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Nitrogen assimilation in plantassociated bacteria
Gail M. Preston
Department of Plant Sciences
University of Oxford
Pseudomonas common ancestor
Pseudomonas syringae
Pseudomonas fluorescens
Organic N
Organic/inorganic N
High O2
Med-low O2
Intimate association
with plant cells
Variable association
with diverse hosts
Low competition
High competition
M. Romantschuk
Endophyte / Leaf surface
Plant Pathogen
S. Molin
Leaf surface / Roots
Plant Growth-Promoting
Genome sequenced strains
P. aeruginosa PA01
P. aeruginosa PA14
P. entomophila L48
P. putida KT2440
P. syringae pv. tomato DC3000
P. syringae pv. syringae B728a
P. savastanoi pv. phaseolicola 1448a
P. fluorescens Pf-5
P. fluorescens Pf0-1
P. fluorescens SBW25
Why study nitrogen metabolism ?
• Nitrogen is essential for life – frequently a limiting factor in
natural environments
• Well characterised metabolic pathways (core metabolites and
secondary metabolites)
• Environmental variability in nitrogen source and availability
• Environmental factors (pH, oxygen etc.) can affect nitrogen
acquisition
• Environmental impact – nitrogen fertilisers on natural
ecosystems
• Variation in nitrogen metabolism across Pseudomonas
Why study
Pseudomonas?
Ps1
Ps2
P. syringae
Leaves of
specific
plant
species
Ps3
Pf1
Pf2
P. fluorescens
Leaf
surface
and soil
Pf3
Pa1
Pp1
P. putida
Pe1
P. entomophila
P. aeruginosa
Soil
Soil and
animals
Niches vary in nutrient availability
environmental conditions – pH, oxygen
host interactions (humans, plants and simple animal models)
Most strains can grow on very minimal media – salt, glucose, NH4 or nitrate
In silico predictions: Using the Pfam database to identify
over and under-represented domains in P. syringae
Amino acid transport
P. aeruginosa
P. putida
P. fluorescens
P. syringae pv tomato
21 AA_permease
21 AA_permease
18 AA_permease
4 AA_permease
P. syringae pv. syringae
5 AA_permease
P. syringae pv. phaseolicola
5 AA_permease
E. coli 24, Yersina pestis 19,
Xanthomonas campestris 11
Xylella fastidiosa 3
d-serine/d-alanine/dglycine; arginine
/ornithine/ putrescine;
cadaverine; lysine;
histidine; threonine;
choline; glutamate;
cysteine
Proline
GABA
Ethanolamine
Aromatic amino acids
X P. syringae pv. tomato
P. syringae pv. tomato
P. fluorescens SBW25
rpoN (sigma-54)
PSPTO4453
Pflu0882
ntrB (NRII)
PSPTO0353
Pflu0344
ntrC (NRI)
PSPTO0352
Pflu0343
glnK (PII)
amt-1 (ammonium transporter)
PSPTO0217
PSPTO0218
Pflu5953
Pflu5952
gltB, gltD (glutamate synthase)(GOGAT)
PSPTO5123/21
Pflu0414/5
glnA (glutamine synthase – type I)
PSPTO0359
Pflu0348
glnD (PII uridylyltransferase)
PSPTO1532
Pflu1268
nac (nitrogen assimilation regulatory
protein)
PSPTO2923
Pflu4026
gdhA (glutamate dehydrogenase)
No orthologous hit
Pflu5326
nirB, nirD (nitrite reductase)
PSPTO2302 - truncated nirB
PSPTO3262/3
Pflu3425/4
Nitrate reductase
Bifunctional nitrate reductase/sulfite
reductase
PSPTO2301
Pflu3426
Nitrate transporter
PSPTO2304
Pflu4609
AA_permease domain proteins
PSPTO5356, 1817, 2026
PSPTO5276
Pflu1674, 5187
Pflu5197, 1103, 0315, 2013, 5442
Pflu0368, 4870, 2264, 3375, 4890,
Pflu4889, 3091, 3323, 3287, 3148,
Pflu3094
Glutamine amidotransferase (class II)
Glutamate synthase
Ammonium transporter (amt-2)
PSPTO2583
PSPTO2585
PSPTO2586
Pflu2324
Pflu2326
Pflu2327
Glutamine synthase (type II)
PSPTO1921, 5309, 5310
Pflu1514, 2163, 3065, 5847, 5849
Ammonium transporter (amt-3)
No orthologous hit
Pflu1747
Glutamine synthase (type III)
No orthologous hit
Pflu2323
Gene//Domain/Putative Function
Predicting RpoN binding sites
P. syringae pv. tomato
P. fluorescens SBW25
Gene//Domain/Putative Function
RpoN (σ54)
regulation of
nitrogen
metabolism…
rpoN (sigma-54)
PSPTO4453
●
Pflu0882
●
ntrB (NRII)
PSPTO0353
-
Pflu0344
-
ntrC (NRI)
PSPTO0352
-
Pflu0343
-
glnK (PII)
amt-1 (ammonium transporter)
PSPTO0217
PSPTO0218
●
●
Pflu5953
Pflu5952
●
●
gltB, gltD (glutamate synthase)(GOGAT)
PSPTO5123/21
-
Pflu0414/5
-
glnA (glutamine synthase – type I)
PSPTO0359
●
Pflu0348
●
glnD (PII uridylyltransferase)
PSPTO1532
-
Pflu1268
-
nac (nitrogen assimilation regulatory
protein)
PSPTO2923
-
Pflu4026
●
gdhA (glutamate dehydrogenase)
No orthologous hit
Pflu5326
○
nirB, nirD (nitrite reductase)
PSPTO2302 - truncated
nirB
PSPTO3262/3
●
●
Pflu3425/4
●
○= intragenic σ54
binding motif,
Nitrate reductase
Bifunctional nitrate reductase/sulfite
reductase
PSPTO2301
●
Pflu3426
●
- = no σ54 binding
motif
Nitrate transporter
PSPTO2304
●
Pflu4609
●
AA_permease domain proteins
PSPTO5356, 1817, 2026
PSPTO5276
●
-
Pflu1674, 5187
Pflu5197, 1103, 0315, 2013,
5442
Pflu0368, 4870, 2264, 3375,
4890, Pflu4889, 3091, 3323,
3287, 3148, Pflu3094
●
○
-
Glutamine amidotransferase (class II)
Glutamate synthase
Ammonium transporter (amt-2)
PSPTO2583
PSPTO2585
PSPTO2586
●
●
○
Pflu2324
Pflu2326
Pflu2327
●
●
●
Glutamine synthase (type II)
PSPTO1921, 5309, 5310
-
Pflu1514, 2163, 3065, 5847,
5849
-
Ammonium transporter (amt-3)
No orthologous hit
Pflu1747
●
Glutamine synthase (type III)
No orthologous hit
Pflu2323
●
● = intergenic σ54
binding motif,
Phenoarrays…
Nitrogen source utilisation
by Pseudomonas
Pf=56
Pa=44
1
1
1
40
2
14
8
Overview of
Pseudomonas
utilisation of 96
nitrogen sources
Ps=64
Amino acid utilisation by Pseudomonas
Nitrogen in natural habitats – the leaf apoplast…
Amino acid region of NMR spectra
glutamine
GABA
Nitrogen metabolism
• Enzymes and metabolites well-defined
• 10+ Pseudomonas genome sequences available
• Diverse ecological niches and selection pressures
• Diversity in nitrogen metabolism
• Experimentally tractable
• Evolving in response to:
• Internal selection (network, flux, regulation)
• External selection (nutrient availability, environment
(e.g. pH, oxygen), host interactions
Modelling the evolution of metabolic
networks…
• Which principle of evolutionary reconstruction
should we apply?
• How do we represent metabolism?
• Which events can happen to a metabolism
• How can we generate models with biological
relevance?
Which principle of evolutionary
reconstruction are we to apply?
Parsimony: evolution has taken the shortest
possible path
Likelihood: evolution has taken the most likely
path based on modelling of all possible
evolutionary events
In practice – often give similar results…
Begin with parsimony? – easier to implement
Evolutionary Metabolic Network Models
Metabolites – Nodes
Reactions - Edges
Adjacency Matrix
Each metabolite is a node (n1, n2, n3, n4…)
For any two nodes I and j : Aij = 1 if there is an edge going from I to j
2 if there is no edge between I and j
0 1 1 0 1


0 0 0 1

A
0 1 1


0 0



0


Dynamical rules for evolution
i) Take two nodes at random
ii) Perform a creation or deletion
of edges with probability μ
Computational Challenges…
Basic question: Computing likelihoods
What is the probability of two observed homologous metabolic networks
Principal answer…
Sum over all possible evolutionary histories
Problem…
Computationally intensive!
Potential strategies…
(i) Develop recursive relations and dynamic programming algorithms
(ii) Markov Chain Monte Carlo methods
Illustrated Metabolism
Network Model
Metabolism Network
0 1 1 0 1


0 0 0 1

A
0 1 1


0 0



0

Adding biological relevance…
• Define initial network according to biological model
• Define core metabolism – label nodes that cannot be deleted – or
nodes that are omnipresent (environmental metabolite sources)
• Define constraints (e.g. preserve connectedness) – label nodes with
allowed changes
• Restrict changes to nodes with at least one allowed change
• Add directionality to connections
• Relate to biological data and evolutionary models
• Network structural features – scale free? How many metabolites?
Ps1
Ps2
P. syringae
Leaves of
specific
plant
species
Ps3
Pf1
Pf2
P. fluorescens
Leaf
surface
and soil
Pf3
Pp1
Pa1
P. putida
P. aeruginosa
One metabolism – accurate graph
Two metabolisms – one metabolism changes into another
Three metabolisms – define ancestral metabolism
Four metabolisms – analysis is phylogeny dependent
Soil
Soil and
animals
Relating model evolution to organismal
evolution…
• Do nodes (metabolites) and edges (enzymes) evolve
at the same rate ?
• Is it reasonable to assume a fixed rate of evolutionary
change?
• Is it reasonable to assume that networks are scale
free?
• Detect and exclude non-functional metabolisms to
produce credible results. What criteria should we use
to define “non-functional” metabolisms ?
Exploring the impact of natural selection on
metabolic networks…
• Is it valid to assume a fixed ‘pool’ of metabolites over
evolutionary time and have just the reactions changing ?
• Can we explore the role of niche-specific conditions in
network evolution by defining core “available”
metabolites ?
• Can we develop theories about how and why selection
has acted on networks by modulating selected variables
(e.g. nitrogen source and availability)
Pathogenic Pseudomonas show clonal population dynamics…
Infection
Apoplast
Modulation
of plant/host
physiology
Impact on
other
organisms
in ecosystem
Defined Niche
Dissemination
Infection
Rhizosphere
Modulation
of plant/host
physiology
Impact on
other
organisms
in ecosystem
Heterogenous
Niche
Dissemination
Relating network models to evolutionary models…
Are parsimony and maximum likelihood equally valid
principles for studying network evolution ?
Can we use network models as a basis for phylogenetic
trees ?
Mycoplasma
Chlamydia
α
γ
γ
γβ
α
γ
β
γβ
γβ
γ
Gram +ve
Consensus tree of 100 jacknife trials
based on presence or absence of 7677
Pfam domain families
Cyanobacteria
Archaea
Mycoplasma/Ureaplasma species
ONION YELLOWS PHYTOPLASMA
Borrelia burgdorferi
Treponema pallidum
Chlamydia species
Wigglesworthia glossinidis
Buchnera species
Candidatus Blochmannia floridanus
Tropheryma whipplei
Bartonella species
Rickettsia species
Wolbachia pipientis
Coxiella burnetii
Haemophilus ducreyi
Pasteurella multocida
Haemophilus influenzae
Nitrosomonas aerogenes
Neisseria meningitidis
XYLELLA FASTIDIOSA
XYLELLA FASTIDIOSA Temecula1
Caulobacter crescentus
Brucella melitensis
Rhodopseudomonas palustris
BRADYRHIZOBIUM JAPONICUM
AGROBACTERIUM TUMEFACIENS
SINORHIZOBIUM MELILOTI
MESORHIZOBIUM LOTI
Acinetobacter species
Bordetella species
XANTHOMONAS CAMPESTRIS
XANTHOMONAS AXONOPODIS
Chromobacterium violaceum
RALSTONIA SOLANACEARUM
PSEUDOMONAS SYRINGAE
Pseudomonas putida
Pseudomonas aeruginosa
Photorhabdus luminescens
ERWINIA CAROTOVORA
Yersinia pestis KIM
Salmonella species
Escherichia coli
Shigella flexneri
Shewanella oneidensis
Vibrio cholerae
Photobacterium profundum
Vibrio vulnificus
Vibrio parahaemolyticus
Deinococcus radiodurans
Firmicutes (Low GC Gram positives)
Actinomycetes (High GC Gram positives)
Thermotoga maritima
Thermotoga denticola
Fusobacterium nucleatum
Bacteroides thetaiotamicron (Low GC Gram positives)
Porphrymonas gingivalis
Chlorobium tepidum
Desulfovibrio vulgaris
Geobacter sulfurreducens
Epsilon Proteobacteria
Aquifex aeolicus
Cyanobacteria
Rhodopirellula baltica
Leptospira interrogans
Bdellovibrio bacteriovorans
Oxford
Jotun Hein
Jon Churchill
Andrea Rocco
David Studholme
(Sainsbury Laboratory – Norwich)
Adaptation of nitrogen assimilation networks may be influenced by:
• Nitrogen source availability and type
• Ability to release nitrogen from complex macromolecules
• Ability to obtain nitrogen through host interactions
• Short and long term variation in nitrogen availability
• Other metabolic factors (e.g. respiration)
• Optimisation of energy consumption
• Consequences of nitrogen utilisation for bacteria-host interactions
(mutually beneficial symbiosis, induction of host defences)
• Evasion of / adaptation to anti-microbial factors (e.g. anti-microbial
peptides transported by N-transporters or inhibitors of N assimilation
enzymes)
Are all events possible?
Are all events equally likely?
G
B
A
C
D
F
E
• Maintain functionality in long term (e.g. retain
intermediate metabolism)
• Maintain core functionality (e.g. retain certain core
metabolites and reactions)
The process
• Define universal/maximal metabolism – all
observed reactions and metabolites
• Extant and ancestral metabolisms represent
subset of universal metabolism
• Metabolisms evolve by having reactions added
or deleted
• Define properties of metabolites (nodes) and
enzymes (edges)
• Estimate probabilities of metabolisms one
evolutionary event away
• Analyse evolution of metabolisms