A High-Throughput Amenable Metasystem to study

Emergence of Host-Microbe Maladaptations

Suresh Gopalan, Ph.D

Department of Molecular Biology, Massachusetts General Hospital

Department of Genetics, Harvard Medical School

Work done late 2006 – mid 2010

Based on presentations at:

1. Broad Institute of Harvard & MIT, Infectious Disease Initiative – Sep 24,

2010

2. Sigma XI Invited Lecture Series, U.S. Army Natick Soldier RD&E Center

(NSRDEC) – May 25, 2010

A ‘metasystem’ of ‘framework model organisms’ to study

‘emergence’ of host-microbe ‘maldaptations’

‘organismal’

1. Why is it important? – i.e., practical significance

2. What is needed to study?

3. How this simplified model system satisfies that goal?

4. Where can we go from this?

System development point of view and

provide experiments supporting conjectures when possible

Need and rationale

Societal induced

mingling of new host-

microbe combinations

(including zoonotic)

Nosocomial

(hospital acquired infections)

Human Microbiome

niche/composition alteration

http://nihroadmap.nih.gov/hmp/index.asp

Transmission of maladapted microbes

1. Change in ‘system status’ of host and microbe under appropriate

environments favors adaptation to microbe related diseases.

System status

Changes (rewiring and cross-talk) in existing signaling modules &

gene regulatory networks etc.

(e.g., biofilm forming microbe and immuno-compromised host)

2. The changes are characteristic and predictive of types of interaction

3. Continued opportunity to interact would lead to permanent fixation

of this adapted state through genetic changes.

And…

1. Such emergence of adaptation is difficult to study in natural settings.

2. Design of an appropriate model(s) can facilitate study of emergence of

such adaptations under controlled environment.

THE PROPOSITION

MAPK cascade

NPR1

nucleus

LRR

CC

NBS NBS

LRR

TIR

Variable:

Kinase /

PEST/

nothing

kinase

Pto PBS1

SENSORS OTHER PATHWAYS

LRR

TIR

LRR

TIR

NBS

nucleus

Viral RNA/

dsDNA

Immunity (including anti - pathogenics , inflammation, cell death)

TLRs NLRs

PLANTS WORMS MAMMALS

A model for discussion today:

Host: Arabidopsis seedlings in a submerged environment

Microbes: Human opportunistic pathogens

Plant pathogens

Commonly ‘innocuous’ laboratory microbes

That recapitulates some features of the proposed need………….

Visual phenotype of Arabidopsis seedlings interacting with different microbes

Ctrl

P. aeruginosa – PA14

B. subtilis

E. coli – Dh5a

P. syringae – DC3000

Does it involve some known virulence components…….?

Ctrl

P. aeruginosa – PA14

PA14::lasR

B. subtilis

E. coli – Dh5a

P. syringae – DC3000

DC3000::hrcC

DC3000/AvrB

lasR: a key regulator of quorum sensing and a subset of virulence factor expression

GacA/GacS

RsmY/RsmZ

LasI/LasR RhlI/RhlR

HCN, pyocyanin, biofilm, virulence

MAPK cascade

NPR1

nucleus

LRR

CC

NBSNBS

LRR

TIR

Variable:

Kinase /

PEST/

nothing

kinase

PtoPBS1

SENSORS

Immunity

PLANTS

MAPK cascade

NPR1

nucleus

LRRLRR

CC

NBSNBS

LRR

TIR CC

NBSNBS

LRR

TIR

Variable:

Kinase /

PEST/

nothing

kinase

PtoPBS1

SENSORS

Immunity

PLANTSCtrl

P. aeruginosa – PA14

PA14::lasR

B. subtilis

E. coli – Dh5a

P. syringae – DC3000

DC3000::hrcC

DC3000/AvrB

host pcd hrcC Defense Avr R AvrB Simple microbial growth……..?

day 0 day 3

microbe medium

conditioned

medium whole well

PA14 5.38 6.9 SD 0.27 7.7 SD 0.39 > 9.5

PA14::lasR 5.30 ND ND > 9.5

B.subtilis 4.00 4.4 SD 0.5 5.6 SD 0.16 6.6 SD 0.18

E. coli 5.62 6.3 SD 0.15 5.35 SD 0.13 7.7 SD 0.3

DC3000 4.84 6.9 SD 0.02 5.04 SD 0.35 > 9.5

DC3000/AvrB 4.70 ND ND > 9.5

DC3000::hrcC 4.84 ND ND > 9.5

Bacteria do not grow well in the plant growth medium

&

Bacterial load does not correlate with visual host damage

Can we get past visual symptoms?

Readout RMP: Relative metabolic potential

RMP

Host growth

Host immunity

Pathogen growth rate &

pathogen load

Virulence effectors RMP

Host growth

Host immunity

Pathogen growth rate &

pathogen load

Virulence effectors

Use a reporter (luciferase for e.g.,) under a constitutive promoter e.g., 35S as a readout?

ONE measure of RMP:

Seed source: Albrecht von Arnim, UTennessee

Luciferase activity as a measure of host damage

Luciferase expressed under a CaMV 35S constitutive promoter

TopCountNXT: Brian Seed’s lab - CCIB

A

0

1

2

3

4

5

6

7

Ctr

l

PA

14

las

R

Bs

Ec

DC

DC

/Av

rB

hrc

C

Lo

g10 R

LU

day 0 day 3 day 5A

0

1

2

3

4

5

6

7

Ctr

l

PA

14

las

R

Bs

Ec

DC

DC

/Av

rB

hrc

C

Lo

g10 R

LU

day 0 day 3 day 5

host pcdhrcC DefenseAvr RAvrB

Would every microbe cause similar damage to varying extents…..?

Ctrl

B. subtilis

S. aureus

Additional evidence for relevance and variety

Day 4

1. Not every microbe will cause host damage in this system (i.e., not non-specific)

3 dpi

Ctrl

Dh5a

Dh5a - GFP

Ctrl

Dh5a

Dh5a - GFP

Ctrl

Dh5a

Dh5a - GFP

2. Even laboratory E.coli causes damage through active host-microbe interaction

0

1

2

3

4

5

6

Ctr

l

PA

14

lasR

pscD

toxA

exo

TU

Y

hcn

C_1

hcn

C_2

PA

O1

PA

14/G

FP

Bs

Lo

g10R

LU

Newer virulence factors to be discovered in P. aeruginosa

PA14 mutants: Rahme, Tan, Miyata, Drenkard, Liberati, Urbach, Ausubel

AMENABILITY TO HIGH-THROUGHPUT AUTOMATION ASSISTED SCREENS

A

0

1

2

3

4

5

6

7

Ctr

l

PA

14

las

R

Bs

Ec

DC

DC

/Av

rB

hrc

C

Lo

g10 R

LU

day 0 day 3 day 5

0

1

2

3

4

5

6

7

Ctrl

PA14

Kan

/PA14

Gen

t/PA14

RL2

244

Kan

/RL22

44

Gen

t/RL22

44

log

10R

LU day 0

day 3

day 5

A POWERFUL SYSTEM TO IDENTIFY POTENT ANTI-INFECTIVES

BY COMPOUND & OTHER SCREENS

No evidence for biofilm formation on leaves

One of the many evidences for importance of using an organismal model host

Do the different microbes cause similar damage?

SYTOX GREEN PROBE

Sytox green staining of membrane permeabilized cells

Visible light

Expected fluorescence pattern

Laser: 488 nm; Dichroic: 560 DLRP

Red: Em 610 LP Green: Em 510-540

Fluorescence based assay is also quantitative - Isocyte trial 1

Luminescence and Fluorescence (two color) serve as two complementary

read-outs for different aspects of ‘system status’

RMP vs. host membrane damage

Remarkably simple workflow!

Do the different microbes cause similar damage?

SYTOX GREEN PROBE

Syto59

Scale bar: 50 mm

DC DC/AB PA14

Some characteristic damages revealed by Sytox green staining

Akin to necrotizing fasciitis ?

Plan to test in mice with Mike Wessels & Laurence Rahme

ctrl

ctrl

50 µm 50 µm

50 µm

DC3000

5 µm 5 µm

5 µm

PA14

lasR - pervasive

Characteristic stomatal staining pattern during infection with PA14

PA14

lasR

Scale bar: 10 mm

Does this mean bacteria invade guard cells…?

Despite characteristic stating pattern, no evidence of intact bacteria in stomatal

guard cells during interaction with P. aeruginosa

EM: Mary McKee – Program in Membrane Biology/CSB

Scale bar: 2 mm

Scale bar: 500 nm

1. Under appropriate conditions even innocuous microbes can adapt to cause

significant host damage

SUMMARY (so far..)

1. Under appropriate conditions even innocuous microbes can adapt to cause

significant host damage

2. A model system utilizing and highlighting such potential (genetics, biology)

to study such adaptations

3. Not general or non-specific

4. Known virulence factors and mechanisms are operative

5. High-throughput automation assisted screens – read-outs for..

6. These interactions represent different modes of adaptation

7. Note, we haven’t given an opportunity for genetic change yet!

8. Predictive ‘System status’ changes of preexisting components and signaling

machinery in host and microbe????

SUMMARY (so far..)

GacA/GacS

RsmY/RsmZ

LasI/LasR RhlI/RhlR

HCN, pyocyanin, biofilm, virulence

Evidence for bacterial ‘system status’ change

20 µm gacA

PA14 = gacA

GacA, LasR role in worms, plants and other pathosystems….

PA14 vs. gacA

0

1

2

3

4

5

6

7

ctrl pa14 gaca lasR RL2244 Dh5a

day0/1

day3/1

day5/1

day0/3

day5/3

10 µm10 µm

lasR

=

gacA/lasR

LasR replacement cassette through Eliana Drenkard

Evidence for bacterial ‘system status’ change

PA14 or gacA

GacA/GacS

RsmY/RsmZ

LasI/LasR RhlI/RhlR

virulence

GacA/GacS

LasI/LasR RhlI/RhlR

virulence

X

?

?

Identifying Novel Rewired Signaling Modules

Evidence for host ‘system status’ alteration in this system

Observed………. Stomatal guard cell patterning defect…….

Expected………..?

Uninfected

PA14

PA14::lasR

Expected…

1. Single cell spacing rule!

2. Set of LRR containing RLKs,

a peptide ligand,

a specific MAP kinase cascade

Myb related transcription factors

IMPLY: Host ‘system status’ (hormone, inter-cellular signals etc.) altered

in this system – probably affecting the execution of immune response

e.g., as in the case of DC3000/AvrB seemingly clustered cell death,

but no defense.

Submerged seedlings do show induction of defense related genes –

earlier work with bacterial and host derived defense elicitors

Denoux…… Gopalan ..Ausubel, Dewdney and microarray data (not shown)

Pieterse et. al., volume 5 number 5 MAY 2009 nature chemical biology review

IMPAIRED HORMONAL SIGNALING INTERACTIONS

IMPAIRED HORMONAL SIGNALING INTERACTIONS

PDF1.2::GUS

0

1000

2000

3000

4000

5000

6000

7000

8000

Ctrl

PA14

gacA

lasR B

sEc

DC

DC/A

Bhr

cC

PA-4

8h

Bs-

48h

DC/A

B-4

8h

PR1

PDF1.2

PA14

lasR

B.s

Xcc

Xcr

PR1::GUS

‘System status change’

= crosses with PDF1.2::GUS

SID2

SA

NPR1

PR1

C

N

AOS

JA

JAR1

JA - Ile

SCF/COI1

MYC2/JIN1

ET

CTR1

EIN2

EIN3

ERF1

JAZs

PDF1.2

Pst

Cor

SID2

SA

NPR1

PR1

C

N

AOS

JA

JAR1

JA - Ile

SCF/COI1

MYC2/JIN1

ET

CTR1

EIN2

EIN3

ERF1

JAZs

PDF1.2

Pst

Cor

High-throughput measurement technologies

DNA, RNA, Protein measurements

Protein, metabolite measurements Next Generation Sequencing

MetaCyc

Klipp & Leibermeister, 2006

Computational and Integration tools and Knowledge bases

ROLE OF A CONSERVED MODULE??

Fig. 13 A core network of two modules negatively correlated to each other (top left, red edges); all genes in the two modules are positively correlated to each other (bottom left, blue edges). Upstream elements (overlapping modules) are represented as green nodes with black edges.

Data Source: Arabidopsis MPSS Plus: miRNA targets - Solexa, Blake Meyers, Pam green etc.,

147 miRNA, 74 unique members, 208 unique target genes

Signal Value range:

untreated: 80

PA14: 1600

laccase family protein / diphenol oxidase family proteinPA14 gacA lasR B. subtilis E. coli DC3000 DC3000/AvrB DC3000::hrcC

Ratio 19.97 16.19 12.09 6.45 3.51 7.93 8.51 1.47

Tempting to speculate……..

A possible miRNA regulated gene,

or a regulated miRNA

Organism Every gene Special Knowledge Framework

Arabidopsis

Transposon

insertions in most

known coding

genes and other

parts of genome

Already evident

alteration in cross-

regulation of known

dominant innate

immune responses

Y

P.aeruginosaNearly ever gene

(Ausubel lab)

Highly antibiotic

resistant Already

evident novel

regulatory

mechanisms

Y

B. subtilis

Under construction

(David Rudner et.

al, Broad)

Resemble

necrotizing fasciitis?

Knowledge to B.

anthracis (for e.g.)?

Y

E.coli Available

Currently the

serendipitous strain

mutation(s)

Y

P. syringae Not available

How microbe keeps

host alive

(metabolically

active?)

Y

X. campaestris Not available

Can be used to

confirm some

hypotheses

N

SYSTEM AMENABLE TO CHEMICAL & GENETIC SCREENs AND

OVERLAY WITH OTHER GENETIC, METABOLIC, SIGNALING, AND

NEW ‘TO BE INFERRED’ INFORMATION FROM SYSTEM-WIDE DATA

A ‘metasystem’ of ‘framework model organisms’ to study

host-microbe ‘maldaptations’

Metasystem: Each microbe (representing different modes of interaction)

interacting with the host Arabidopsis seedling (organismal).

Framework model organisms: Each organism used here are extensively

studied models with large resources, and are considered benchmark for

building new theories, technologies etc.

Maladaptations: Commonly considered ‘innocuous’ microbes acquiring

capability to inflict host damage under appropriate conditions through

‘system status’ change.

Thus the system positioned well for integrative approach to building a

‘knowledge framework’ on environments that lead to new host-microbe

‘maladaptations’ and extent of adaptations to guide appropriate action.

The system and concept also paves way for complementary models to be built!

niche/composition alteration Nosocomial

(hospital acquired infections)

http://nihroadmap.nih.gov/hmp/index.asp

Societal induced and artificial intermingling

Transmission of maladapted microbes

1. Genetics, readily available tools

2. Many well known dominant pathways

3. High throughput and automation

– genetic (host and microbe) and compound screens

4. Long history of reference and knowledge

5. Continually emerging measurement and computational tools

6. Direct homologous components, structural similarity, modular similarity

with human health and agricultural relevant organisms

Summary advantages: System and Approach

ACKNOWLEDGEMENTS

FRED AUSUBEL

Department of Molecular Biology, Massachusetts General Hospital &

Department of Genetics, Harvard Medical School

Current and former members of the Ausubel Lab

Albrecht von Arnim, University of Tennessee

Brian Seed’s Lab

Center for Computational and Integrative Biology, MGH

Su Chiang, Sean Johnston, ICCB/NERC, HMS - Longwood

Supporters (potential collaborators) on unfunded NIH and other grant Apps.

Fred Ausubel, George Church, Gary Ruvkun, David Rudner,

Laurence Rahme, Michael Wessels

YOU!!!