De la epidemiologia molecular a la epidemiologia

genomica: Una nueva era para la investigacion de

enfermedades infecciosas en un contexto global.

V JORNADA SOBRE VIGILANCIA DE LA SALUD PUBLICA

INTEGRACION DE NUEVOS CONOCIMIENTOS Y

EXPERIENCIAS EN LA VIGILANCIA DE LA SALUD PUBLICA

Departament of Biology and Biochemistry

Universidad de Bath, Reino Unido

The Future of PulseNet: Faster, cheaper, and better foodborne disease detectionA new testCulture-independent diagnostic tests, or CIDTs, are new tests that can

detect the DNA of bacteria directly from patient samples like stool. As

their name implies, CIDTs do not need a culture (cells grown in a lab) to

identify the bacteria that caused a patient’s illness. Clinical laboratories

are increasing their use of CIDTs because they save costs and diagnose

illnesses faster.

The challenge to PulseNetFor PulseNet to work, scientists need genetic information drawn from

cultures. Because CIDTs skip the step of producing a culture, these

newer tests don’t produce DNA fingerprints to store in PulseNet’s

database. Without these data, public health scientists will not be able

to find, monitor, and prevent foodborne disease outbreaks or follow

trends to know if prevention policies are working. Many outbreaks

will go undetected or be detected later when the outbreak has grown,

and contaminated products will remain on the market longer .

Harmonizing the systemCDC is working closely with public health officials, diagnostic

laboratories, CIDT kit manufacturers, and clinicians to ensure that

PulseNet collects the DNA fingerprints it needs. PulseNet is also

developing a new way to detect outbreaks that doesn’t depend on

cultures. These innovations are in the early stages of research and

development, but we are heading toward a system that answers both

needs: rapid and less costly diagnosis for individuals and a way to

ensure essential data are still collected.

Transforming disease detection In 2013, CDC began using whole-genome sequencing (WGS)

to detect outbreaks caused by Listeria, the most deadly

foodborne pathogen. WGS reveals all the genetic material,

or the genome, of an organism (like bacteria and viruses) in

one efficient process. The Listeria project was one of the first

to receive support from CDC’s Advanced Molecular Detection

(AMD) initiative, a program that uses newer, more powerful,

pathogen detection technologies to find outbreaks sooner.

CDC is partnering with other federal agencies and state and

local health departments to analyze Listeria from human cases

and possible food sources.

Since the Listeria project began, scientists have detected

more clusters, solved outbreaks faster with fewer cases,

linked cases to likely food sources, and identified Listeria in

unexpected foods, such as caramel apples and ice cream. CDC

is quickly expanding the use of WGS in state laboratories and

has begun using WGS for investigations of other foodborne

pathogens such as Campylobacter, E. coli, and Salmonella.

WGS has been a game changer for outbreak detection,

spurring innovation and new discoveries. With WGS and

PulseNet working in tandem, we have taken a significant

leap forward in making food safer to eat.

CS263572A

100

90

80

70

60

50

40

30

20

10

0

Clusters detected

LISTERIA

Clusters detected

sooner or only by WGS

1419

21

6 60

Outbreaks solved

14

9

Cases linked to

food source

6

16

93

Num

ber of clu

sters

Detecting Listeria: Before and After WGS*

* Whole genome sequencing

Pre-WGS (Sept 2012 – Aug 2013)

WGS Year 1 (Sept 2013 – Aug 2014)

WGS Year 2 (Sept 2014 – Aug 2015)

years ofyears ofTM

Number of EU/EEA countries applying (green bar) or planning within three

years (hatched green bar) to apply WGS-based typing for (A) surveillance

application and (B) outbreak investigations, by pathogen target

ECDC roadmap for integration of molecular and genomic typing into European-level surveillance and epidemic preparedness

ECDC TECHNICAL REPORT

ECDC roadmap for integration of molecular and genomic typing into European-level surveillance and epidemic preparedness – Version 2.1, 2016–19

10Didelot et al. (2011) Recombination and Population Structure in Salmonella enterica. PLoS Genet 7(7)

Salmonella Typhimurium LT2 genome

microarray

genomes

MLST scheme

Técnicas clásicas

de typado

Genomas

completos

Métodos Tradicionales vs Secuenciación Genómica

Flujo de trabajo para el análisis de datos genómicos de organimos patógenos

WHY DO WE NEED TYPING?

Related isolates

Share the same strain

Unrelated isolates

Have a different strain

OUTBREAK STRAIN 1

STRAIN 2

STRAIN 3

STRAIN 4

Identification of epidemiologically linked isolates

WHY DO WE NEED TYPING?

Related isolates

Share the same strain

Unrelated isolates

Have a different strain

OUTBREAK STRAIN 1

STRAIN 2

STRAIN 3

STRAIN 4

Identification of epidemiologically linked isolates

OUTBREAK STRAIN 1 OUTBREAK STRAIN 2

Ancestro común???

Como varía el genoma de una bacteria????

Transducción

Fagos

Transformación

DNA ambienteConjugación

Plásmidos

Core genome

• Mutaciones

• Recombinación

Genoma accesorio

• Genes transmitidos

lateralmente

Filogenia

Adaptación local

Resistencia AB

Virulencia

Genoma Población Demografía

Biogeografía Evolución Colonización

STRAIN 1 STRAIN 2

Ancestro común

Cuándo?

Dónde?

Fecha de emergencia de clones y variantes

En que lugar se origináron y vias de dispersión

Introducción? Vía de entrada del patógeno

Adaptado? Posibilidades de llegar a ser endémico

Biological ‘corridors’ of disease?

Environm

enta

l

str

ain

Clin

ical str

ain

2) Genome sequencing and SNP identification

3) Comparative sequence analysis and phylogenetic

determination

1) Isolation of (•) strains

List of software

• TRIMMINGtrimommatichttp://www.usadellab.org/cms/?page=trimmomatic

• MAPPINGBWAhttp://bio-bwa.sourceforge.net

• SNP CALLINGSAMtools (includes bcftools)http://samtools.sourceforge.netFreeBayeshttps://github.com/ekg/freebayesSnpEffhttp://snpeff.sourceforge.net/GATKhttps://www.broadinstitute.org/gatk/downloadArtemis + ACT + DNAPlotterhttp://www.sanger.ac.uk/resources/software/artemis/http://www.sanger.ac.uk/resources/software/act/#downloadshttp://www.sanger.ac.uk/resources/software/dnaplotter/

• Filtering SNPVCFToolshttp://vcftools.sourceforge.net/

• Visualization IGVhttp://www.broadinstitute.org/igv/BamViewhttp://bamview.sourceforge.net/CIRCOShttp://circos.ca/software/download/CGViewhttp://www.bioinformatics.org/cgview/

• PHYLOGENYPhyMLhttp://code.google.com/p/phyml/RAxMLhttp://sco.h-its.org/exelixis/web/software/raxml/index.htmlPUmPERhttps://github.com/fizquierdo/perpetually-updated-treesMrBayeshttp://mrbayes.sourceforge.net/download.php

• Molecular clockBEASThttp://beast.bio.ed.ac.uk/home#toc-2

• Population structureStructure (fine Structure)http://www.maths.bris.ac.uk/~madjl/finestructure/finestructure_info.html

• ClonalOrigenMauvehttp://gel.ahabs.wisc.edu/mauve/Clonalframehttp://www.xavierdidelot.xtreemhost.com/clonalframe.htm

Raw sequences

Process sequences (QA, adapter removal)(Trimmomatic)

Genome assembly(A5 pipeline)

Genome alignment(Harvest tools)

Molecular clock analysis(Beast)

Recombination analysis(ClonalframeML)

Core SNPsCore genome

Phylogenetic analysis(RAxML)

Remove recombinant regions(cutseq)

Remove gaps(Trimal)

Extract SNPs in coding regions

(SnpEff)

….. más alla de secuenciación y bioinfomática

Significant global rates of movement identified by BSSVS.The color of the connections represent the relative strength by which the rates among these two locations are supported: white = weak, magenta = strong.

Richard Neher (University of Basel) Trevor

Bedford (Fred Hutchinson Cancer Research Center, Seattle)

Evolución y Diseminación Transcontinental

de Salmonella Infantis ST32

Salmonella

Infections associated with Salmonella Infantis, Peru

Strain Year ST Source AM C SXT CIP CTX NA N CAZ TE AMC

SRR3931661 20/4/2010 32 C S S S S S R R S R

SRR3931669 25/5/2011 32 C S S R S S R R S S

SRR3931684 8/3/2010 32 E S S R S S R R S R

SRR3931695 8/3/2010 32 E S S R S S R R S R

SRR3931858 3/1/2013 32 C R S R S S R R S R S

SRR3931714 23/4/2012 32 C R R R S R R R R R R

SRR3931679 20/2/2012 32 E S S R S S R R S R S

SRR3931664 7/5/2011 32 C S S R S S R S R S

SRR3931707 4/6/2011 32 C R S S R R R R R S

SRR3931689 12/9/2012 32 C R R R S R R R R R S

SRR3931677 1/10/2012 32 E R R R S R R R R R R

SRR3931690 10/4/2013 32 C R R R S R R R S R S

SRR3931691 10/7/2013 32 C R R R S R R R R R S

SRR3931681 29/8/2013 32 C R R R S R R R S R S

SRR3931680 27/8/2013 32 C R R R S R R R S R S

SRR3931692 10/8/2013 32 C R R R S R R R S R S

SRR3931682 27/8/2013 32 C R R R S R R R S R S

SRR3931685 10/5/2013 32 C R R R S R R R R R S

SRR3931683 10/12/2012 32 C R R R S R R R R R S

SRR3931678 8/11/2012 32 C R R R S R R R R R R

SRR3931687 19/12/2012 32 E R R R S R R R R S

SRR3931688 19/12/2012 32 E R R R S R R R R R S

SRR3931676 4/6/2012 32 E R R R S R R R R R S

SRR3931693 26/7/2014 32 C R R R S R R R R R S

SRR3931694 10/4/2014 32 C R R R S R R R R R S

SRR3931686 23/5/2013 32 C R R R S R R R R R S

ANTIBIOTICS

AM: ampicillin, C: chloroamphenicol, SXT: trimethoprim/sulfamethoxazole, CIP: ciprofloxacin, CTX: cefotaxime, NA: nalidixicacid, N: neomycin, CAZ: ceftazidime, TE: tetracycline, AMC: amoxicillin & clavulanic acid. Strains: blue is clade 1, green isclade 2 and pink clade 3 as per the phylogenetic trees.

Antibiotic resistance profile for all Peru genomes

• All Peru strains were resistant to: nalidixic acid, neomycin and tetracycline.

• All of clade 3 was resistant to: ampicillin, chloramphenicol, cefotaxime and trimethoprim/sulfamethoxazole

• SRR3931714, SRR3931677 and SRR3931678 were resistant to: all antibiotics except ciprofloxacin which was the only antibiotic all strains were sensitive to.

Phylogeny

Pangenome analysis

Pangenome analysis

CVM44454_2014-05-09_USA-MA_clinical --> 316160 bpFSIS1502169 2015 USA-NC young chicken --> 323122 bpFSIS1502916_2015_USA-NJ_comminuted_chicken --> 322518 bpN55391_2014_USA-TN_chicken_breast --> 316814 bpSRR3931690_2013_Peru_clinical <-- 323100 bp (in silico)pC271 E. coli aislada en Bolivia el 2011 <-- 125315 bp

pC271

Plasmid from an E. coli isolated in Bolivia in 2011 (PMID: 26100713)

Resistance-associated genes:-CTX-M-65 Betalactamase-fosA3 fosfomycin resistance protein - Not present in S. Infantis-aaC(3)IV aminoglycoside N(3')-acetyltransferase -aph(4) amynoglycoside phosphotransferase -floR florfenicol/chloramphenicol export protein -Tem-1b class A beta-lactamase - Not present in S. Infantis

Evolution of the megaplasmid in Salmonella Infantis

Evolution of the (mega)plasmid in Salmonella Infantis

pFSIS1502169 (323122 bp, 296 CDS)

(125315 bp, 170 CDS)

• Both plasmids have 99% of sequence similirarity

• Share 116597/125315 bp (93,04%).

• 14 CDS exclusive in pC271 and 6 CDS -in the shared region- only pFSIS1502169.

Evolution of the (mega)plasmid in Salmonella Infantis

Mega-plasmid S. Infantis (pFSIS1502169):

• 323122 bp with 296 CDS

• 52 are transposases y with 22 resistance genes:

arsenical resistance protein ArsHTetA family tetracycline resistance MFS efflux pump mercuric resistance transcriptional repressor protein MerDmercury resistance protein dicarboxylate transporter/tellurite-resistance protein TehAclass A beta-lactamase AAC(3) family aminoglycoside 3-N-acetyltransferase protein ImpB - UV resistance mercuric transport protein periplasmic component mercury transport protein MerC CDS polyketide synthase CDS non-ribosomal peptide synthetase CDS APH(3') family aminoglycoside O-phosphotransferase CDS trimethoprim-resistant dihydrofolate reductase DfrA1 CDS methyltransferase molybdopterin-guanine dinucleotide biosynthesis protein MobC CDS chloramphenicol efflux MFS transporter CDS protein-disulfide isomerase streptomycin kinase CDS ANT(3'') family aminoglycoside nucleotidyltransferase CDS QacE family quaternary ammonium compound efflux SMR transporter CDS dihydropteroate synthase CDS

pFSIS1502169

Empty boxes indicate absence of the gene, filled boxes indicate presence of the gene, and empty space indicates more than 1 Blast hit. Almost all strains contain tet(A), sul1, dfrA14 and aadA1. The first big clade at the top seems to have more AR genes than the rest. All strains contain at least 3 AR genes, with a maximum of 9 resistance genes.

Antibiotic resistance genes of 162 Peru and USA S. Infantis strains

SRR3931708

SRR3931680

SRR3931779

SR

R3

93

16

94

SRR1916101

DRR022744

SRR3931819

DRR022767

SR

R181

08

72

SR

R39

31

69

7

SRR3931790

DRR02

2720

SR

R393168

9

DRR022736

SRR3931665

SR

R3

93

16

75

DR

R0

22

72

2

ER

R1

01

411

7

DR

R02

273

5

SR

R3

93

17

20

SR

R3

931

69

6

SRR3931667

SRR3931695

SRR3931668

SRR4095252

SR

R1916122

SRR1157945

DR

R02

27

51

DR

R0

22

739

DR

R02

2741

N553

91

GC

F 001931575

DRR022

718

SR

R3931670

SR

R1812857

SRR3931692

DRR022763

DRR022719

SR

R18

10851

DR

R0

22

738

SR

R3

931

693

DR

R022752

DRR022765

SR

R1

81

28

15

DRR

022725

DRR022780

SR

R3931719

DR

R0

22

76

9

ER

R1

01

41

08

SRR4095249

SRR3931858

SR

R393

1676

SRR39

3173

7

ER

R1

01

411

4

SR

R3931707

DRR022737

SR

R1916126

ER

R1

01

411

6

SR

R3931711

GCA 000812595

SRR3931669

SR

R3

93

172

1

SRR3931684

SRR3931713

SR

R1810850

SR

R1812878

SRR4095253

DR

R0

22773

SR

R3

93

169

8

SR

R19

16110

DRR022781

SR

R393

1710

DRR022779

SRR3931681

DR

R022

726

SRR4095254

SR

R3931685

SRR3931662

DRR022723

SR

R19

16

111

SRR3931666

SR

R3

93

17

02

ERR1014109

SRR3931682

DRR022759

SR

R1745619

SR

R393

1773

DR

R022754

SR

R19

16109

SRR3931814

SRR1745554

SR

R1

745538

SRR3453168

SR

R3931664

DRR022745

SRR

3931885

DRR022740

SR

R39

3168

8

SR

R1

81

281

4

DR

R022749

SRR1812829

SR

R39

31

722

SRR4095255

DRR022761

DRR022770

GCA 001518495

SRR1916087

SRR3931843

GCF 0

01931

615

SRR3931837

SRR1544363

SRR3931717

SRR1664279

DR

R022766

SRR3931848

SR

R39

3169

0

SR

R39

3169

1

ER

R1014119

SR

R3

93

16

99

SR

R39

3171

2

SR

R3

02

73

30

SRR3931873

SRR3931673

SR

R39

316

78

SR

R3931683

SR

R39

316

86

DR

R02

2772

SRR3931674

SR

R3

93

17

55

DRR022753

ER

R1

01

411

5

SR

R3

93

17

01

DRR022750

DRR022758

SRR3931854

DRR

0227

74

SRR1916080

SRR3931703

SR

R3931715

SR

R3931677

SRR3931663

SR

R39

316

87

SRR1916073

SRR3931784

DRR022762

SR

R3

931749

SR

R3

931

71

8

SRR1258567

GCA 000506925

SRR3931825

DR

R02

2742

SR

R19

16072

ERR1014118

SRR

3931714

SR

R1

91

60

99

SR

R3931706

SR

R3

93

17

16

SRR3931808

SRR3931705

SRR3931704

DRR022760

ER

R1

01

411

2

DRR022724

ER

R1

01

411

3

SR

R3

93

17

44

SRR3931767

SR

R393

1709

SRR3931831

DR

R022748

SR

R3

93

17

61

DRR022743

SR

R1812796

SRR4095251

DR

R02

2775

SR

R393

167

9

aac(3

)-IId

aac(

3)-

IVa

aadA

1

aadA

22

aph(3

')-I

c

aph

(4)-

Ia

bla

CT

X-M

-1

bla

CT

X-M

-15

bla

CT

X-M

-65

bla

TE

M-1

16

bla

TE

M-1

A

bla

TE

M-1

B

cm

lA1

dfr

A1

dfr

A14

dfr

A8

floR

fosA

lnu(B

)

mef(

B)

mp

h(A

)

oqxB

QnrS

1

sul1

sul3

tet(

A)

Colored ranges

New plasmid version

Countries

Egypt

Hungary

India

Israel

Italy

Japan

Peru

Trukey

USA

Tree scale: 0.00001

Resistance genes (ResFinder database) with the lineage where the new version of the plasmid (with the presence of the CTX-M-65 gene).

According the BEAST results, the first evidence of the presence of this plasmid was in a strain from Italy in 2007

Molecular Clock

Phylogeography of S. Infantis ST32 1999 to 2016

Dr. Narjol Gonzalez-Escalona

Dr. Joaquin Triñanes

Dr. Ronnie Gavilán

Rebecca Girton

Michel Abanto

Dr Cristóbal ChaidezDr Juan Ramón Ibarra