Nicholas. R. Thomson

Wellcome Trust Sanger Institute

Pathogen Faculty Group leader

&

London School of Hygiene & Tropical Medicine

Chair of Bacterial genomics and Evolution

Heralding in a new era of bacterial genomics

Innovation Meeting: Human Bartonellosis or Carrion´s Disease in the XXI century: Challenges and Research needs for control

models and vaccines

8-12 June, 2015

What does a bacterial genome look like Part II Comparative Genomics?

Region of Synteny

Blast output

Genome 1

Genome 2

Example of an insertion or deletion Region of Synteny

Genome 1

Genome 2

Blast output

Staphylococcus aureus

• Widespread Gram +ve bacteria – Natural flora of the skin – ~40% carriage

• Versatile pathogen associated with

a wide range of diseases – Minor wound infections – Food poisoning – Toxic shock syndrome – Endocarditis – Haemolytic pneumonia

• Complex pathology

Image kindly provided by Sharon Peacock, Oxford University

Capitalizing on a pathogen’s genome

• Complete gene complement

– Unravel the mechanisms of disease

• Highlight new virulence factors

• Reconstruct metabolism

– Identify new targets for antimicrobial therapies

• Rational drug design

• Identification of potential vaccine targets

MRSA252

S. aureus comparative genomics

SCC element

Genomic island

Pathogenicity island

Integrated plasmid

Prophage

Transposon

Conjugative element

0 Mb 1.0 Mb 2.0 Mb 3.0 Mb

Mu50

N315

MRSA252

MW2

MSSA476

COL

USA300_TCH1516

NCTC8325

RF122

USA300_FCH3757

Newman

Mu3

JH1

JH9

Core and accessory genomes

• Core genome - Stable – House keeping genes

• Central metabolism • Transporters • Cell division • Replication and information transfer

– Regulators – Surface proteins – Virulence factors

• Accessory genome - Variable

– Miscellaneous metabolism – Virulence factors – Surface proteins – Horizontal transfer functions – Drug resistance

• Gram -ve bacterium – ingestion of contaminated food or

water – carrier state Mary Mallon a.k.a Typhoid

Mary (1907)

Typhoid: • Febrile illness • Complications:

– Intestinal hemorrhage – Intestinal perforation – Encephalitis – Dehydration

• Estimated 21.7 million illnesses and 217,000 deaths (2000)

Salmonella enterica subsp. enterica, serovar Typhi

Salmonella vs. Escherichia coli - core and accessory

genomes

DNA

matches

S. Typhi

E. coli

Looking for genes and understanding function

Salmonella vs. E. coli - core and accessory genomes

DNA

matches

S. Typhi

SPI-6 SPI-1 SPI-7 ST15

prophage PAI

CP4-6 Qin Rac CP4-44

E. coli

Salmonella pathogenicity Islands SPI

G+C

tRNA

phage/IS genes

Pseudogenes

virulence

Table 2 Salmonella Pathogenicity Islands (SPI)

Nomenclature Description size &

features

Original Reference

SPI-1 T3SS, iron uptake 40 kb (Hansen-Wester and Hensel 2001)

SPI-2 T3SS 40 kb,

tRNA-valV

(Hensel 2000)

SPI-3 mgtCB Mg2+

transport 17 kb, tRNA-sel C

(Blanc-Potard et al. 1999)

SPI-4 type I secretion and large

repetitive protein

23 kb (Parkhill et al. 2001a; Wong et al.

1998)

SPI-5 T3SS effectors 8 kb, tRNA-

ser T

(Wood et al. 1998)

SPI-6 saf tcs fimbrial systems 59 kb,

tRNA-aspV

(Parkhill et al. 2001a)

SPI-7 Vi antigen and the SopE

prophage

134 kb,

tRNA-pheU

(Parkhill et al. 2001a)

SPI-8 bacteriocin immunity

protein

6.8 kb,

tRNA-pheV

(Parkhill et al. 2001a)

SPI-9 type I secretion and large

repetitive protein

16kb (Parkhill et al. 2001a)

SPI-10 prophage ST46, sef fimbrial operon

33 kb,

tRNA- l euX

(Parkhill et al. 2001a)

SPI-11 T3SS effectors and

PhoPQ-activated proteins

14 kb (Chiu et al. 2005)

SPI-12 msgA & narP 6.3 kb (Chiu et al. 2005)

SPI-13 required for survival in

chicken macrophages

~7 kb,

tRNA-pheV

(Shah et al. 2005)

SPI-14 unknown ~11 kb (Shah et al. 2005)

SPI-15 Proteins of unknown function

6.5kb tRNA-gl y

(Vernikos and Parkhill 2006)

SPI-16 bacteriophage remnants

and LPS modification

genes

4.5kb tRNA-

ar g

(Vernikos and Parkhill 2006)

SPI-17 bacteriophage remnants

and LPS modification

genes

5.1kb tRNA-

ar g

(Vernikos and Parkhill 2006)

SGI-1* Multiple antibiotic

resistance genes

43 kb (Boyd et al. 2001)

HPI iron uptake in Yersinia ND (Oelschlaeger et al. 2003)

* S. Typhimurium DT104 only

ND – not determined

Salmonella Pathogenicity Islands

-lactamase

aminoglycoside

sulphonamide

trimethoprim

streptomycin

chloramphenicol

quaternary ammonium

Epidemiological Tools

Epidemiological Tracking of bacterial pathogens

Epidemiology is the study of patterns of health and illness and associated factors at the population level. It helps inform evidence-based medicine, identifying risk factors for disease and informing optimal treatment Epidemiology relies on a number of scientific disciplines: Biology (to understand disease processes), Biostatistics (to understand raw information available), Geographic Information (map disease patterns) Social science disciplines (to identify risk factors).

Universally applied •Phage Typing •PFGE

Random amplification •RAPD

Widely Used Techniques for Epidemiological Tracking

Repeat Based •VNTR •MLVA

Optical Mapping Sequencing

•MLST

Hybridisation •Microarray

Whole genome sequence

BB TAGCAGCGCAGCCCTCCAACGCGCCATCCCCGTCCGGCCGGCACCATCCCGCATACGTGT

BP TAGCAGCGCAGCCCTCCAACGCGCCATCCCCGTCCGGCCGGCACCATCCCGCATACGTGT

BPP TAGCAGCGCAGCCCTCCAACGCGCCATCCCCGTCCGGCCGGCACCATCCCGCATACGTGT

************************************************************

BB TGGCAACCGCCAACGCGTATGCGCGCGGATGCG----------TCGCACAAAGCCCTCGA

BP TGGCAACCGCCAACGCGCATGCGTGCAGATTCG----------TCGTACAAAACCCTCGA

BPP TGGCAACCGCCAACGCGTATGCGCGCGGATGCGCGCGGATGCGTCGCACAAAGCCCTCAA

***************** ***** ** *** ** *** ***** ***** *

BvgA half-site

BB TTCTTCCGCACATCCCGCTACTGCAATCCAACACGGCGCGAACGCTCCTTCGGCGCAAAG

BP TTCTTCCGTACATCCCGCTACTGCAATCCAACACGGCATGAACGCTCCTTCGGCGCAAAG -75

BPP TCCTTCCGCACATCCCGCTACTGTAATCCAACACGGCGCAAACGCCCCTTCGGCGCAAAG

* ****** ************** ************* ***** **************

BvgA half-site

BB TCGCACGATGGTACCGGTCGCCGTCCAGACTGTGCCGACCCCCCTGCCATGGTGTGATCC

BP TCGCGCGATGGTACCGGTCACCGTCCGGACCGTGCTGACCCCCCTGCCATGGTGTGATCC -15

BPP TCGCACGATGGTACCGGTCGCCGTCCGGACCGTGCCGACCCCCCTGCCATGGTGTGATCC

**** ************** ****** *** **** ************************

-35

BB GCAAAATAGGCGCCACCGAAACGCAGAGGGGAAGACGGGATG

BP GTAAAATAGGCACCATCAAAACGCAGAGGGGAAGACGGGATG

BPP GCAAAATAGGCGCCACAGAAACGCAGAGGGGAAGACGGAATG

GCAAAATAGGCGCCTCCGAAACGCAGAGGGGAAGACGGGATG

Single Nucleotide polymorphisms SNPs

ACGT

Ancestor

gen

erat

ion

s

A->G

ACCT ACCT ACGC ACGT Population

GCGT GGGT GGGT GCGT

C->G G->C

T->C

Infe

r b

ack

SNPs can be used to draw a phylogenetic tree

Slide courtesy of Kat Holt

Vibrio cholerae phylogeny

Evolution in the source population is represented by the ‘trunk’ of the tree, which occurs continuously through time Branches on the tree represent outbreaks

Periodically strains will travel from the source population to non-endemic areas

Periodically strains will travel from the source population to non-endemic areas These strains then cause an outbreak

The outbreaks run their course, and then they die out

The next time that Cholera is seen in the non endemic area, the strain will not be a descendent of the last outbreak strain, Rather it will have emerged more recently from the source population

Wave 1

Wave 2

Wave 3

Wave 1

Wave 2

Wave 3

2005-09

1989-97

2003-07

1992-2002

1993-98

1975-86

1937-61 1966-71

1967-89

1969-73 1969-81

1981-85

1974 1986-87

1969-73

7th Pandemic Transmission of Cholera

WHO Global Task Force for Cholera Control (GTFCC)

Drug acquired resistance (1978-1984)

Wave 1

Wave 2

Wave 3

Multiple drug resistance first reported (1988)

Tetracycline (1968) shown to be effective in treating Cholera

A SNP based maximum likelihood phylogeny of the Chandigarh V. cholerae.

Abd El Ghany M, Chander J, Mutreja A, Rashid M, Hill-Cawthorne GA, et al. (2014

Acknowledgements

91

Alejandro Cravioto Dong-Wook Kim

Stephen Baker

Kat Holt

Claire Jenkins

Wanderley Dias da Silveira

Dani Cohen

Anthony Smith Karen Keddy

John Clemens Shah Faruque Kaisar Talukder

Habib Bukhari

Sam Kariuki

Jan Holmgren Michael Lebens

Francois-Xavier Weill Ingrid Filliol

Ankur Mutreja Alison Mather John Lees Julian Parkhill Gordon Dougan

Rosario Morales UNAM Gabriella Delgardo UNAM Natalie Weiller Josefina Campos

Hue

KH2

KH1

Phylogeography of Shigella in Vietnam

~10% establishment

of new local reservoirs

Holt et al., PNAS 2013

Hue

KH2

KH1

Parallel evolution in local areas

CTX-M-15

(IncI1)

CTX-M-14

(IncA/C)

Holt et al., PNAS 2013

Bacteria with fastidious growth requirements or isolated from new sources

Why? Live isolates not always available (facilitates new fields of research) • No in vitro culture • Expense prohibitive for some questions • cultural differences in acceptable sample type e.g. using vs vaginal swab

Dead or low viability archived samples can be sequenced Culture bias? e.g. c. 50-70% of C. trachomatis strains don’t culture Within host variation and minority variants – estimate bacterial load, Markers of drug resistance. Can be used to look at any sample – clinical/environmental (V. cholerae viable non culturable)

IMS-MDA

Non cultured Samples

Clinical

Other

Accurate Whole genome sequence

Urogenital (Swab/urine)

Rectal (Swab/stool)

Blood (Swab/sample)

Lung/Pharyngeal (Swab/sputum etc)

Skin/ulcer/eye (swab/scab/exudate)

Environmental (water/soil etc)

STI chlamydia gonnorhea

Polymicrobial

Diarrheal cholera

dysentery

Skin lesion/ulcer

Yaws Chancroid

Respiratory

LRTI TB

Target/Non-target DNA

RNA bait set

Library construction Multiplex tags

High quality sequence

Sample Sequence

IMS-MDA

100.0

15169_6#56

15169_6#62

15169_7#77

15169_6#66

15169_7#89

15169_6#53

15169_7#82

Tcuniculi_CP002103

TPA_DAL-1_CP003115

TPA_Chicago_CP001752

15169_6#51

15169_6#72

15169_6#57

15169_6#52

15169_7#73

15169_6#58

15169_6#71

15169_6#63

15169_6#68

TPP_CDC2_CP002375

15169_6#50

15169_7#78

15169_6#70

15169_6#59

15169_6#55

15169_7#75

15169_7#80

15169_7#81

TPP_SamoaD_CP002374

TPA_Sea81-4_CP003679

TPE_BosniaA_CP007548

15169_7#74

15169_6#69

15169_6#65

15169_6#49

15169_6#67

15169_6#60

15169_6#54

15169_7#79

15169_6#64

15169_6#61

TPP_Gauthier_CP002376

TPA_MexicoA_CP003064

TPA_Nichols_AE000520

15169_7#92

TPA_SS14_CP000805

15169_7#91

T.p. pallidum (syphilis)

10 chancre Yaws & Syphilis

0.1-0.2% diversity between T. pallidum subspecies (Yaws vs Syphilis)

T.p. pertenue (yaws)

rabbit ‘syphilis’

Sequencing non-cultivable Treponemes now possible

• Treponema subspecies cause Bejel, Yaws and Syphilis • 12 million new cases of syphilis annually (Mother to child) • 3 million infections Yaws and bejel • WHO Yaws eradication campaign underway

but confusion of Yaws vs chancroid lesions Goals • Establish true diversity & understand mixed infections • Identify mechanisms behind host and tissue tropism • Identify vaccine candidates

36 new genomes (in 2015)

T.p. endemicum (bejel)

Alsmark et al., 2004 PNAS

Bartonella Genomics