Nitrogen assimilation in plant-associated bacteria

1. Nitrogen assimilation in plant-associated bacteria Gail M. Preston Department of Plant Sciences University of Oxford

2. Pseudomonas common ancestor Pseudomonas syringae Pseudomonas fluorescens Organic N High O2 Intimate association with plant cells Low competition Organic/inorganic N Med-low O2 Variable association with diverse hosts High competition S. Molin M. Romantschuk Endophyte / Leaf surface Plant Pathogen Leaf surface / Roots Plant Growth-Promoting

3. 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

4. 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

5. Leaves of specific plant species Leaf surface and soil Soil Soil and animals Why study Pseudomonas? Ps1 Ps2 P. syringae Ps3 Pf1 Pf2 P. fluorescens Pf3 Pp1 P. putida Pe1 P. entomophila Pa1 P. aeruginosa 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

6. P. syringae pv tomato 4 AA_permease P. syringae pv. syringae 5 AA_permease P. syringae pv. phaseolicola 5 AA_permease P. putida 21 AA_permease P. fluorescens 18 AA_permease P. aeruginosa 21 AA_permease E. coli 24, Yersina pestis 19, Xanthomonas campestris 11 Xylella fastidiosa 3 d-serine/d-alanine/d-glycine; arginine /ornithine/ putrescine; cadaverine; lysine; histidine; threonine; choline; glutamate; cysteine Proline GABA Ethanolamine Aromatic amino acids In silico predictions: Using the Pfam database to identify over and under-represented domains in P. syringae Amino acid transport

7. X P. syringae pv. tomato

9. Predicting RpoN binding sites

10. RpoN (σ54)regulation of nitrogen metabolism… ● =intergenic σ54 binding motif, ○= intragenic σ54 binding motif, - = no σ54 binding motif

11. Phenoarrays… Nitrogen source utilisation by Pseudomonas

12. Pf=56 Pa=44 1 1 1 40 2 14 8 Overview of Pseudomonas utilisation of 96 nitrogen sources Ps=64

13. Amino acid utilisation by Pseudomonas

14. Nitrogen in natural habitats – the leaf apoplast… Amino acid region of NMR spectra glutamine GABA

15. 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

16. 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?

17. 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

18. 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 • Dynamical rules for evolution • Take two nodes at random • Perform a creation or deletion of edges with probability μ

19. 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… • Develop recursive relations and dynamic programming algorithms • Markov Chain Monte Carlo methods

20. Illustrated Metabolism Network Model Metabolism Network

21. 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?

22. Leaves of specific plant species Leaf surface and soil Soil Soil and animals Ps1 Ps2 P. syringae Ps3 Pf1 Pf2 P. fluorescens Pf3 Pp1 P. putida Pa1 P. aeruginosa One metabolism – accurate graph Two metabolisms – one metabolism changes into another Three metabolisms – define ancestral metabolism Four metabolisms – analysis is phylogeny dependent

23. 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 ?

24. 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)

25. Pathogenic Pseudomonas show clonal population dynamics… Apoplast Dissemination Infection Defined Niche Modulation of plant/host physiology Impact on other organisms in ecosystem

26. Rhizosphere Dissemination Infection Heterogenous Niche Modulation of plant/host physiology Impact on other organisms in ecosystem

27. 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 ?

28. Archaea Mycoplasma Mycoplasma/Ureaplasma species ONION YELLOWS PHYTOPLASMA Borrelia burgdorferi Treponema pallidum Chlamydia Chlamydia species Wigglesworthia glossinidis Buchnera species Candidatus Blochmannia floridanus Tropheryma whipplei α Bartonella species γ Rickettsia species Wolbachia pipientis γ Coxiella burnetii Haemophilus ducreyi Pasteurella multocida Haemophilus influenzae γβ Nitrosomonas aerogenes Neisseria meningitidis XYLELLA FASTIDIOSA XYLELLA FASTIDIOSA Temecula1 Caulobacter crescentus α Brucella melitensis Rhodopseudomonas palustris BRADYRHIZOBIUM JAPONICUM AGROBACTERIUM TUMEFACIENS γ SINORHIZOBIUM MELILOTI MESORHIZOBIUM LOTI Acinetobacter species β Bordetella species XANTHOMONAS CAMPESTRIS γβ XANTHOMONAS AXONOPODIS Chromobacterium violaceum RALSTONIA SOLANACEARUM PSEUDOMONAS SYRINGAE γβ Pseudomonas putida Pseudomonas aeruginosa Photorhabdus luminescens ERWINIA CAROTOVORA Yersinia pestis KIM γ Salmonella species Escherichia coli Shigella flexneri Shewanella oneidensis Vibrio cholerae Photobacterium profundum Vibrio vulnificus Vibrio parahaemolyticus Deinococcus radiodurans Gram +ve 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 Consensus tree of 100 jacknife trials based on presence or absence of 7677 Pfam domain families Epsilon Proteobacteria Aquifex aeolicus Cyanobacteria Cyanobacteria Rhodopirellula baltica Leptospira interrogans Bdellovibrio bacteriovorans

29. Oxford Jotun Hein Jon Churchill Andrea Rocco David Studholme (Sainsbury Laboratory – Norwich)

31. 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)

32. Are all events possible? Are all events equally likely? G C D B A F E • Maintain functionality in long term (e.g. retain intermediate metabolism) • Maintain core functionality (e.g. retain certain core metabolites and reactions)

33. 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