P

P.s. tabaci

Null

P.s. syringae Hrp- (TTSS) mutant

HR

P.s. syringae

>50 pathovars based on host specificityTobacco Bean Tomato

P.s. pv. tabaci P HR HRP.s. pv. syringae HR P HR P.s. pv. tomato HR HR P

30 m

P. s. tomato DC3000 model pathogen

•hosts tomato and Arabidopsis•representative stealth parasite•Hrp type III secretion system (TTSS)

Pseudomonas syringae

Tobacco leaves

ROBIN BUELL - TIGR

ALAN COLLMER Cornell

JIM ALFANO - Nebraska

XIAOYAN TANG - Kansas

ARUN CHATTERJEE - Missouri

GREG MARTIN - BTI

SANDY LAZAROWITZ - Cornell

TERRY DELANEY - Cornell

SAM CARTINHOUR

DAVID SCHNEIDER

CHRIS MEYERS

Modeling of virulence gene regulation networks in P. syringae

Molecular/cellular determinants of plant- bacterium interactions

USDA/ARSCenter for Agricultural Bioinformatics

Cornell Theory Center

NSF PGRP DBI-0077622

Experimental biology

Computational biology

Functional Genomics of the Interactions of Tomato and Pseudomonas syringae pv tomato DC3000

http://pseudomonas-syringae.org

http://monod.cornell.edu

ORF BLAST P value

HMM E value

Microarray signal ratio1

Comment

SyrEPto 0 9.8e-7 NT Toxin (syringomycin) biosynthesis

AvrPphFPto 3e-36 1.7e-6 3±2 Effector

HrpW-related 2e-4 1.9e-6 14±7 Candidate harpin

AvrPphD1Pto 0 1.9e-6 30±17 Effector

AvrXv3Pto 7e-12 3.4e-6 ND Effector

AvrPpiB11Pto 1e-152 7.8e-6 11±9 Effector

AvrPpiB12Pto 1e-150 7.8e-6 10±6 Effector

AvrPphD2Pto 2e-44 3.0e-5 27±11 Effector

HopPtoA2 1e-177 2.8e-4 ND Effector

HopPtoB2 2e-16 2.6e-3 ND Effector

AvrRps4Pto 2e-44 2.5e-2 ND Effector

CorRPto 2e-72 6.6e-2 ND Coronatine biosynthesis regulator 1Relative to 16S and 23S rRNA genes

Virulence-related ORFs newly found by Hidden Markov Model search of P.s. tomato DC3000 genome

48<1e-4

78<1e-3

212<1e-2

Fouts, Abramovitch, Alfano, Baldo, Buell, Cartinhour, Chatterjee, D'Ascenzo, Gwinn, Lazarowitz, Lin, Martin, Rehm, Schneider, van Dijk, Tang, and Collmer. 2002. Proc. Natl. Acad. Sci. USA 99:2275-2280.

Genome of P. s. tomato DC3000

Buell et al. 2003. PNAS 100:10181-10186

6.5 Mb

5,763 ORFs

3,797 ORFs also in P.aeruginosa and P. putida

811 unknown ORFs not in P.a. or P.p.

7% of genome mobile genetic elements

298 ORFs implicated in virulence, including

•38 confirmed TTSS substrates

•19 strong candidates

pDC3000A carries at least 4 avr/hop genes

The problem of genomewide identification of Hrp effector genes in P. syringae

HR

avr

hrp

hrp

HR

hrp

R

avr

R

R

HR

hrp

R

hrp

Effector candidate "Hop"

Effector candidate "Avr"

-10

-35 GGAACT GGCACC GAAACT GAAACC GGAACC

TCACNNA CCACNNA ACACNNA CTACNNA

# of occurrences

# of occurrences

13 1 1 1 45

1 58 1 1

-35

NNN GGAACC NNNNNNNNNNNNNNNN CCACNNA NNN

-10

Most effectors found by avirulence phenotype

•All known avr genes preceded by "Hrp box" promoters

Mutant phenotypes typically weak or lacking

Secretion/injection "Hops" testable, but slow

No common motifs reported in proteins Disease

EEL CEL

tRN

Ale

uor

f4or

f3or

f2

tnpA

orf1 orf8

orf1

avrE

avrF

orf3

orf4

hrpW

orf5

orf6

orf7

avrP

to

avrP

toB

hrp gene cluster

mini-Tn5gus tagging of genes activated by HrpL alternative sigma factor

hrp

hrpL

tRNAleu

Fouts, Abramovitch, Alfano, Baldo, Buell, Cartinhour, Chatterjee, D'Ascenzo, Gwinn, Lazarowitz, Lin, Martin, Rehm, Schneider, van Dijk, Tang, and Collmer. 2002. Proc. Natl. Acad. Sci. USA 99:2275-2280.

EEL CEL

Hrp Pathogenicity Island

ORF1-avrPphF M K N A F D L L V E G L A K D Y N M P P L P D K K H I D E V Y C F E F Q S G M N

ORF2-avrPphF M G N I C G T S G S R H V Y S P S H T Q R I T S A P S T S T H V G G D T L T S I

a. Position 3 or 4 is I, V, or L, and between this residue and the starting M is a polar, positively charged or P residue

b. No MIVLFYW residues appear in position 5.

c. No D or E in first 12 residues.

d. The first 50 residues are amphipathic, rich in polar amino acids, and never have more than 3 of the MIVLFYW group in a row.

e. No C between positions 5 and 50

a c edbViolations:

Violations: none

ORF1-'AvrRpt2

ORF2-'AvrRpt2

Tsiamis et al. 2000. EMBO J. 19:3204

The rules successfully predict which unknown ORFs encode effectors

Petnicki-Ocwieja, Schneider, Tam, Chancey, Shan, Jamir, Schechter, Buell, Tang, Collmer, and Alfano. 2002. Proc. Natl. Acad. Sci. USA 99:7652-7657.