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Nitrogen assimilation in plant-associated bacteria Gail M. Preston Department of Plant Sciences University of Oxford
Nitrogen assimilation in plant-associated bacteria
Gail M. Preston
Department of Plant Sciences
University of Oxford
S. MolinM. Romantschuk
Endophyte / Leaf surface
Plant Pathogen
Leaf surface / Roots
Plant Growth-Promoting
Pseudomonas syringae Pseudomonas fluorescens
Pseudomonas common ancestor
Organic N
High O2
Intimate association with plant cells
Low competition
Organic/inorganic N
Med-low O2
Variable association with diverse hosts
High competition
P. syringae pv. tomato DC3000P. syringae pv. syringae B728a
P. savastanoi pv. phaseolicola 1448a
P. aeruginosa PA01 P. aeruginosa PA14
P. entomophila L48
P. putida KT2440
P. fluorescens Pf-5 P. fluorescens Pf0-1 P. fluorescens SBW25
Genome sequenced strains
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
P. syringae
Ps1
Ps2
Ps3
Pf1
Pf2
Pf3
Pp1
Pa1
P. fluorescens
P. putida
P. aeruginosa
Leaves of specific plant species
Leaf surface and soil
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
Pe1 P. entomophila
Why study Pseudomonas?
P. aeruginosa
21 AA_permease
P. putida
21 AA_permease
P. syringae pv tomato
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 3Proline
GABA
Ethanolamine
Aromatic amino acids
d-serine/d-alanine/d-glycine; arginine /ornithine/ putrescine; cadaverine; lysine; histidine; threonine; choline; glutamate; cysteine
P. fluorescens
18 AA_permease
In silico predictions: Using the Pfam database to identify over and under-represented domains in P. syringae
Amino acid transport
Gene//Domain/Putative FunctionP. 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)
PSPTO0217PSPTO0218
Pflu5953Pflu5952
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 nirBPSPTO3262/3
Pflu3425/4
Nitrate reductaseBifunctional nitrate reductase/sulfite reductase
PSPTO2301 Pflu3426
Nitrate transporter PSPTO2304 Pflu4609
AA_permease domain proteins PSPTO5356, 1817, 2026PSPTO5276
Pflu1674, 5187Pflu5197, 1103, 0315, 2013, 5442 Pflu0368, 4870, 2264, 3375, 4890, Pflu4889, 3091, 3323, 3287, 3148, Pflu3094
Glutamine amidotransferase (class II) Glutamate synthaseAmmonium transporter (amt-2)
PSPTO2583PSPTO2585PSPTO2586
Pflu2324Pflu2326Pflu2327
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
Predicting RpoN binding sitesPredicting RpoN binding sites
● = intergenic σ54 binding motif,
○= intragenic σ54 binding motif,
- = no σ54 binding motif
Gene//Domain/Putative FunctionP. 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)
PSPTO0217PSPTO0218
●●
Pflu5953Pflu5952
●●
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 nirBPSPTO3262/3
●●
Pflu3425/4 ●
Nitrate reductaseBifunctional nitrate reductase/sulfite reductase
PSPTO2301 ● Pflu3426 ●
Nitrate transporter PSPTO2304 ● Pflu4609 ●
AA_permease domain proteins PSPTO5356, 1817, 2026PSPTO5276
●-
Pflu1674, 5187Pflu5197, 1103, 0315, 2013, 5442 Pflu0368, 4870, 2264, 3375, 4890, Pflu4889, 3091, 3323, 3287, 3148, Pflu3094
●○---
Glutamine amidotransferase (class II) Glutamate synthaseAmmonium transporter (amt-2)
PSPTO2583PSPTO2585PSPTO2586
●●○
Pflu2324Pflu2326Pflu2327
●●●
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 ●
RpoN (σ54) regulation of nitrogen metabolism…
Phenoarrays…
Nitrogen source utilisation by Pseudomonas
Pf=56 Pa=44
Ps=64
1
14
40
11
2
8
Overview of Pseudomonas utilisation of 96 nitrogen sources
Amino acid utilisation by Amino acid utilisation by PseudomonasPseudomonas
Amino acid region of NMR spectra
glutamine
GABA
Nitrogen in natural habitats – the leaf apoplast…
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
• 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?
Modelling the evolution of metabolic networks…
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
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
00
110
1000
10110
A
Evolutionary Metabolic Network Models
Metabolites – Nodes
Reactions - Edges
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
0
00
110
1000
10110
A
Illustrated Metabolism
Network Model
Metabolism Network
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?
P. syringae
One metabolism – accurate graph
Two metabolisms – one metabolism changes into another
Three metabolisms – define ancestral metabolism
Four metabolisms – analysis is phylogeny dependent
Ps1
Ps2
Ps3
Pf1
Pf2
Pf3
Pp1
Pa1
P. fluorescens
P. putida
P. aeruginosa
Leaves of specific plant species
Leaf surface and soil
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 ?
• 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)
Exploring the impact of natural selection on metabolic networks…
Apoplast
Modulation of
plant/host physiology
Dissemination
Defined NicheInfection
Impact on other
organisms in ecosystem
Pathogenic Pseudomonas show clonal population dynamics…
Rhizosphere
Modulation of
plant/host physiology
Dissemination
Heterogenous Niche
Infection
Impact on other
organisms in ecosystem
Are parsimony and maximum likelihood equally valid principles for studying network evolution ?
Can we use network models as a basis for phylogenetic trees ?
Relating network models to evolutionary models…
Consensus tree of 100 jacknife trials based on presence or absence of 7677 Pfam domain families
γβ
α
MycoplasmaChlamydia
Gram +ve
Cyanobacteria
γβ
γ
ONION YELLOWS PHYTOPLASMA
XYLELLA FASTIDIOSAXYLELLA FASTIDIOSA Temecula1
BRADYRHIZOBIUM JAPONICUMAGROBACTERIUM TUMEFACIENSSINORHIZOBIUM MELILOTIMESORHIZOBIUM LOTI
XANTHOMONAS CAMPESTRIS
RALSTONIA SOLANACEARUM
XANTHOMONAS AXONOPODIS
PSEUDOMONAS SYRINGAEPseudomonas putidaPseudomonas aeruginosa
ERWINIA CAROTOVORAPhotorhabdus luminescens
Yersinia pestis KIMSalmonella speciesEscherichia coliShigella flexneriShewanella oneidensisVibrio cholerae
Deinococcus radiodurans
Vibrio vulnificusVibrio parahaemolyticus
Thermotoga maritimaThermotoga denticolaFusobacterium nucleatum
Aquifex aeolicusCyanobacteriaRhodopirellula balticaLeptospira interrogansBdellovibrio bacteriovorans
Epsilon ProteobacteriaGeobacter sulfurreducensDesulfovibrio vulgarisChlorobium tepidumPorphrymonas gingivalis
Actinomycetes (High GC Gram positives)
Firmicutes (Low GC Gram positives)
Bacteroides thetaiotamicron (Low GC Gram positives)
Photobacterium profundum
Chromobacterium violaceum
Bordetella speciesAcinetobacter species
Rhodopseudomonas palustris
Caulobacter crescentusBrucella melitensis
Neisseria meningitidisNitrosomonas aerogenesHaemophilus influenzaePasteurella multocidaHaemophilus ducreyiCoxiella burnetii
Rickettsia species
Bartonella species
Tropheryma whipplei
ArchaeaMycoplasma/Ureaplasma speciesBorrelia burgdorferiTreponema pallidumChlamydia speciesWigglesworthia glossinidisBuchnera speciesCandidatus Blochmannia floridanus
Wolbachia pipientis
α
γβγγ
γβ
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?
• Maintain functionality in long term (e.g. retain intermediate metabolism)
A B C D
E
F
G
• Maintain core functionality (e.g. retain certain core metabolites and reactions)
• 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
The process
