Sequencing viral genomes for diagnostic and

SPHINX HCV workshop – June 2012 – TUNIS

Sequencing viral genomes for diagnostic and molecular epidemiology studies of viral diseases Jean-Luc Bailly EPIE EA4843 – Epidemiology and Pathogenesis of Enterovirus Infections Université d’Auvergne – Faculté de Médecine – France National Reference Centre for Enteroviruses/Parechoviruses

Genotyping (diagnosis) and molecular epidemiology rely on similar basic viral factors Genetic diversity, viral evolution, virus transmission

BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Cholera outbreak in London in 1854 John Snow (1813 – 1858)

The Abbey-row cholera outbreak took place in September 1857 in West Ham by the eastern branch of the Lea River, north of the Isle of Dogs. The location is shown as a red box in the right side of the 1841 map. http://www.ph.ucla.edu/epi/snow.html BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

What John Snow lacked in his toolkit? Molecular typing = genotyping Technology of rapid gene sequencing: obtain molecular sequence data Methods for comparing nucleotide sequences

Science of molecular epidemiology i.e. phylogenetic epidemiology


Molecular typing

pathogens

Indirect information on the diseases Diagnostic tests Clinical features Risk factors Genotyping Molecular epidemiology Mathematical epidemiology Host demography

Pathogenesis

Transmission routes

Management of patients Monitoring disease spread Epidemiology of an infectious disease BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Molecular typing of viruses Genome sequence data Virus identification

Applications in viral infectious diseases Diagnosis Epidemiology


RNA viruses (1) RNA viruses more often associated with epidemic and emerging infectious diseases (than DNA viruses)

(2) Genes of RNA viruses evolve rapidly

Fundamental characteristic Jones et al. Nature (2008) 451:990-994. BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

RNA viruses: high evolutionary rates 10-3 to 10-4 nucleotide substitutions per site per year

HCV HIV-1

Duffy et al. (2009) Rates of evolutionary changes in viruses: patterns and determinants. Nature Rev Genet. 9:2679:267-276. 276 BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

High evolutionary rates: implications 10-3 to 10-4 nucleotide substitutions per site per year 1. Technical consequences in amplification/sequencing Choice of viral genes

Construction of primer sequences

2. Implications for diagnosis

Use specific patterns for virus genotyping Virus identification in patients

Occurrence of drug resistance

3. Implications for molecular epidemiology

Watch evolutionary changes arise on a ‘real-time’ scale BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

RNA virus evolution Epidemiological/ecological events Continuous time scale

Coincidence on a continuous time scale allows direct human observation Evolution of RNA viruses occurs at ~ the same rate as the underlying ecological and epidemiological events that shape their diversity Pybus, Rambaut. (2009) Evolutionary analysis of the dynamics of viral infectious diseases. Nature Rev Genet. 10:540. Grenfell et al. (2004) Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303:327. BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Epidemiological events / evolutionary history Transmission between two individuals Transmission to different host species (human/mosquito) Dissemination to different tissues Presence of an antiviral drug (treatment)

RNA virus evolution Epidemiological/ecological events Continuous time scale

Viral genes = biomarkers Identify viruses (genotyping) Trace past epidemiological events (phylogenetic analysis) BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Virus genotyping / management of patients

Molecular identification of hepatitis C viruses in diagnostic investigations of chronic infections


Sequencing = identify HCV with the greatest precision Family: Flaviviridae Genus: Hepacivirus Species: Hepatitis C virus Genotype: 1, 2, 3, 4, 5 ... Lowest levels in the phylogeny

Subtype: 1a, 1b, 1c ... Strain


Diversity revealed by phylogenetic analysis of nt sequences Hepatitis C virus

Diversity of genotypes/subtypes: high substitution rate transmission rate

Candidate new HCV genotype 7

7

Henquell et al. (2011) Virologie 15:28615:286-295.

Murphy et al. (2007) J Clin Microbiol 45:1102-1112.

Why do we need to determine HCV genotypes/subtypes?

Simmonds. Simmonds. (2004) J Gen Virol. Virol. 85:317385:3173-3188. BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Management of patients with a chronic infection Quantitative detection of HCV RNA HCV genotyping before the start of treatment 1. Length of treatment, ribavirin dose 2. Prognostic information on treatment outcome

HCV genes used in our laboratory 2 5NC

C

Core

3 E1

E2

Enveloppe

Genotype specific primers

p7

NS2

1 NS3

Protease / Helicase

4A

NS4B

Hydophobic, membraneassociated

NS5A

NS5B

3NC

Polymerase

Pan genotype primers BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

HCV genotyping by direct gene sequencing In serum specimens (population sequencing)

Gene amplification (RT-PCR) / one portion of the viral genome Gene sequencing with PCR primers Final identification: comparative analysis of nt sequence with reference sequences / web facilities: http://hcv.lanl.gov http://jose.med.kuleuven.be/genotypetool/html/subtypinghcv.html


http://jose.med.kuleuven.be/genotypetool/html/subtypinghcv.html Automated sequence analysis by the BLAST method


http://hcv.lanl.gov Automated sequence analysis by the BLAST method


http://hcv.lanl.gov

Genotype/subtype determined from sequences that match with highest scores BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

HIV-1 genotyping / management of patients HIV-1 resistance testing / antiretroviral drug treatment Different treatments inhibit distinct stages in virus replication: Selection of viral populations / drug resistance phenotype Gene sequencing = identifying mutations associated with drug resistance

LTR

gag

Core: Core: MA, CA, NC

pol

RT, PR, INT

Antiretroviral drug treatments for inhibiting virus replication, integration of viral genome into host DNA, maturation of viral proteins

Vif vpr tat vpu

env

Enveloppe

rev nef

LTR

Antiretroviral drug treatments for inhibiting attachment of virus particles BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Prospective virus genotyping / diseases surveillance Enterovirus / aseptic meningitis / neurological manifestations Human enteroviruses > 100 serotypes Serotypes within species HEV-B: seasonal epidemics EV-71 serotype: increased pathogenicity (encephalitis, acute flaccid paralysis) Gene sequencing:

Single stranded positive RNA genome ~7500 nt

= identify species/genotypes and clinical features = differentiation of polioviruses = surveillance of community outbreaks (e.g. hand, foot and mouth diseases) BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Virus genotyping / surveillance of respiratory diseases

Prospective diagnostic genotyping of rhinoviruses and surveillance of severe respiratory infections


Rhinoviruses = Human enteroviruses (Picornaviridae) Polioviruses

HEV-C

HEV-B

Human rhinovirus (HRV)

Branch length 0.1 substitution per site

VP1 VP2 VP3 VP4

5’ non coding region

VP4

VP2

VP3

Capsid proteins

Virus species

HEV-D

HEV-A polymerase

VP1 Positive RNA genome (length = ~ 7.5 kb)


Phylogenetic pattern of human rhinoviruses (HRV) H RV-C7 1 32 HR 7 5977 V-C2 Q8523 D EU7 EU081807 HRV-C EU75 2381 HRpat9 V17 C2 2 EF0 EU 08 95 G H UE2U0 8 R V-C13 772 EEGUU7 9 H R 94 461679H9 HRV 7 -C 5 R 6 V p 2 V- C3 a t5 EEQF39 78358 -C 1 EF 2 F15823752 HH RV 0 8620 774 RV -C p E7U72 5 38 7 HR -C p at 16 EF59FJ66 HR 7 HR V- C at 1 H V- 10 4 070 05157 V-C RV C 72 4 3 7 p -C 3 60 HR H a t1 R 6 V HR -C VV- p C p C at1 at 2 pa 0 1 t2

61 genotypes (33 + 28 pending approval)

-77 6-107 AF343608 HRVA 0-6 0V 4 2125 5148VHR A-1VA FJ44 3-21A VA -A RA 643 H R09 HHR V AF34 3AF34433660 7HR A-8V A R8VHHRRV 23 3 H A F4335 8 33 623 0 9 303 A F 04 0234336 A2-V4AR-VA VA Y AFF3 4 RA A 8 HV HR7 H 9 R6 9 3 5 H59 5 34 1 6 3 43 AF 023F34 F3 X A A

R VA -36 886 H HRVA-58 3435 023 A F04 AY M16248 HRVA-89 AF A34 F3358 435990HRVA-7 HR VA -8 8 01 AFAY A F64 34 06 3A4F3 -28 359HR 6 2 HVA 4 RV A FAAFF334 345H R V -20 A-5 3 34334435981 HA V -6 5385865 HR 7 H H RVA 8 A YA F3 RVR VAA- 51 0 1 4433559 A A Y64A Y 954 H -71-65 040 8 01 6HRRVA 02 HR 40 VA - 8 A F35 VA9 H -95 34H R - 78 RV A-4 35 VA 5 93 -4 HR6 VA -8 0

74 serotypes

5 t1 pa

-C RV 6 H -C2 5 V 11 t 4 92 R 5 -C pa 27 4 341 Hpat2 V C -C 7 9 4 HR V V EU52 V-C V- C 2 H R2 HR 5 7 R R 5 1 U 9 9 12 V-C t 12 E 0 H 28 H 480172 236 H R -C pa at 17 8 95 3 2 Q 0 9 UU G 8238 H RV RV -C p RV -C2 9 2 6 2 E 4 8 H 9H 5 6 EF 9 0705 2398FJ61 569 FJ GQ EU5EU 3 V- C20 pat t19 EU 6978 51 HR a -C R V R V- C p H H 79980096 -C pa t18 1 8 V R 0 J5 EU F241 2 H EU7 5 H RV -C pat2 2 45 FJ61 57 EU081796 HRV- C14 EU697850 HRV-C19 EF58 85 HR GQ4 EU6923 7839 V-C 4 66 H E GU EEUFU0752448 2 HRV RV-C pat1 2 3 7 0 7 29 72 8 HR -C pat 2 8 1 44 6 V- C EU 80 0 59 E FJ 80 H 2 HHRRV-C 25 0 U 8 R 2 EF06108 419 5 V- C V-C pa 07 H180 7 H 33 t6 72 RV 5 H RV EU 64 -C p RV - -C p 08 H R at1 C p a t23 18 V 1 at 8 03 -C1 HR 2 VC pa t7

HRV-A

A A F A F33 4 A FF34433663 3 4 36 3 1 AF 2 33462364 HHHRR AFA3Y01 6 3 RVVVAA AF43360420670H1HR A --174 A 43 H R RHVV RA 3249 V AV---A A F3F43343652909 H -161 7-318 AF343 60 6 21 HR V-A HRRVA 4 V A HR A -5-59103 A-96A-5 AF343622VHRV 7-67 HRVA-75 AF34363903 VA HR A-1 321 RVVA AF3436 -962 3 HR A F3445312 7HHR VA FJ443 605 AF3

AY 01 AF 64 A F 34 05 3 4 36 H 36 3 A R V3 5 H A A F A F A6 R A FF34334 36 F334436-1H2RVVA A F34366 15 16 36113 A -5 A F3344361187 HHRRHVRV 2 HHR- 630 A -2A RVVA AF334633 7 HHRRVVVA -62259-4 4 A--59 606 H RAA--3 48 9 A F3 43 VA-8 64 2 HR VA1-8A-64 AF343629 HRV 5 A-41 HRVVA AF34 3600 -59 0 HR -4 11 -9 AF3436 A 4H RVA VA-61 8 H RV 5129 HRA--156 AF34 363 FJ443 43603 3HRRVVA- 7--362343 8 F AA -4 -03 A 43617 H VV A -6 RR VA 3 491 HH A FL 2 2 425 HRRVVAVA 363661 92 HHRHR 4 3 4 A FA F33 43023271 4 A FY0443 6436 A F3 F3 A A

HRV-C

0 C3 0 V- a t2 15 HR-C p -C 69 V RV C23 66 HR 0 H V4722 80 HR Q 7 81 24 4 G615 U0524 -C2 4 pat2 FJ EEU 7 H RV RV -C -C16 H HR V 426 3 75 2 992 808 17 EU FJ86 U081HR V-C V- C8 9 E R 81 80223 227 HHRV -C pa t28 E U0 GQ 3472 48HR V-C pat27 HM 214340 GU EU5 74 G QG22900 31 HRV-C18 U 2 943348H RV -C2 8 0 HR V 2 C 3 1 -4 V B -4 B HRRV 04 H 9 6 4 55 - 9 014 36RVB 2 6 AAYF32 H RVB -8 4 5 36 H VB 3 3 4 653 H R VB -9 A F34 3 240 B-27 39 HR AF Y0440 HRAVY0402 7 1 5 A B 43 6 HR V - 70 AF3 6 45 HRVB 343364 6 1 A AFF3 40237 HRVB-9 04 AY AY016400 0163 99 HRHRVB-48 A YAY 01 6 VB -69 398 H RV B -5 2 AY01 6 A Y0403 HR VB AY -3 A A016440024 1 H F Y 01NC034 3 1 HR RVB- 35 6 4A F01 648 VB -3 0 3 49 H R 7 4 2 0 HR36 5 HR V B-8 VB 0 H VB- 6 R 1 6 VB 4 -7 2

HRV-B

A

25 serotypes

2 -9 9 VB B- 7 - 83 HR RV RVB 38 5 H4 7 H 02515436 0J44A4 F3 AYF 0.05

HRV-C: • Virus isolation: NO • Identified only with molecular methods, RT-PCR HRV-A, HRV-B: • Cell culture isolation: YES (with standard cell lines)

Y0 40 24 2

A

HR F3 VB 43 - 9 65 7 1 HR

VB

-5

• Identified with serotyping methods (1950-1980)


Pathogenicity of human rhinoviruses (HRVs) HRV = agents of common cold Upper respiratory tract infections High frequency in the general population

Sensitive molecular methods: 1. Improvement of diagnosis 2. Involvement in lower infections of the respiratory tract Severe diseases: Pneumonia in young children, older adults, immunocompromised patients

Rhinitis Sinusitis Otitis Pharyngitis Exacerbation Asthma Chronic lung diseases Lower respiratory manifestations {Bronchiolitis} {Pneumopathies} Mofified from Cordey et al. (2008) Virologie 12:36112:361-376.

Holt and Sly. (2012) Nature Medicine 18:72618:726-735. BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Objectives Winter 2009-2010 (H1N1 pandemic period)

1. Characterise clinical features / patients (positive HRV RNA) 2. Determine prospectively distribution of HRV species / genotypes Genotyping procedure: Nasopharyngeal aspirates, nasal swabs … Purification of viral RNA (automated)

232 respiratory specimens; 209 patients (children, n= 109) VP4

VP2

Single-tube cDNA synthesis + PCR Sequencing with PCR primers Identification (phylogenetic analysis of sequences)

Amplicon length = ~ 550 ntd Final sequence length = 414 ntd BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Results (1): Phylogenetic analysis of sequences HRV infection in 58 patients (specimens, n=63); 13 adults Genotyping: 34 HRV genotypes identified HRV-A: 52%

HRV-B: 6%

HRV-C: 40%

Diversity of co-circulating strains

Bronchiolitis, pneumonia, exacerbation of asthma: 34/45 children Pneumonia, exacerbation of chronic lung diseases: 8/13 adults (one death, an immunocompromised patient, multi-organ failure)

Prolonged infections, co-infections or serial infections


Results (2): Phylogenetic analysis of serial infections 9 HRV sequences determined in 4 children

Patient 11, n=2, same virus, prolonged infection / 1 month Patient 54, n=1, 2 genotypes, dual infection Patient 9, n=2, 2 genotypes, serial infections Patient 16, n=3, 3 genotypes, serial infections

HRV-A

HRV-C

Need for prospective diagnosis of HRV infections and virus genotyping

Comparison with 160 reference HRV sequences

HRV diversity: Cause of frequent severe infections High hospital admission rate in children Henquell et al (2012) J Clin Virol 53:28053:280-284. 284

Standard phylogenetic analysis (neighbour joining) with www.megasoftware.net/) computer program MEGA5 (http://www.megasoftware.net


Phylogenies as tools used for detecting viral epidemiological patterns Molecular epidemiology = Consistent phylogenetic/statistical framework to describe genetic relationships between pathogens to make inference on their evolution, spread over time and across geographic regions (Phylogeography)

Phylogenetic trees have various exploratory potentials BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

What does phylogenetic analysis consist of? Gene sequencing to obtain molecular sequence data

Calculate genetic distances between all sequence pairs

Multiple sequence alignment

Distance matrix

Estimate relationships btw seq. Reconstruct a phylogenetic tree

Identify consistent patterns in viral diversity from the phylogeny

Clusters of genetically related sequences BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

What does phylogenetic analysis consist of? Gene sequencing to obtain molecular sequence data

Neighbour-joining One tree, accuracy = bootstrap

Calculate genetic distances between all sequence pairs Estimate relationships btw seq.

Maximum likelihood One optimized tree, accuracy = bootstrap

Reconstruct a phylogenetic tree

Identify consistent patterns in viral diversity from the phylogeny

Coalescent methods A sample of thousands trees including that with the highest likelihood; calculation of credibility intervals


Phylogenies = biological features

Static representations?

Phylogeny of cytochrome c by Fitch and Margoliash (1967)

Phylogenies = Information about various dynamics

Phylogenies contain information about various dynamics Diversity

Spatial

Temporal

Population 3

Region 1

Population 2

Region 2

Population 1

Time 3 Time 2

Region 3

Time 1

Branch length substitution per site

Consistent lineages correspond to known phenotypic traits

1900

Lineages falling into geographically defined clusters

Years

2000

Lineages ordered according to sampling time

Influenza A viruses Enterovirus genogroups Pybus, Rambaut. Evolutionary analysis of the dynamics of viral infectious diseases. Nature Rev Genet. 2009. 10:540. Grenfell et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 2004. 303:327. BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Spatial dynamics Yellow fever virus Bryant et al. (2007) PLOS Pathogens 3:5.e75


Lineage C1b

Temporal dynamics

(Asia 19971997-2007) (Europe 19981998-2008)

Enterovirus 71 Enterovirus associated with epidemics of hand, foot and mouth disease

Lineage C1a (1986-1998)

Ladder-like shape

AUS 86-96 USA 87-95 MYS 97-03 TWN 98 SGP 98-02 THA 07-08 GBR 98-06 NLD 02 FRA 00-07 GER 00-08 THA 07-08

◄ Subgenogroup C1

AUS 95-99 USA 97-98 MYS 97-99 JPN 97-03 TWN 98 GBR 99 NLD 00-06 FRA 00-09 GER 06-07 THA 07-08 SGP 08

◄ Subgenogroup C2

Mirand et al. (2010) J Gen Virol 91:2263-2277. Lineage C2b (Europe 2006-2009)

Lineage C2a

◀ Europe 1999-2000 ◀ Taiwan 1998


Phylogenetic methods based on the coalescent concept Population 3

Population 2 Population 1

Region 1

Time 3

Region 2

Time 2

Region 3

Time 1

Branch length substitution per site

1900

Years

2000

Different dynamics / same analysis BAILLY Jean-Luc - Workshop SPHINX HCV – TUNIS – June 2012

Coalescent is a concept of population genetics Kingman, 1982

Sampled individual Not sampled

Ancestor

Past

−

A concept that describes a relationship between coalescence time and population size

Coalescence time

2

1

TMRCA: Time to the Most Recent Common Ancestor

Sampling time − Present

Coalescence at a given time during the evolutionary history depends on the size of the overall population

Probabilistic phylogenetic method calculate the probability that 2 sequences have a common ancestor

Viral molecular epidemiology: estimate divergence time of lineages, i.e. evolutionary history Kingman JFC. (1982) Stoch Proc Appl 13:235-248. BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Coalescence time and virus population size 2 sequences sampled in a small size population will have recent coalescence

2 sequences sampled in a large population will have distant coalescence Sampled individual Not sampled

Ancestor

Past

−

Distant TMRCA

− Recent TMRCA 2

1

2

1

Present

BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Coalescence time and virus population size Sample of 5 sequences Past

− − −

5

4

3

2

Number and frequency over time of coalescence events are related to the TMRCA2 demographic pattern of the TMRCA1 overall population TMRCA3

1

Present

Phylogeny (topology, node organization) reflects the demographic history of circulating viral populations (size changes over time)


‘Typical’ phylogenetic patterns (1) Virus population demography

topology of phylogenetic tree

Phylogenetic pattern of a population evolving according to an exponential growth HCV genotypes

.../... BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012



Population evolving according to an constant size

.../... BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012



Population evolving according to a shrinking size

Natural populations rarely display typical patterns .../... BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

What can we expect from coalescent analyses? 1. Estimate evolutionary rates of viruses (number of substitutions per site per year) 2. Estimate their divergence date from a common ancestor 3. Shape of the phylogenetic tree patterns of virus/host interactions

Sample size = important parameter for consistency of results

Phylogenetic analyses performed with Bayesian statistical methods (Computer programs: MrBayes§, BEAST* ...) § http://mrbayes.sourceforge.net / http://mrbayes.sourceforge.net/

* http://beast.bio.ed.ac.uk/Main_Page http://beast.bio.ed.ac.uk/Main_Page BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Transmission of hepatitis C virus genotype 5 in a population living in a rural area of central France

Henquell et al. (2011) Evolutionary history of hepatitis C virus genotype 5a in France, a multicenter ANRS study Infection, Genetics and Evolution 11:496-503.


General epidemiological patterns of HCV-5

Henquell et al. (2011) Virologie 15:28615:286-295.

Clermont-Ferrand

Henquell et al. (2011) Virologie 15:28615:286-295. Gaudy et Goudeau. (2005) Virologie 15:34315:343-355.


The beginning (early 2000’s) Observations from diagnostic practice

(1) Virological observation (prospective genotyping in patients with hepatitis C infection)§: genotype 5 = third genotype (hospital of Clermont-Ferrand)

(2) Epidemiological observation (hepatologists)*: most patients with HCV-5 infection lived in a rural area (located at 25 km south east from Clermont-Ferrand)

§ Henquell et al (2004) J Clin Microbiol. 42:3030-3035.

* Abergel et al (2007) Aliment Pharmacol Therap 26:1437-1446. BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Questions raised by HCV-5 infections in central France How was the virus transmitted? When did transmission begin in the rural area? Genetic relationships between HCV-5 strains in France? Time origin of transmission in France? Geographical relationships between worldwide HCV-5 strains?

Evolutionary history of HCV-5: the time origin estimated with molecular data and Bayesian statistical analyses BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Study population and molecular data HCV-5 study population

HCV-5 genotyping (gene amplification and sequencing)

Group 1: 131 patients (central France)

E1 genomic segment (808 bp) NS3/4 segment (618 bp)

Group 2: French blood donors, n=14

All sequences had a known sampling date coalescence analyses

Patients in other hospitals, n=108

Phylogenetic investigations with computer program BEAST *

* http://beast.bio.ed.ac.uk/Main_Page http://beast.bio.ed.ac.uk/Main_Page BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Dating HCV5 / Central France / Coalescent analysis For accurate reconstruction:

Present

1) Sampling dates of blood specimens

−2003

2) Specify other calibration time points = known dates of transmission between Pairs of blood donor and recipient Husband and wife in marital couples

−

TMRCA

?

Most recent common ancestor

Past


Results (1) Time origin of HCV-5 in central France Molecular data from group 1 Pairs of blood donor and recipient Husband and wife in marital couples

Time origin of HCV-5 (1942-1967) 1954


Results (2) Time origin of HCV-5 in France Molecular data from groups 1 and 2

Geographic origins in France

(1921-1956) 1939 1954 (1942-1967)

Comparisons to HCV-5 sequences from other countries: no consistent geographic patterns Precise geographic origin of HCV-5: unknown BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Conclusions: molecular data / epidemiological data 1900

1910

1920

1930

1940

1950

1960

1970

1970

1939

Origin in France 1954

Origin in central France Involvement of a local physician in iatrogenic transmission

Early 1950’s

1972 1991

Blood transfusion No transmission occurred after 1991 (anti HCV tests in blood donors)


CONCLUSIONS


Different requirements Virus genotyping

Viral molecular epidemiology

Short sequences (#200 nt)

Long sequences

Sequence similarity patterns

Large sequence datasets

(% nucleotide similarity)

Evolutionary patterns (% nt substitutions/site/year)

Blast or standard NJ methods (Web facilities) Curated sequence databases Consistent virus identification

Robust phylogenies: Maximum likelihood Coalescent analysis (Bayesian statistical framework)

Virus dynamics BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Influenza A H3N2

Phylogenies can help identify reassortment in RNA viruses with segmented genomes Phylogenies illustrating the history of the influenza A virus H3N2 estimated with hemagglutinin (HA) and polymerase (PA) genes. Wolf et al (2006) Biology Direct 1:34.

Variations in clustering of virus groups between two genes = indicative of reassortment (or recombination in ssRNA viruses) BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Influenza A H3N2

Ancestral sequence reconstruction Phylogenies illustrating amino acid changes in the hemagglutinin (HA) gene of influenza A virus H3N2. Wolf et al (2006) Biology Direct 1:34.

Phylogenies can help identify mutations associated with antigenic variations


Dengue virus 4

Demography of virus populations (Bayesian skyline) H3N2

Phylogenies can help identify patterns of virus circulation / spread BAILLY Jean-Luc − Workshop SPHINX HCV – TUNIS – June 2012

Important progress in understanding the epidemiology of medically important viruses

Evolutionary dynamics studies with molecular data

Major improvements in 3 technologies: Genomic sequencing, Statistical analysis, High-performance computing

+++ Comprehensive surveys / Large-scale sampling / Epidemiological data


European collaborators DENMARK, Copenhagen (Dr B Böttiger, Statens Serum Institut)

FINLAND, Helsinki (Dr M Roivainen, WHO Collaborating Centre for Poliovirus Surveillance and Enterovirus Research))

GERMANY France, Lyon (Pr B Lina,

• Berlin (Dr S Diedrich, Robert Koch Institute) • Stuttgart (Dr E Ladwig, Labor Enders)

CNR Entérovirus)

AUSTRIA, Vienna (Dr G Wewalka Austrian Agency for Health and Food Safety))

ITALY, Bolzano (Prof. C. Larcher, Laboratorio Aziendale di Microbiologia e Virologia)

HUNGARY, Budapest (Dr A Farkas,

CYPRUS (Dr C Christodoulou, Cyprus Institute of Neurology and Genetics)

National Center for Epidemiology)

Molecular epidemiology of enterovirus infections

EPIE EA4843 Epidemiology and Pathogeny of Enterovirus Infections

Pr Hélène Peigue-Lafeuille Dr Jean-Luc Bailly Dr Cécile Henquell Dr Martine Chambon Dr Christine Archimbaud Dr Audrey Mirand Dr Christel Regagnon Dr Amélie Brebion Gwendoline Jugie Isabelle Simon Nathalie Rodde Romain Volle, PhD student Chervin Hassel, PhD student