Characterizing intra-host influenza virus populations to predict emergence

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1 Characterizing intra-host influenza virus populations to predict emergence June 12, 2012 Forum on Microbial Threats Washington, DC Elodie Ghedin Center for Vaccine Research Dept. Computational & Systems Biology

2 Intra-host RNA virus diversity persistent HIV, HCV, (time) Replication errors, recombination HIGH DIVERSITY acute Influenza A?

3 Factors that affect viral diversity antivirals Immune pressure Transmission bottlenecks

4 Does natural selection occur within individual hosts? How big is the population bottleneck at transmission? What is the extent of mixed infection? What is the mutational spectrum within individual hosts? What is the fitness distribution of these mutations?

5 Influenza A Virus: 3 models to explain transmission and emergence A B C Host 1 Host 2 Host 1 Host 2 Host 1 Host 2 Immunocompromised Host 2 Persistent infection

6 What happens to viral diversity if influenza infection is persistent? persistent HIV, HCV, (time) HIGH DIVERSITY acute Influenza A? What happens in immunocompromised patients? (longer infection period)

7 Emergence of H275Y resistance in immunocompromised host Q1: Is the drug-resistant variant present at time 0 (sample 1) before oseltamivir treatment? Q2: What other variant positions are associated with H275Y? Q3: Is the drug-resistant virus a variant of the WT or due to a mixed infection?

8 NA Aligned short sequence reads on segments 1.4 Kb

9 Consensus assembly would read this as a CAC TAC leads to drug resistance

10 From alignment of sequence reads can generate summaries at each codon NA Position Total reads Dominant Minor 1 Minor V {GTC} A {GCC} D {GAC} E {GAA} K {AAA} E {GAG} M {ATG} V {GTG} T {ACG} N {AAT} S {AGT} D {GAT} A {GCC} T {ACC} A {GCA} P {CCT} P {CCC} P {CCA} N {AAT} S {AGT} H {CAT} Y {TAT} Y {TAC} C {TGT} H {CAC} Y {TAC} R {CGC} Y {TAT} N {AAT} Y {TAC} E {GAG} G {GGG} E {GAA} E {GAA} E {GAG} G {GGA} C {TGC} C {TGT} Y {TAC} S {TCC} P {CCC} S {TCA} C {TGT} S {AGT} C {TGC} Y {TAT} Y {TAC} H {CAT} P {CCT} P {CCC} P {CCA}

11 Emergence of H275Y resistance in immunocompromised host Q1: Is the drug-resistant variant present at time 0 (sample 1) before oseltamivir treatment? Q2: What other variant positions are associated with H275Y? Q3: Is the drug-resistant virus a variant of the WT or due to a mixed infection?

12 Diversity of H1N1/2009: Many strains co-circulate (but antigenically similar) Wave 1 April 2009 Nelson et al. (2011) J. Virol. 85:828

13 Wave 2: mix across geographical regions but dominance of one strain Clade 7

14 Immunosuppressed patient infected during wave 2 of H1N1/2009 pandemic Sample 1 Sample 2 Consensus seq: H275 Deep seq: H275 Consensus seq: H275 Deep seq: H Y tamiflu De novo mutations or mixed infection?

15 Reconstruction of variants? Representation of minor to major variants residue positions on the NA protein Frequency estimation for assembly?

16 Difficult to link positions on separate sequence reads Representation of minor to major variants Unique residue positions on the NA protein Sequence reads

17 The Assembly Phasing Problem Sample 1 Sample 2 Bansal et al (2008) Genome Res. 18:1336

18 R CLADE 2 Partial assembly of sequence reads demonstrates presence of co-infecting variants CLADE 7 Ghedin et al (2011) J. Inf. Dis. 203:168

19 Emergence of H275Y drug resistance mutation Q1: Is the drug-resistant variant present at time 0 (sample 1) before oseltamivir treatment? Q2: What other variant positions are associated with H275Y? Q3: Is the drug-resistant virus a variant of the WT or due to a mixed infection? Q4: Can the drug resistance mutation be transmitted?

20

21 Q1. Are drug-resistant viruses present as a subpopulation in the son? Q2. How is the bottleneck at transmission? Son Father t=i Tamiflu Rx RT-PCR: Drug sensitive? t=i t=i + 6 Tamiflu prophylaxis RT-PCR: Drug resistant

22 Son to Father Transmission Results for NA (codon 275) Q1: Is the drug-resistant strain present as a subpopulation in the individual carrying the oseltamivir-sensitive strain? YES

23 Log10 Sequence coverage of each codon on all proteins Son Father Major codons Most common minor variant codons

24 Account for all sources of stochastic variation to determine distribution of true variants Monte Carlo methods to generate empirical null distribution Simulation process: Assumptions: 5 days of infection/10 cycles of replication; Per site mutation rate: 1 in 10,000 Efficiency of replication: simulated PCR cycles; Per site mutation rate: 1 in 10,000 Efficiency of replication: 0.8 Sequencing error: per site; Error profile used (accounting for HiSeq GC bias): Test statistic calculated for each position in simulated and experimental data; Report p-value calculated from rank

25 What is being transmitted? Son Father t=i t=i + 6 tamiflu

26 Summary 1. Multiple variants transmitted transmission bottleneck is not especially narrow. 2. Some variants are lost at transmission, and some attain a higher frequency in the recipient transmission plays a major role in shaping patterns of genetic diversity in influenza virus. 3. Pre-existing variation at 275 position in NA mutation not deleterious, compensatory mutations not necessary

27 Reconstructing chains of transmission for an epidemic? Consensus sequence R 0 =1.2 At the scale flu evolution happens, there are not enough changes to reconstruct transmission networks

28 Reconstructing transmission phylogeny Deep Sequencing significant changes in the distribution in the intra-host population

29 Acknowledgements Ghedin Lab: Jay DePasse Adam Fitch Eddie Holmes Jesse Papenburg Guy Boivin Marie-Eve Hamelin Lady Tatiana Pinilla Funding: NIH/NIAID

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