A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin

A Systems Biology Perspec2ve on Plant-‐Pathogen Interac2ons

Leighton Pritchard

A Con2nuum l Pathogenicity is a loaded term:

l  o4en reflects human interest in the system

l  disease on crop plants could be coincidental to ‘wild type’ interac<ons

l A con<nuum of interac<on modes, including symbiosis and pathogenicity

l The loca<on of the system on this con<nuum may depend on context

l  e.g. Pectobacterium atrosep/cum:potato

no impact host death

A basic observa2on Pathogen Host

Biological cells (and organisms) can be represented as networks

Biological networks l Common way to represent structure

l  Several biological subsystems are networks

l Universal representa<on l  All biological systems have parts that can be represented

as networks

l Networks (a.k.a. graphs) are mathema<cally well-‐understood: Graph Theory

l  Many tools exist, relevant to biology




as networks



Biological networks l Metabolic networks (e.g. KEGG)

(generic) Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

Biological networks l Regulatory/signalling networks

(mouse) (Drosophila)

Biological networks l Protein-‐protein interac<on networks

(Arabidopsis/H.arabidopsidis/P.syringae) (yeast)




as networks



What is a network? l Networks have nodes (a.k.a. ver<ces)

l  Nodes typically represent ‘things’:

� proteins, chemical compounds, people, towns, junc<ons…

l Nodes are connected by edges (a.k.a. arcs) l  Edges typically indicate some rela<onship between nodes

� physical interac<on, substrate:product, friends on Facebook

l  Edges may be directed (from one node to another) or undirected (no or ambiguous direc<on)

� chemical conversion: directed; interac<on: undirected

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n1 n2 n1 n2 n1 n2

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Many things are networks l My Facebook friends network:

l  Nodes: people

l  Edges: friendships between people

l Useful concepts for biology: l  ‘friend of a friend’; ‘six degrees of separa<on’; clusters of friends

Solange Mateo Montalcini

Maeve Price

Peter Cock

Catherine Tackley

Gavin Cowie

Steffi Keir

Yvonne McAvoy

Jennifer White

Rachel Clewes

Juan Morales

Karen Faulds

David Ian Ellis

Laura Banasiak

Andrea Semião

Daniel Tackley

Andrew Lipscombe

Bleddyn Hughes

Sue Stovell

Laura Didymus

Hywel GriffithsCharles Twist

Christian Payne

Helen Johnson

Phil Parsonage

Colin McGill

Allan N. Gunn

Will AllwoodKatherine Hollywood

Judith Robertson

Andrew Murdoch

David Broadhurst

Lydia Castelli

Miles Armstrong

Paul Keir

Fiona White Gagg

Lizzie Wilberforce

Joanne Fitchet

Laura Baxter

Alison Gilhespie

Jorunn Bos

James Gagg

Andy Smith

Clare Baxter

Susan Somerville

Neil Bhaduri

Joanna Jones

Colleen Gagg

Susan Quinn McGhee

Al Macmillan

Norman StewartKevin Knox

Susan BreenMichael Barrow

Phil Dennison

Andrew McKenzie

Matthew Blackburn

Christelle Robert

Tim Arrowsmith

Emma Robertson

Jane Ballany

Chris Thorpe

Andrew Dalke

Sonia Humphris

Juan Morales

Eleanor Gilroy

Chris McDonald

Natalie Homer

Anna Åsman

Ruth Polwart

Tim Morley

Kenny Duncan

Iddo Friedberg

Remco Stam

Ramesh Vetukuri

Louise Matheson

Simon Easterman

Philip Law

Craig Shaddy Shadbolt

Simon Garrett

Agata Kaczmarek

Simon Pendlebury

Rays JiangChristiane AusJena

Pedro Mendes

Iris Stone

Ingo Hein

Adriana Ravagnani

Eduard Venter

Charles Gordon

David CookeJonathan Gagg

Roger Jarvis

Ross McMahon

Stefan Engelhardt

Edgar Huitema

Thomas PritchardTracy Canham

Sophien Kamoun

Florietta JupeAmbreen Owen

Hazel McLellan

Many things are networks l My Facebook friends network:

l  Nodes: people

l  Edges: friendships between people

l Useful concepts for biology: l  ‘friend of a friend’; ‘six degrees of separa<on’; clusters of friends

Solange Mateo Montalcini

Maeve Price

Peter Cock

Catherine Tackley

Gavin Cowie

Steffi Keir

Yvonne McAvoy

Jennifer White

Rachel Clewes

Juan Morales

Karen Faulds

David Ian Ellis

Laura Banasiak

Andrea Semião

Daniel Tackley

Andrew Lipscombe

Bleddyn Hughes

Sue Stovell

Laura Didymus

Hywel GriffithsCharles Twist

Christian Payne

Helen Johnson

Phil Parsonage

Colin McGill

Allan N. Gunn

Will AllwoodKatherine Hollywood

Judith Robertson

Andrew Murdoch

David Broadhurst

Lydia Castelli

Miles Armstrong

Paul Keir

Fiona White Gagg

Lizzie Wilberforce

Joanne Fitchet

Laura Baxter

Alison Gilhespie

Jorunn Bos

James Gagg

Andy Smith

Clare Baxter

Susan Somerville

Neil Bhaduri

Joanna Jones

Colleen Gagg

Susan Quinn McGhee

Al Macmillan

Norman StewartKevin Knox

Susan BreenMichael Barrow

Phil Dennison

Andrew McKenzie

Matthew Blackburn

Christelle Robert

Tim Arrowsmith

Emma Robertson

Jane Ballany

Chris Thorpe

Andrew Dalke

Sonia Humphris

Juan Morales

Eleanor Gilroy

Chris McDonald

Natalie Homer

Anna Åsman

Ruth Polwart

Tim Morley

Kenny Duncan

Iddo Friedberg

Remco Stam

Ramesh Vetukuri

Louise Matheson

Simon Easterman

Philip Law

Craig Shaddy Shadbolt

Simon Garrett

Agata Kaczmarek

Simon Pendlebury

Rays JiangChristiane AusJena

Pedro Mendes

Iris Stone

Ingo Hein

Adriana Ravagnani

Eduard Venter

Charles Gordon

David CookeJonathan Gagg

Roger Jarvis

Ross McMahon

Stefan Engelhardt

Edgar Huitema

Thomas PritchardTracy Canham

Sophien Kamoun

Florietta JupeAmbreen Owen

Hazel McLellan

Many things are networks l  Google Maps

l  Nodes: road junc<ons (and end points in culs de sacs)

l  Edges: roads

l  Structure view

l  Flow/traffic view

l  Useful concepts for biology: l  Network ‘flow’ or ‘flux’; distance on a network; shortest path



l  Edges: roads

l  Structure view





l  Edges: roads

l  Structure view



Networks are abstract l Networks are collec<ons of nodes and edges

l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important

l  If a network describes biology well… l  …what is true about the network will be true about the biology

l  (some networks describe biology beèr than others)

l Abstract truths about networks can be true about biology l  If a network of type X is robust to random damage, and a biological

network is of type X, we can say that the biological network is robust to random damage.







Networks are abstract

Networks are abstract















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l Any network with this structure has the same behaviour

l  Behaviour of specific regulatory network is dictated by its structure:

l  Behaviour dependent on structure of system as a whole: need to understand this at a systems level

MacLean and Studholme. A Boolean model of the Pseudomonas syringae hrp regulon predicts a <ghtly regulated system. PLoS ONE (2010) vol. 5 (2) pp. e9101 doi:10.1371/journal.pone.0009101





l Abstract truths about networks can be true about the biology they represent

l  If a network of type X is robust to random damage, and a biological network is of type X, we can say that the biological network is robust to random damage.

Choosing a representa2on l Network should be an adequate representa<on of biology

l  Choice of representa<on should suit biological ques<on

l  e.g. do we represent chemical compounds, or moie<es?







Choosing a representa2on l What does this diagram mean?

l  Are all enzymes expressed at same <me?

l  Are all enzymes expressed in all <ssues?

l  Are all metabolites always available?

l  30-‐40% of metabolic ac<vity has no known gene associated with it (Chen and Vitkup. Distribu<on of orphan metabolic ac<vi<es. Trends Biotechnol (2007) vol. 25 (8) pp. 343-‐348 doi:10.1016/j.<btech.2007.06.001)

Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

Choosing a representa2on l What does this diagram mean?

l  Are all enzymes expressed at same <me?

l  Are all enzymes expressed in all <ssues?

l  Are all metabolites always available?

l  30-‐40% of metabolic ac<vity has no known gene associated with it (Chen and Vitkup. Distribu<on of orphan metabolic ac<vi<es. Trends Biotechnol (2007) vol. 25 (8) pp. 343-‐348 doi:10.1016/j.<btech.2007.06.001)

Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

Choosing a representa2on l Biological networks are dynamic

l  There may be homeostasis, but it’s dynamic homeostasis

l  “The only steady-‐state is death”

l What kind of dynamics?

l  Kine<c equa<ons

l  ODE/Stochas<c representa<on of processes

� e.g. enzyme kine<cs

E + S ⌦ ES ⌦ EP ! E + P

v =[S]V

max

[S] + [Km]





l  Kine<c equa<ons

l  ODE/Stochas<c representa<on of processes

� e.g. enzyme kine<cs

E + S ⌦ ES ⌦ EP ! E + P

v =[S]V

max

[S] + [Km]





l  Boolean (on/off, 0/1)

� e.g. regula<on/signalling

nodes

<me

Host-‐pathogen interac2on Pathogen Host

A representa<on of host and pathogen as two networks


PAMP/MAMP detec<on: host immune receptor detects (interacts with) non-‐self chemical species derived from microbe/pathogen


Effector ac<on I: pathogen-‐derived species (probably protein) interacts with host network component


Effector ac<on II: pathogen-‐derived species (probably protein) manipulates (interacts with) host network process


Effector-‐triggered resistance I: host immune receptor interacts with pathogen-‐derived effector


Effector-‐triggered resistance II: host immune receptor detects self-‐ modifica<on (induced by pathogen effector)


Host-‐pathogen interac2on is the coming together of two networks into a single network: different proper2es than either network alone


How does this affect culturability? Tight connec2on correlates with obligate biotrophy, hence difficult to culture?

Host-‐pathogen interac2on


Pathogen

Host

l How does host/pathogen network respond to interac<on?

l What is best way to aàck a network?

l What is best way to defend against mul<ple aàck strategies?

l Are some parts of a network predictably more influen<al than others?


Pathogen

Host






Pathogen

Host






Pathogen

Host





Influence in networks l Efficient aàckers:

l  cause greatest favourable host disrup<on for least effort

l  should target influen<al points in host network

l Efficient defenders:

l  protect against greatest amount of poten<al change for least effort

l  protect against most commonly-‐targeted points in network


l Greatest benefit for least cost

l What are the most influen<al points in a network?



























l What are the most influen2al points in a network?

l  can we predict/iden<fy them?

Robustness in biological networks l Biological networks are typically robust and error-‐tolerant

l  (necessary for descent with modifica<on)

l  e.g. only 17% of yeast genes essen<al to cell viability in rich media

Winzeler et al. Func<onal characteriza<on of the S. cerevisiae genome by gene dele<on and parallel analysis. Science (1999) vol. 285 (5429) pp. 901-‐906

Robustness in biological networks l Biological networks are typically robust and error-‐tolerant

l  (necessary for descent with modifica<on)

l  e.g. only 17% of yeast genes essen<al to cell viability in rich media

Winzeler et al. Func<onal characteriza<on of the S. cerevisiae genome by gene dele<on and parallel analysis. Science (1999) vol. 285 (5429) pp. 901-‐906

Structural robustness in biological networks

l Some network structures enhance robustness

l  Many biological networks have converged to same network structures

Barabási and Oltvai. Network biology: understanding the cell's func<onal organiza<on. Nat Rev Genet (2004) vol. 5 (2) pp. 101-‐13 doi:10.1038/nrg1272 Kitano. Biological robustness. Nat Rev Genet (2004) vol. 5 (11) pp. 826-‐37 doi:10.1038/nrg1471

•  Aa: random Erdös-‐Renyi graph: not robust to random aàck (not common in biology)

•  Ba: random ‘scale-‐free’ network: robust to random aàck (most biological networks)

•  Ca: hierarchical network: robust to random aàck (many signalling networks)

l Some network structures enhance robustness

l  Many biological networks have converged to same network structures

Barabási and Oltvai. Network biology: understanding the cell's func<onal organiza<on. Nat Rev Genet (2004) vol. 5 (2) pp. 101-‐13 doi:10.1038/nrg1272 Kitano. Biological robustness. Nat Rev Genet (2004) vol. 5 (11) pp. 826-‐37 doi:10.1038/nrg1471

•  Aa: random Erdös-‐Renyi graph: not robust to random aàck (not common in biology)

•  Ba: random ‘scale-‐free’ network: robust to random aàck (most biological networks)

•  Ca: hierarchical network: robust to random aàck (many signalling networks)


l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network

l  dele<on or disrup<on dissociates (breaks) the network


•  Pathways from detec<on (e.g. immune recep<on) to host response

•  Signalling pathways

•  E.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’




MAMP detec2on



•  e.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’




MAMP detec2on




chi<n

chi<nase




MAMP detec2on




chi<n

chi<nase

Avr4




MAMP detec2on

•  Redundancy and cross-‐talk in signalling pathways protects against this fragility

•  e.g. PTI/ETI cross-‐talk

l Network hubs l  highly-‐connected nodes

l  characteris<c of ‘scale-‐free’ (and similar) networks



•  Why do hubs occur?

•  How many hubs do we expect?

•  How are they related to biology?

l Network hubs l  highly-‐connected nodes

l  characteris<c of ‘scale-‐free’ (and similar) networks



•  Why do hubs occur?

•  How many hubs do we expect?

•  How are they related to biology?

l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Robust because of node degree distribu<on

l  Very few ‘hubs’; most nodes make few connec<ons

l  Random dele<on more likely to remove node with few connec<ons


Albert et al. Error and aàck tolerance of complex networks. Nature (2000) vol. 406 (6794) pp. 378-‐82 doi:10.1038/35019019

l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Robust because of node degree distribu<on

l  Very few ‘hubs’; most nodes make few connec<ons

l  Random dele<on more likely to remove node with few connec<ons


Albert et al. Error and aàck tolerance of complex networks. Nature (2000) vol. 406 (6794) pp. 378-‐82 doi:10.1038/35019019

l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Diagnos<c ‘degree distribu<on’ (count of connec<ons to each node)

l  Yeast protein interac<on network has power-‐law distribu<on

l  Essen<al 17% of genes correlated with highly-‐connected nodes (hubs)


l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Diagnos<c ‘degree distribu<on’ (count of connec<ons to each node)

l  Yeast protein interac<on network has power-‐law distribu<on

l  Essen<al 17% of genes correlated with highly-‐connected nodes (hubs)


l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Most studied biological networks are ‘scale-‐free’

l  ‘Scale-‐free’ property proposed to arise from network evolu<on

l  ‘older’ nodes more likely to be hubs

l  ‘older’ nodes more likely to be func<onally-‐conserved, sequence constrained?

l  Hubs are good targets for network disrup<on: what role do they play in pathogen/host evolu<on?


l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Most studied biological networks are ‘scale-‐free’

l  ‘Scale-‐free’ property proposed to arise from network evolu<on

l  ‘older’ nodes more likely to be hubs

l  ‘older’ nodes more likely to be func<onally-‐conserved, sequence constrained?

l  Hubs are good targets for network disrup<on: what role do they play in pathogen/host evolu<on?


l Bacterial Type III effectors engage a limited set of host processes across host kingdoms e.g.:

l  turnover by modula<on of ubiqui<na<on

l  altera<on of transcrip<on

l  altera<on of phosphoryla<on

l Strategies such as the targe<ng of ubiqui<na<on are used by bacterial fungal and oomycete pathogens across a range of hosts


l Bacterial Type III effectors engage a limited set of host processes across host kingdoms e.g.:

l  turnover by modula<on of ubiqui<na<on

l  altera<on of transcrip<on

l  altera<on of phosphoryla<on

l Strategies such as the targe<ng of ubiqui<na<on are used by bacterial fungal and oomycete pathogens across a range of hosts


The Guard Hypothesis l The Guard Hypothesis describes indirect R gene:effector interac<on

l  Direct R gene:effector interac<on could lead to overwhelming R gene load

l  A. thaliana has ≈200 R genes (1% of gene complement)

l  If ‘hubs’ are common targets for pathogens…

l  …guarding the hub with one R gene is more efficient than gene-‐for-‐gene interac<ons

l  …network topology implies the Guard Hypothesis

l  If ‘hubs’ are universal targets… l  …network topology determines which

nodes are likely to be involved in host-‐pathogen interac<on Dangl and Jones. Plant pathogens and integrated

defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161








nodes are likely to be involved in host-‐pathogen interac<on Dangl and Jones. Plant pathogens and integrated

defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161

Dangl and Jones. Plant pathogens and integrated defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161








nodes are likely to be involved in host-‐pathogen interac<on

Interac2ons with hubs l Host: Arabidopsis thaliana

l Pathogens: Pseudomonas syringae, Hyaloperonospora arabidopsidis

l  Independent effector evolu<on

l  Matrix-‐2-‐hybrid (yeast-‐2-‐hybrid)

l  Pathogen effectors share more common targets than expected (if random)

l  Common targets more highly connected (i.e. are ‘hubs’) than expected (if random)

Mukhtar MS, et al. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333: 596–601. doi:10.1126/science.1203659.















Modules in networks l Mo<fs are small subnetworks

l  Many have specific dynamic and logic behaviour:

� Accelerate/slow response

� Enforce sequen<al responses

� Lock signal on or off

� Filter out noise in signals

� Generate pulse in response to signal

� Generate oscilla<ons

�  Integrate and process mul<ple signals

Shoval and Alon. SnapShot: network mo<fs. Cell (2010) vol. 143 (2) pp. 326-‐e1 doi:10.1016/j.cell.2010.09.050



� Accelerate/slow response

� Enforce sequen<al responses

� Lock signal on or off

� Filter out noise in signals



�  Integrate and process mul<ple signals







Modules in networks l Bow-‐<e structure

l Many inputs → restricted set of intermediates → many outputs

Modules in networks l Bow-‐<e structure

l Many inputs → restricted set of intermediates → many outputs

l  e.g. complex nutrients → metabolic intermediates → complex compounds

Modules in networks l Open ques<ons:

l  Do aàckers preferen<ally target (or introduce) par<cular mo<fs?

l  Do aàckers preferen<ally target the ‘knots’ of bow-‐<e structures?

Influence in networks l Network structure (topology) is not everything

l Network topology is determined by dynamic processes

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Idealised topology Expression paèrn 1 Expression paèrn 2

Influence in networks l Network structure (topology) is not everything

l Dynamic processes are overlaid on topology

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Idealised topology Reac<on kine<cs dictate rela<ve flux

v =[S]V

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v =[S]V

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v =[S]V

max

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Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA)

l Some processes more influen<al because of dynamic (kine<c) considera<ons

l  ODE representa<on of biochemical network

l  Used to understand biochemical pathways

l  Used in ra<onal drug design: target/priori<se elements with large control coefficients

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Reac<on kine<cs dictate rela<ve flux

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

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[S] + [Km]

v =[S]V

max

[S] + [Km]

Kacser and Burns. The molecular basis of dominance. Gene/cs (1981) vol. 97 (3-‐4) pp. 639-‐66 Kacser and Burns. The control of flux. Biochem Soc Trans (1995) vol. 23 (2) pp. 341-‐66 Westerhoff and Kell. What biotechnologists knew all along ...?. J Theor Biol (1996) vol. 182 (3) pp. 411-‐420 Sato et al. Network Modeling Reveals Prevalent Nega<ve Regulatory Rela<onships between Signaling Sectors in Arabidopsis Immune Signaling. PLoS Pathog (2010) vol. 6 (7) pp. E1001011 doi:10.1371/journal.ppat.1001011


l Some processes more influen<al because of dynamic (kine<c) considera<ons

l  ODE representa<on of biochemical network

l  Used to understand biochemical pathways

l  Used in ra<onal drug design: target/priori<se elements with large control coefficients

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v =[S]V

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v =[S]V

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v =[S]V

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[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

Kacser and Burns. The molecular basis of dominance. Gene/cs (1981) vol. 97 (3-‐4) pp. 639-‐66 Kacser and Burns. The control of flux. Biochem Soc Trans (1995) vol. 23 (2) pp. 341-‐66 Westerhoff and Kell. What biotechnologists knew all along ...?. J Theor Biol (1996) vol. 182 (3) pp. 411-‐420 Sato et al. Network Modeling Reveals Prevalent Nega<ve Regulatory Rela<onships between Signaling Sectors in Arabidopsis Immune Signaling. PLoS Pathog (2010) vol. 6 (7) pp. E1001011 doi:10.1371/journal.ppat.1001011


l Key points: l  Rela<ve change in pathway flux in response to a change

in [enzyme] is the flux control coefficient

l  Rela<ve change in [metabolite] in response to a change in [enzyme] is the concentra2on control coefficient

l  Control coefficient = 0 ⇒ no influence

l  Control coefficient = 1 ⇒ strong posi<ve influence

l  Control coefficient = -‐1 ⇒ strong nega<ve influence

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v =[S]V

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l Key points: l  Rela<ve change in pathway flux in response to a change

in [enzyme] is the flux control coefficient

l  Rela<ve change in [metabolite] in response to a change in [enzyme] is the concentra2on control coefficient

l  Control coefficient = 0 ⇒ no influence

l  Control coefficient = 1 ⇒ strong posi<ve influence

l  Control coefficient = -‐1 ⇒ strong nega<ve influence

l We might expect aWackers to target network elements with large control coefficients

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l Key points: l  Control coefficients dependent on rest of network:

calculated at same <me

l  Control coefficients are a system-‐level property (can’t be determined in isola<on)

l  It is unusual for any single element to have complete control over any part of the network

l  (Nearly) no rate-‐limi<ng steps

l  Any part of the network is typically under control of mul<ple other network elements

l  Distributed/democra<c control is the norm

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Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904 D. Fell, Understanding the Control of Metabolism, first ed., Portland Press, 1997.








l  Distributed/democra<c control is the norm

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l  Distributed/democra2c control is the norm

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v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]

v =[S]V

max

[S] + [Km]


Metabolic Control Analysis (MCA) l Yeast glycolysis

l Most enzyme kine<c parameters known

l Fit to known fluxes, then parameter-‐scan (>8000 dis<nct simula<ons)

l Three regimes of control found:

l  Main regime: only significant control by hexose transport (HXT) and hexokinase (HK)

l  Minor regime: HXT, HK and alcohol dehydrogenase (ADH)

l  Biologically inaccessible regime: [GLCi] ≈ 300mM phosphofructokinase (PFK) control

Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904







l  Biologically inaccessible regime: [GLCi] ≈ 300mM phosphofructokinase (PFK) control








l  Biologically inaccessible regime: [GLCi] ≈ 300mM under phosphofructokinase (PFK) control



l HXT dominates pathway control

l External [hexose] is a signal, as HXT is sensi<ve to it.


Distributed Control l MCA implies distributed control of networks

l Network topology also implies distributed control (minimal interven<on sets: MIS)

l What does this imply for host-‐pathogen interac<ons?

l  Several points in network are influen<al

� Can be predicted with sufficient informa<on about system

l  A pathway/network element may be under distributed control

� May need to hit several parts of the network to produce change

� Single effectors unlikely to be sufficient


















l Network topology also implies distributed control




� Pathogens may require ‘sets’ of effectors

�  Implies ‘Redundant Effector Groups’ and func2onal redundancy?

Kvitko et al. Dele<ons in the repertoire of Pseudomonas syringae pv. tomato DC3000 type III secre<on effector genes reveal func<onal overlap among effectors. PLoS Pathog (2009) vol. 5 (4) pp. E1000388 doi:10.1371/journal.ppat.1000388


l Network topology also implies distributed control


l  Context-‐dependence of effector func<on:

� H.arabidopsidis ATR13 suppresses callose deposi<on

� P. syringae HopM1 suppresses callose deposi<on

� ATR13 complements callose deposi<on, but does not fully restore virulence in HopM1 mutant (EDV)

K.H. Sohn, R. Lei, A. Nemri, J.D.G. Jones, The downy mildew effector proteins ATR1 and ATR13 promote disease suscep<bility in Arabidopsis thaliana, Plant Cell 19 (2007) 4077–4090.

Distributed Control l We can consider ‘system’ as defining a landscape, permi~ng types of control

l Autocra<c control: l  Flat landscape

l  Can move any network element to any ‘state’

l Democra<c control:

l  Rugged landscape (constrained by rest of network)

l  Network elements restricted to ‘valleys’ in the landscape

Bar-‐Yam et al. Systems biology. A`ractors and democra<c dynamics. Science (2009) vol. 323 (5917) pp. 1016-‐7 doi:10.1126/science.1163225





















l Pathogens introduce new elements that change the landscape: effectors


A state-‐based model of interac2on l Prevailing model: zig-‐zag(-‐zig…)

Hein et al. The zig-‐zag-‐zig in oomycete-‐plant interac<ons. Mol Plant Pathol (2009) vol. 10 (4) pp. 547-‐62 doi:10.1111/j.1364-‐3703.2009.00547.x

Jones and Dangl. The plant immune system. Nature (2006) vol. 444 (7117) pp. 323-‐9 doi:10.1038/nature05286


l Has some problems:

l  scope (only host immune system, not rest of interac<on with pathogen)

l  ordering of events (are PTI/ETI etc. dis<nct and well-‐ordered?)

l  <mescale (evolu<onary, or during interac<on?)

l  size scale (organism or cell level)

l  Quan<ta<ve or qualita<ve (what is the ‘amplitude’ of defence?)




































l  Is there a more general framework for host-‐pathogen interac2ons?

Pritchard L, Birch P (2011) A systems biology perspec<ve on plant-‐microbe interac<ons: Biochemical and structural targets of pathogen effectors. Plant Science 180: 584–603. doi:10.1016/j.plantsci.2010.12.008.

A state-‐based model of interac2on l Biological cells can be represented as networks

l Each element in the network can be quan<fied:

l  enzyme concentra<on (or expression level)

l  metabolite concentra<on

l  phosphoryla<on/ubiqui<na<on/charge states as dis<nct en<<es

l  etc.

l We represent lists of values as vectors

[v1, v2, v3, . . . , vk]






l  etc.

l We represent ordered lists of values as vectors

[v1, v2, v3, . . . , vk]






l  etc.

l We represent ordered lists of values as vectors

[v1, v2, v3, . . . , vk]

A state-‐based model of interac2on l Vectors are co-‐ordinates in space

l  vectors of length two: points on a surface (2D space)

l  vectors of length three: points in 3D space

l  vectors of length k: points in k-‐dimensional space

l Points that are close together are ‘similar’

A state-‐based model of interac2on l Vectors are co-‐ordinates in space

l  vectors of length two: points on a surface (2D space)

l  vectors of length three: points in 3D space

l  vectors of length k: points in k-‐dimensional space

l Points that are close together are ‘similar’

A state-‐based model of interac2on l Let our vector represent the measured state of the cell (e.g. host-‐pathogen) system

l  enzyme/metabolite concentra<ons, etc.

l Each point in k-‐space represents a different state of the system

l  similar states are close together in k-‐space

[v1, v2, v3, . . . , vk]

A state-‐based model of interac2on l Let our vector represent the measured state of the cell (e.g. host-‐pathogen) system

l  enzyme/metabolite concentra<ons, etc.

l Each point in k-‐space represents a different state of the system

l  similar states are close together in k-‐space

[v1, v2, v3, . . . , vk]

A state-‐based model of interac2on l States that lead to similar phenotypes can be grouped in phases:

l  regions of space where cell state corresponds to named behaviour

l Temporal evolu<on of a cell can be viewed as a transi<on through states

v1

v2

apoptosis

ROS produc<on

seed leaf

root

HR

A state-‐based model of interac2on l States that lead to similar phenotypes can be grouped in phases:

l  regions of space where cell state corresponds to named behaviour

l Temporal evolu<on of a cell can be viewed as a transi<on through states

v1

v2

apoptosis

ROS produc<on

seed leaf

root

HR

A state-‐based model of interac2on l Complex systems can behave in complex ways

l A common feature of complex systems is aJractors

l  A`ractors are ‘endpoints’: states, or sets of states, to which the system is ‘a`racted’

l  Analogous to stable equilibria: when the system is perturbed, it returns to its a`ractor.

l  Do cell phenotypes correspond to a`ractors?
















apoptosis

ROS produc<on

seed leaf

root

HR

A state-‐based model of interac2on l A`ractors are associated with the regions of space that lead to them: ‘basins’

l A`ractors can be: l  Single points

l  Cycles

l  Complex ‘regions’

A state-‐based model of interac2on l A`ractors are associated with the regions of space that lead to them: ‘basins’

l A`ractors can be: l  Single points

l  Cycles

l  Complex ‘regions’

A state-‐based model of interac2on l  Interac<on of a pathogen with the host can push the system from one basin of aJrac/on to another

l There may be mul<ple routes between basins of a`rac<on, depending on the direc<on or <ming of perturba<on

l  There may be more than one way to provoke a specific outcome from the host (or from the pathogen)

A state-‐based model of interac2on l  Interac<on of a pathogen with the host can push the system from one basin of aJrac/on to another

l There may be mul<ple routes between basins of a`rac<on, depending on the direc<on or <ming of perturba<on

l  There may be more than one way to provoke a specific outcome from the host (or from the pathogen)

A state-‐based model of interac2on l Effectors may divert the expected WT system trajectory:

l  ‘Pushing’ the host cell state towards a different aJractor/state

l  ‘State’ may be a developmental checkpoint

l Diversion of the trajectory may also be beneficial to the host

l The pathogen may detect the host state and respond accordingly (e.g. <ssue-‐specific effector produc<on in Us/lago maydis; stage-‐ and <ssue-‐specific oomycete effectors)

v1

v2

nutrient produc<on

PTI

seed Epidermal cell

root

HR

A state-‐based model of interac2on l The Jones-‐Dangl Zig-‐Zag(-‐Zig) model is encapsulated within a state-‐based model

PTI

No challenge

ETI

ETS


PTI

No challenge

ETI

ETS


PTI

No challenge

ETI

ETS


PTI

No challenge

ETI

ETS


PTI

No challenge

ETI

ETS


PTI

No challenge

ETI

ETS

A state-‐based model of interac2on l The state-‐based model has advantages:

l  Scope: can include host and pathogen, and extend beyond host immunity

l  Ordering: explicit ordering of events represented by paths in the model (determined by model)

l  Timescale: explicit (determined by model)

l  Size scale: can include mul<cellular systems

l  Quan2ta2ve or qualita2ve: explicit (dependent on model)

PTI

No challenge

ETI

ETS







PTI

No challenge

ETI

ETS







PTI

No challenge

ETI

ETS







PTI

No challenge

ETI

ETS







PTI

No challenge

ETI

ETS

Summary l Biological systems have natural network representa<ons

l  But representa<on must be reasonable and suit the ques<on being asked

l  Interac<on of host and pathogen makes a new single network from two ini<al networks

l Network topology affects l  Network behaviour

l  Suscep<bility to aàck (hubs, bridges)

l Network dynamics affect

l  Network behaviour

l  Suscep<bility to aàck (distributed control)

l A state-‐based framework may be useful for understanding host-‐pathogen interac<ons

Acknowledgements l Systems Biology at Aberystwyth/Manchester

l  Doug Kell, David Broadhurst, Pedro Mendes, Roy Goodacre, Andy Woodward, Simon Garre`,

l Computa<onal biology at JHI

l  Peter Cock

l Phytophthora research at JHI l  Paul Birch, Steve Whisson, Miles Armstrong

l Bacteriology research at JHI l  Ian Toth, Sonia Humphris, Nicola Holden

l Many, many discussions with colleagues

Danger Theory l Proposed by computer scien<sts in machine learning: avoids detec<on ‘bloat’ of one ‘recogni<on gene’ per threat.

l Popular in (animal) immunology; Analogous to Guard Hypothesis and Dense Overlapping Regions (DORs)

l  Integra<on of mul<ple signals and contextual cues

Aickelin et al. Danger theory: The link between AIS and IDS?. Lect Notes Comput Sc (2003) vol. 2787 pp. 147-‐155

Danger Theory l Some signals ‘cri<cal’ and require immediate response (e.g. avirulence gene products?)

l Other signals contextual – require ‘processing’ (e.g. MAMPs) Boller and Felix. A renaissance of elicitors: percep<on of microbe-‐associated molecular paèrns and danger signals by paèrn-‐recogni<on receptors. Annu. Rev. Plant. Biol. (2009) vol. 60 pp. 379-‐406 doi:10.1146/annurev.arplant.57.032905.105346

Danger Theory l Context dependence and non-‐linear signal may lead to problems of interpreta<on in experiments.

l Danger R when signal ≥ 5 l  a+b+c+d = 6 ⇒ R

l  a+b+c = 4 ⇒ no R

l  a+b+d = 4 ⇒ no R

l  a+c+d = 5 ⇒ R

l  b+c+d = 5 ⇒ R

l  a+b = 3 ⇒ no R

l  {c and d} required for R?



l  a+b+c = 4 ⇒ no R

l  a+b+d = 4 ⇒ no R

l  a+c+d = 5 ⇒ R

l  b+c+d = 5 ⇒ R

l  a+b = 3 ⇒ no R




l  a+b+c = 4 ⇒ no R

l  a+b+d = 4 ⇒ no R

l  a+c+d = 5 ⇒ R

l  b+c+d = 5 ⇒ R

l  a+b = 3 ⇒ no R




l  a+b+c = 4 ⇒ no R

l  a+b+d = 4 ⇒ no R

l  a+c+d = 5 ⇒ R

l  b+c+d = 5 ⇒ R

l  a+b = 3 ⇒ no R


l No: a+b+c+e, a+b+d+e ⇒ R


l Danger R when signal ≥ 5 l  All single knockouts ⇒ R ∴ all receptors redundant?

l  a+c+e = 4 ⇒ no R a+b+c+e = 5 ⇒ R ∴ {a,b} non-‐redundant?

l  a+c+d+e = 5 ⇒ R b+c+d+e = 5 ⇒ R ∴ {a,b} redundant?

l  ‘unequal gene2c redundancy’

Education

A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin