systems biology and genomics of microbial pathogens

Systems Biology and Genomics of Microbial Pathogens

From virulence gene discovery to vaccine development and therapeutic intervention

Ramy Karam Aziz, PhDDepartment of Microbiology and ImmunologyFaculty of Pharmacy, Cairo University, Egypt

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Acknowledgement

Rick StevensArgonne National Laboratory

Ross OverbeekThe Fellowship for

Interpretation of Genomes FIG

Malak KotbUniversity of North Dakota Bernhard Palsson

UC San Diego

Victor NizetUC San Diego

Pep CharusantiUC San Diego and Novo Nordisk

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Acknowledgement

Wollongong/ Queensland:Mark Walker and his lab

UCSDVictor Nizet LabJohn BuchananJason ColeAndrew HollandsBernhard Palsson lab

UTHSC Memphis• Rita Kansal• Sarah Rowe• Bill Taylor

Argonne & U Chicago• Rick Stevens• Ross Overbeek• Veronika Vonstein

Cairo University

Helmholtz Ctr.G.S. Chhatwaal

SDSURob Edwards U North Dakota

• Malak Kotb• Kotb Lab

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Main goal

• Introducing the basics of the emerging fields of

systems biology and genomics using examples

from my research on microbial pathogens in the past

15 years

2 Feb 2016

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Outline• Introduction

– The re-emerging danger of infectious diseases– What are systems biology and genomics?

• Pathogen #1: Group A Streptococcus– Virulence gene discovery– The quest for vaccine targets

• Pathogen #2: Streptococcus iniae– Reconstructing the virulome of S. iniae

• Pathogen #3: Shiga toxin-producing EHEC– Predicting novel drug targets against pathogenic E. coli

• Conclusion• Post scriptum…

2 Feb 2016

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INFECTIOUS DISEASES AGAIN?Introduction

2 Feb 2016

The killers return!

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Alarming WHO and CDC reports (2014)

2 Feb 2016

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Alarming WHO and CDC reports (2014)

2 Feb 2016

Superbugs vs. antibiotic bottleneck!

2 Feb 2016 SCITA BIOFANS 2016 Courtesy: Nina Haste, UCSD

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SYSTEMS BIOLOGY & GENOMICSIntroduction

2 Feb 2016

Defining the concepts

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Systems biology? New?

• Holistic (genome-wide)– The whole is not necessarily equal to the sum of the parts

• Unbiased/ hypothesis-free hypothesis-generating– Often times you don’t know what you’re looking for when

you start• Integrative (multi-omics)

– Integrating comprehensive data into overlaid networks

2 Feb 2016

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Systems biology? New?

2 Feb 2016

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Systems biology? New?

2 Feb 2016

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Systems biologyWeather maps:Systems, subsystems, and individual components

Significant vs. marginaleffectors vs. noise

2 Feb 2016

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Systems biology• Holistic… but including all details!

– The case of traffic maps

• Integrative (multi-omics)2 Feb 2016

http://cdn.theatlanticcities.com/img/upload/2011/12/01/20111130-road/largest.jpg

http://vector1media.com/spatialsustain/wp-content/uploads/2008/04/air-traffic.gif

Systems biology

Metabolism“traffic map”

2 Feb 2016 SCITA BIOFANS 2016

Genomics

FOOTBALLOMICS(Find it on SlidShare.net)

What can football teach us about genomics & systems biology?

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Lessons to learn from football…

2 Feb 2016

Lesson #1:

18-25 players (genes) are listed, but only 11 are "transcribed" into the pitch and those are the ones that are likely to be "expressed"…

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Lessons to learn from football…

2 Feb 2016

Lesson #2: The "game" is highly regulated:

Some players may be induced or repressed in the middle of the game

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Lessons to learn from football…

2 Feb 2016

Lesson #3: systems biology and “–omics”

Genome vs. sum of its genes/ systems vs. reductionist view:

a team's outcome is not necessarily equal to the

sum of its players efforts

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Lessons to learn from football…

2 Feb 2016

Lesson #4a: A players "function" is context dependent: player-player interactions are key to interpret the overall outcome

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Lessons to learn from football…• Lesson #4b:

– A players "function" is context dependent. A protein expressed in another bacterial host may behave very differently.

Salah with Chelsea vs. Salah with Fiorentina & now Rome

2 Feb 2016

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APPLICATION ON 3 PATHOGENS

2 Feb 2016

Applying systems biology to better understand microbial pathogenesis and neutralize it when necessary

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GROUP A STREPTOCOCCUSPathogen #1

2 Feb 2016

Integration of multi-omic data to understand microbial genotypic and phenotypic variation

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Background: invasive GAS infections• Invasive group A streptococcal (GAS) infections have

reemerged in the 1980s

Sore throat

ImpetigoE

rysi

pela

sNecrotizing Fasciitis

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Experimental model

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Experimental model

Parent in vitro Animal-Passaged5-14 days

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Proteomics of phenotypic variants

2 Feb 2016

4 5 6 7 8 9 10pI

105

75

50

35

30

25

15

MWt(kDa)

AP (+protease inhibitor)

Aziz et al., 2004. Mol Microbiol, 51(1):123

WT (+protease inhibitor)

4 5 6 7 8 9 10pI

105

75

50

35

30

25

15

MWt(kDa)

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Lessons to learn from football…

2 Feb 2016

Lesson #2 (remember): The "game" is highly regulated: Most players are “differentially expressed” at different time points…

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Discovery of Streptodornase1 (Sda1)

2 Feb 2016

105

75

50

35

30

25

4 5 6 7 8 9pI

MWt(kDa)

SpeB

SICGAPDH

MF3

MF/SpeF

SibA

AmyASLO

Ska

M

Sda

NADGH

B5

CAMP

Spy0136SpeA

EndoS

Tr: Trypsin peaks.S: StreptodornaseD peaks.

Tr

S

SS

S

S

S

Tr

S

S

S

C IEF-Gel Overlay

MALDI-TOF MS

Aziz et al., 2004. Mol Microbiol, 54(1):184

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Discovery of Streptodornase1 (Sda1)• At UCSD, Buchanan and Nizet et al. knocked out the sda1 gene, and

found out that it helps releasing the bacteria from NETs.

rSda1-329 rSda1-3900

20

40

60

80

100

Recombinant Proteins

% R

educ

tion

in D

NA

conc

entr

atio

n

rSda1-329

rSda1-390

Aziz et al., 2004. Mol. Microbiol., 54(1):184 Buchanan et al., 2006, Current Biology 16 (4): 396

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The perfect storm

http://stke.sciencemag.org/content/vol2007/issue379/cover.dtl

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Emergence of flesh-eating streptococci

M1T1

Aziz & Kotb. Emerg Infect Dis. 2008 Oct;14(10):1511-7

M1 SF370

2 Feb 2016

M1T1Chromosome

M1T1.Z(Sda1)

M1T1.Y(SpeA)

M1T1.X

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A model for virulence gene exchange

hylP hol lys tox prx attR

hylP’ Lys’ tox’ prx’ attR’hol’

Phage 1

Phage 2

2 Feb 2016

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Group A hyaluronic acid capsule

2 Feb 2016

J Bacteriol. 2012 Nov;194(22):6154-61

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Group A carbohydrate (Lancefield) antigen

2 Feb 2016

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Conclusion: GAS genomics• Genomics + integration of multiple data (genomic,

transcriptomic, proteomic, immuno-proteomic, and phenotypic) allowed us to:– discover the genes responsible for some of classical virulence

factors (streptodornase, carbohydrate antigen, and capsule)– identify prophages that largely determine the differences

between different streptococcal strains. Those prophages encode toxins (e.g., streptodornases and superantigens), which play a major role in virulence.

• “Genome mining” provides a gold trove for:– novel drug discovery: through targeting virulence genes– reverse vaccinology: through finding novel vaccine targets from

sequence data

2 Feb 2016

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Lessons to learn from football…• Lesson #5:

– Newly acquired players have an initial advantage, but “defense/immunity” starts building up herd immunity

M. Salah with Fiorentina stopped scoring after a while!

2 Feb 2016

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Humans; bacteria; phages; mobile toxins

2 Feb 2016

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STREPTOCOCCUS INIAEPathogen #2

2 Feb 2016

Reconstructing pathogenesis from the genome/ Fishing for virulence genes in the fish pathogen Streptococcus iniae

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Streptococcus iniae, 5 years ago…

2 Feb 2016

Possible routes of S. iniae infection:Nares/ Gills/ G.I.T.

Signs/ Symptoms:Lethargy, anorexia, loss of orientation, ulcers, exopthalmia or “popeye”, organ damage and meningoencephalitis

S. iniae threatens the worldwide aquaculture industry (loss: $200 M/year) and poses an emerging risk to humans who handle raw fish (25 invasive human cases until 2007).

Courtesy: Carlo Milani

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Reconstruction…

2 Feb 2016

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Can we reconstruct virulence?

2 Feb 2016

Genome Dynamics (Krager). 2009;6:21-34.

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Genome annotation and reconstruction

Credit: V. Fischetti

2 Feb 2016

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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but

interpretation of genomes is the bottleneck• The SEED database

http://www.theseed.org• RAST: rapid annotation using subsystems

technology

2 Feb 2016

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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but

interpretation of genomes is the bottleneck• The SEED database

http://www.theseed.org• RAST: rapid annotation using subsystems

technology

2 Feb 2016

April 2014

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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but

interpretation of genomes is the bottleneck• The SEED database

http://www.theseed.org• RAST: rapid annotation using subsystems

technology

2 Feb 2016

October 2014

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Automated (metabolic) reconstruction

2 Feb 2016

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Automated (metabolic) reconstruction

2 Feb 2016

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How to reconstruct the “virulome”?• Bottom-up approach

(reconstruction)– Reverse genetics– Start from the genome

• Tools used:– RAST subsystems

analysis: 60 candidate virulence genes

– Text mining BLAST results

– In silico hybridization

• Top-down approach (genetics)– Forward genetics– Start from phenotypes

• Methodology:– Transposon mutagenesis

followed by screening mutants in a hybrid striped bass model

2 Feb 2016

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Reconstructed “virulome”

2 Feb 2016

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Verified virulence factors• M-like proteins and C5a peptidase

2 Feb 2016

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Verified virulence factors• Polysaccharide deacetylase (Pdi): a potential

peptidoglycan deacetylase

Lysozyme resistance

2 Feb 2016

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Lessons to learn from football…

2 Feb 2016

Lesson #6a: Not all players are equally dangerousTargeting key players =

• Virulence gene inhibition

• Reverse vaccinology

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SHIGA TOXIN-PRODUCING E. COLIPathogen #3

2 Feb 2016

Using genome-wide metabolic reconstruction for drug target prediction

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E. coli O157:H7• Enterohemorrhagic

(hemorrhagic colitis or HUS)

• Shiga toxins-encoding

• Food-associated outbreaks

• Ancestor of the Jack-in-the-Boxstrain (1993 outbreak in Western USA)

• Record number of prophages (25 prophages in Sakai)

2 Feb 2016

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E. coli O157:H7 genome• Full sequence to allow high-resolution analysis of

single mutations

2 Feb 2016

Genome Announc. 2014 Aug 14;2(4).

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E. coli O157:H7 genome• Full sequence to allow high-resolution analysis of

single mutations

2 Feb 2016

Genome Announc. 2014 Aug 14;2(4).

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Computational modeling of 55 E. coli genomes

2 Feb 2016

Monk et al. PNAS 2013

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Lessons to learn from football…

2 Feb 2016

Lesson #1: (remember)

18-25 players (genes) are listed, but only 11 are "transcribed" into the pitch and those are the ones that are likely to be "expressed"…

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Lessons to learn from football…• Lesson #6b:

Not all players are equally dangerous and not all teams can survive without “essential” players

2 Feb 2016

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In silico analysis of gene essentiality1) Delete genes (pathways) in silico

2) Compute whether growth is possible on multiple substrates

3) Perform experiments to validate4) Inconsistencies indicate knowledge gaps

2 Feb 2016 Courtesy: Pep Charusanti

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In silico analysis of gene essentiality1) Delete genes (pathways) in silico

2) Compute whether growth is possible on multiple substrates

3) Perform experiments to validate4) Inconsistencies indicate knowledge gaps

2 Feb 2016 Courtesy: Pep Charusanti

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Methodology: precise gene deletion

One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Datsenko KA, Wanner BL. PNAS 2000 Jun 6;97(12):6640-5.

2 Feb 2016

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Analysis of synthetic lethality

• In silicodeletegene pairs (same strategy)

2 Feb 2016 Courtesy: Pep Charusanti

ISC-FOPCU 2015

Applying the workflow

25 April 2015

Aziz et al. Sci Rep. 2015

ISC-FOPCU 2015

Applying the workflow

25 April 2015

Aziz et al. Sci Rep. 2015

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Results…

2 Feb 2016

List 197 genes75 pairs

List 262 genes52 pairs

List 336 genes31 pairs

Aziz et al. Sci Rep. 2015

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Results…

2 Feb 2016

Aziz et al. Sci Rep. 2015

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WITH SYSTEMS BIOLOGY, WE CAN:

General Conclusion

2 Feb 2016

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Using systems biology, we …• discovered novel virulence factors in well-studied

organisms (e.g., sda1, gac, hasB’1)

• understood the molecular basis for genotypic variations and phenotypic states of an organism (M1T1 GAS)

• predicted the virulence potential of a previously unsequenced, poorly studied organism (S. iniae)

• predicted drug and vaccine targets and are testing the predictions

2 Feb 2016

Post Scriptum:The Human Microbiome vs.

Pharmacotherapy

Ramy Karam Aziz, PhDDepartment of Microbiology and ImmunologyFaculty of Pharmacy, Cairo University, Egypt

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THE HUMAN MICROBIOME AND DRUGS

2 Feb 2016

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The human microbiome The human

microbiome is the summation of microorganisms that reside on the surface and in deep layers of skin, in the saliva and oral mucosa, in the conjunctiva, and in the gastrointestinal and urogenital tracts.

2 Feb 2016

The Economist. Aug 2012

1013 ~ 1014

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PharmacoMicrobiomics: Human microbiome vs. Drugs

2 Feb 2016

Mariam Rizkallah

Rama Saad

The PharmacoMicrobiomics Database

2 Feb 2016 SCITA BIOFANS 2016

http://pharmacomicrobiomics.org

Rizkallah et al. CPPM Med 2012

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Your questions?

Nucleic Acids Res. 2010 Jul;38(13): Cover