling Solution
• Knowledge representation & reasoning Logic
program Interpreting
• Optimisation Stable
Grounder Solver
models
reaction(“r1”).
reactant(“A”, “r1”).
product(“B”, “r1”). A r1 B
% r2 D
reaction(“r2”).
reactant(“B”, “r1”). C • Existing variation for pseudo-
reactant(“C”, “r1”). state modelling [Thuillier et al 2021]
product(“D”, “r1”).
% • Building block for many
seed(“A”). applications
seed(“C”).
%
scope(M) :- seed(M).
5 scope(M) :- product(M,R); reaction(R); scope(N) : reactant(N,R).
Prigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation
6
Prigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation Activated reactions
6
Prigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation Activated reactions Producibility checking
6Prigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation Activated reactions Producibility checking
6
Pathway detectionPrigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation Activated reactions Producibility checking
±
hybrid
(FBA)
6
Pathway detection Gap-fillingPrigent et al 2017, Aite et al 2017,
Applications of the Boolean abstraction of producibility Frioux et al 2018, Frioux et al 2019
Scope calculation Activated reactions Producibility checking
±
hybrid
(FBA)
6
Pathway detection Gap-filling Community analysisMetagenomics meets metabolism
Sampling
Metagenome Strategy
Amplicon Shotgun Sequencing
sequencing metagenomics Strategy
[Frioux et al. CSBJ 2020]
s
on
cti
Mapping to Assembly
fun
Diversity Reference
ific
Genome/Functional
Generation
ec
Mapping to ref. DB
sp
in-
SSU/LSU DB gene catalogs MAGs
tra
s/s
cie
pe
Functions Functions
fs
so
Pathways from from single Mapping De novo
Interactions Functional
los
matched ref. to ref.
le
genes Pathway GSMN Inference
sib
genomes* GSMNs*
os
modelling
*P
Gene-centric Genome-centric
“bag-of-genes” “bag-of-genomes”
Identification of microbial interactions from Combine genome reconstruction and metabolic modelling
metagenomic data is key to better understand to unravel the functional repertoire and community
complex ecosystems organisation of uncultivated species
7Metabolic networks and communities
Shotgun metagenomic sequencing
Metagenome-Assembled Genomes
(MAGs)
8Metabolic networks and communities
Shotgun metagenomic sequencing
Metagenome-Assembled Genomes
(MAGs)
A C F
environment
One sample à One set of MAGs
What can we learn about the community?
8Metabolic networks and communities
Shotgun metagenomic sequencing
A R1 B
R2 D D
R3 E R4 G
C
F
Metagenome-Assembled Genomes
(MAGs)
A C F
environment
1. Reconstruct GSMNs for
One sample à One set of MAGs
all genomes
à One set of metabolic networks
What can we learn about the community?
8Metabolic networks and communities
?
Shotgun metagenomic sequencing
A R1 B
R2 D D
R3 E R4 G
C
F
Metagenome-Assembled Genomes
(MAGs)
A C F
environment
1. Reconstruct GSMNs for
One sample à One set of MAGs
all genomes
à One set of metabolic networks 2. Assess putative
complementarity
between them
What can we learn about the community?
8Metabolic networks and communities
?
Shotgun metagenomic sequencing
A R1 B
R2 D D
R3 E R4 G
C
F
Metagenome-Assembled Genomes
(MAGs)
A C F
environment
1. Reconstruct GSMNs for
One sample à One set of MAGs
all genomes
à One set of metabolic networks 2. Assess putative
complementarity
between them
3. Identify key species
What can we learn about the community? associated to a function
8[Belcour*, Frioux* et al. eLife 2020]
Metage2Metabo [Karp et al. Bioinformatics 2002]
[Frioux et al. Bioinformatics 2018]
A pipeline for metabolic screening of communities
Automatic GSMN reconstruction
Microbiota
- MAGs
- Reference genomes
9[Belcour*, Frioux* et al. eLife 2020]
Metage2Metabo [Karp et al. Bioinformatics 2002]
[Frioux et al. Bioinformatics 2018]
A pipeline for metabolic screening of communities
Automatic GSMN reconstruction Individual metabolic capabilities
Microbiota
- MAGs
- Reference genomes
9[Belcour*, Frioux* et al. eLife 2020]
Metage2Metabo [Karp et al. Bioinformatics 2002]
[Frioux et al. Bioinformatics 2018]
A pipeline for metabolic screening of communities
Automatic GSMN reconstruction Individual metabolic capabilities
Microbiota
- MAGs
- Reference genomes
Collective metabolic capabilities
9[Belcour*, Frioux* et al. eLife 2020]
Metage2Metabo [Karp et al. Bioinformatics 2002]
[Frioux et al. Bioinformatics 2018]
A pipeline for metabolic screening of communities
Automatic GSMN reconstruction Individual metabolic capabilities
Microbiota
- MAGs
- Reference genomes
Added value of cross feeding Collective metabolic capabilities
9[Belcour*, Frioux* et al. eLife 2020]
Metage2Metabo [Karp et al. Bioinformatics 2002]
[Frioux et al. Bioinformatics 2018]
A pipeline for metabolic screening of communities
Automatic GSMN reconstruction Individual metabolic capabilities
Microbiota
- MAGs
- Reference genomes
Added value of cross feeding Collective metabolic capabilities
Minimal community selection
Systematic screening of metabolic
potential and mutualistic potential
in a microbiota
9[Belcour*, Frioux et al. eLife 2020]
Metage2Metabo [Frioux et al. Bioinformatics 2018]
Key species
l Minimal community selection:
combinatorial problem solving
+ nutrients
The concept of key species addresses the combinatorial challenge brought by
functional redundancy in microbiomes
10[Belcour*, Frioux et al. eLife 2020]
Metage2Metabo [Frioux et al. Bioinformatics 2018]
Key species
l Minimal community selection:
combinatorial problem solving
+ nutrients
l However… 1 solution (= 1 minimal community) among
millions? Huge combinatorics
The concept of key species addresses the combinatorial challenge brought by
functional redundancy in microbiomes
10[Belcour*, Frioux et al. eLife 2020]
Metage2Metabo [Frioux et al. Bioinformatics 2018]
Key species
l Minimal community selection:
combinatorial problem solving
+ nutrients
l However… 1 solution (= 1 minimal community) among
millions? Huge combinatorics
l Key species = species found in at least one of the
predicted minimal communities (MC)
> Essential symbionts : species predicted in every MC
> Alternative symbionts : species predicted is some but
not all MC
The concept of key species addresses the combinatorial challenge brought by
functional redundancy in microbiomes
10Metage2Metabo
Application to 1,520 reference genomes of
cultivable species of the human gut microbiota
156 metabolites that cannot be
produced by individual species
Classification into categories: lipids (28),
carbohydrate derivatives (58)…
Key species computation for
categories of metabolites
+ minimal communities enumeration
Analysis and visualisation
11Metage2Metabo
Key species by groups of metabolic end-products
Enumeration of all minimal communities
How do bacteria associate within them?
Any patterns?
12Metage2Metabo
Key species by groups of metabolic end-products
a. b.
Actinobacteria
Lipids
Bacteroidetes
1 Am
de
Firmicutes
"OR"
Fusobacteria
"AND" Proteobacteria
2
"OR"
3 "AND"
4 "AND"
d.
"OR" Sugar derivatives
"AND"
"AND"
5
58,520 minimal communities associated to the
producibility of the 28 lipids can be visualised as a
compressed association graph.
à Species with equivalent roles in the communities
12Metage2Metabo
Same “reading pattern” for all groups
Essential symbionts (ES) shared between several groups of targets
à species with unique functions among the 1,520?
13 4/5 ES for carbohydrates are studied as probioticsTake home messages
Boolean abstraction of metabolic producibility
> Modelling framework complementary to constraint-based modelling
An integrative workflow - Metage2Metabo
> Global analysis of metabolism: redundancy - complementarity
> Key species screening: complexity reduction aureme/metage2metabo
cfrioux/MeneTools
> Suitability to MAGs incompleteness bioasp/meneco
Use cases for microbiome functional analyses:
> Comparison of metabolic repertoire in genome collections
> Comparison of cohort samples through their MAGs composition
14- Simon Dittami
- Arnaud Belcour - Falk Hildebrand - Pleiade project team
- Méziane Aite - NBI Computing https://inria.fr/en/pleiade
- Anne Siegel Infrastructure for Science - Anthony Bretaudeau
We’re hiring!
Engineer/postdoc position - 2 years - at Inria Bordeaux
systems biology – metabolism
gut microbiota dynamics – logic modelling
https://tinyurl.com/postdoc-sysbio-inriaYou can also read
