Kerensa McElroy

Sequential Bottlenecks Drive Viral Evolution in Early Acute Hepatitis C Virus Infection

Hepatitis C is a pandemic human RNA virus, which commonly causes chronic infection and liver disease. The characterization of viral populations that successfully initiate infection, and also those that drive progression to chronicity is instrumental for understanding pathogenesis and vaccine design. A comprehensive and longitudinal analysis of the viral population was conducted in four subjects followed from very early acute infection to resolution of disease outcome. By means of next generation sequencing (NGS) and standard cloning/Sanger sequencing, genetic diversity and viral variants were quantified over the course of the infection at frequencies as low as 0.1%. Phylogenetic analysis of reassembled viral variants revealed acute infection was dominated by two sequential bottleneck events, irrespective of subsequent chronicity or clearance. The first bottleneck was associated with transmission, with one to two viral variants successfully establishing infection. The second occurred approximately 100 days post-infection, and was characterized by a decline in viral diversity. In the two subjects who developed chronic infection, this second bottleneck was followed by the emergence of a new viral population, which evolved from the founder variants via a selective sweep with fixation in a small number of mutated sites. The diversity at sites with non-synonymous mutation was higher in predicted cytotoxic T cell epitopes, suggesting immune-driven evolution. These results provide the first detailed analysis of early within-host evolution of HCV, indicating strong selective forces limit viral evolution in the acute phase of infection.

Contribution of Intra- and Interhost Dynamics to Norovirus Evolution

ABSTRACT Norovirus (NoV) is an emerging RNA virus that has been associated with global epidemics of gastroenteritis. Each global epidemic arises with the emergence of novel antigenic variants. While the majority of NoV infections are mild and self-limiting, in the young, elderly, and immunocompromised, severe and prolonged illness can result. As yet, there is no vaccine or therapeutic treatment to prevent or control infection. In order to design effective control strategies, it is important to understand the mechanisms and source of the new antigenic variants. In this study, we used next-generation sequencing (NGS) technology to investigate genetic diversification in three contexts: the impact of a NoV transmission event on viral diversity and the contribution to diversity of intrahost evolution over both a short period of time (10 days), in accordance with a typical acute NoV infection, and a prolonged period of time (288 days), as observed for NoV chronic infections of immunocompromised individuals. Investigations of the transmission event revealed that minor variants at frequencies as low as 0.01% were successfully transmitted, indicating that transmission is an important source of diversity at the interhost level of NoV evolution. Our results also suggest that chronically infected immunocompromised subjects represent a potential reservoir for the emergence of new viral variants. In contrast, in a typical acute NoV infection, the viral population was highly homogenous and relatively stable. These results indicate that the evolution of NoV occurs through multiple mechanisms.

Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions

Deep sequencing harnesses the high throughput nature of next generation sequencing technologies to generate population samples, treating information contained in individual reads as meaningful. Here, we review applications of deep sequencing to pathogen evolution. Pioneering deep sequencing studies from the virology literature are discussed, such as whole genome Roche-454 sequencing analyses of the dynamics of the rapidly mutating pathogens hepatitis C virus and HIV. Extension of the deep sequencing approach to bacterial populations is then discussed, including the impacts of emerging sequencing technologies. While it is clear that deep sequencing has unprecedented potential for assessing the genetic structure and evolutionary history of pathogen populations, bioinformatic challenges remain. We summarise current approaches to overcoming these challenges, in particular methods for detecting low frequency variants in the context of sequencing error and reconstructing individual haplotypes from short reads.

Reconstruction of Ribosomal RNA Genes from Metagenomic Data

Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.

Strain-specific parallel evolution drives short-term diversification during Pseudomonas aeruginosa biofilm formation

Significance Within-population genetic diversity is an essential evolutionary prerequisite for processes ranging from antibiotic resistance to niche adaptation, but its generation is poorly understood, with most studies focusing on fixed substitutions at the end point of long-term evolution. Using deep sequencing, we analyzed short-term, within-population genetic diversification occurring during biofilm formation of the model bacterium Pseudomonas aeruginosa. We discovered extensive parallel evolution between biological replicates at the level of pathways, genes, and even individual nucleotides. Short-term diversification featured positive selection of relatively few nonsynonymous mutations, with the majority of the genome being conserved by negative selection. This result is broadly consistent with observations of long-term evolution and suggests diversifying selection may underlie genetic diversification of Pseudomonas aeruginosa biofilms. Generation of genetic diversity is a prerequisite for bacterial evolution and adaptation. Short-term diversification and selection within populations is, however, largely uncharacterised, as existing studies typically focus on fixed substitutions. Here, we use whole-genome deep-sequencing to capture the spectrum of mutations arising during biofilm development for two Pseudomonas aeruginosa strains. This approach identified single nucleotide variants with frequencies from 0.5% to 98.0% and showed that the clinical strain 18A exhibits greater genetic diversification than the type strain PA01, despite its lower per base mutation rate. Mutations were found to be strain specific: the mucoid strain 18A experienced mutations in alginate production genes and a c-di-GMP regulator gene; while PA01 acquired mutations in PilT and PilY1, possibly in response to a rapid expansion of a lytic Pf4 bacteriophage, which may use type IV pili for infection. The Pf4 population diversified with an evolutionary rate of 2.43 × 10−3 substitutions per site per day, which is comparable to single-stranded RNA viruses. Extensive within-strain parallel evolution, often involving identical nucleotides, was also observed indicating that mutation supply is not limiting, which was contrasted by an almost complete lack of noncoding and synonymous mutations. Taken together, these results suggest that the majority of the P. aeruginosa genome is constrained by negative selection, with strong positive selection acting on an accessory subset of genes that facilitate adaptation to the biofilm lifecycle. Long-term bacterial evolution is known to proceed via few, nonsynonymous, positively selected mutations, and here we show that similar dynamics govern short-term, within-population bacterial diversification.

Accurate single nucleotide variant detection in viral populations by combining probabilistic clustering with a statistical test of strand bias

BackgroundDeep sequencing is a powerful tool for assessing viral genetic diversity. Such experiments harness the high coverage afforded by next generation sequencing protocols by treating sequencing reads as a population sample. Distinguishing true single nucleotide variants (SNVs) from sequencing errors remains challenging, however. Current protocols are characterised by high false positive rates, with results requiring time consuming manual checking.ResultsBy statistical modelling, we show that if multiple variant sites are considered at once, SNVs can be called reliably from high coverage viral deep sequencing data at frequencies lower than the error rate of the sequencing technology, and that SNV calling accuracy increases as true sequence diversity within a read length increases. We demonstrate these findings on two control data sets, showing that SNV detection is more reliable on a high diversity human immunodeficiency virus sample as compared to a moderate diversity sample of hepatitis C virus. Finally, we show that in situations where probabilistic clustering retains false positive SNVs (for instance due to insufficient sample diversity or systematic errors), applying a strand bias test based on a beta-binomial model of forward read distribution can improve precision, with negligible cost to true positive recall.ConclusionsBy combining probabilistic clustering (implemented in the program ShoRAH) with a statistical test of strand bias, SNVs may be called from deeply sequenced viral populations with high accuracy.

Identification of a polymorphic collagen-like protein in the crustacean bacteria Pasteuria ramosa

Pasteuria ramosa is a spore-forming bacterium that infects Daphnia species. Previous results demonstrated a high specificity of host clone/parasite genotype interactions. Surface proteins of bacteria often play an important role in attachment to host cells prior to infection. We analyzed surface proteins of P. ramosa spores by two-dimensional gel electrophoresis. For the first time, we prove that two isolates selected for their differences in infectivity reveal few but clear-cut differences in protein patterns. Using internal sequencing and LC/MS/MS, we identified a collagen-like protein named Pcl1a (Pasteuria collagen-like protein 1a). This protein, reconstructed with the help of Pasteuria genome sequences, contains three domains: a 75-amino-acid amino-terminal domain with a potential transmembrane helix domain, a central collagen-like region (CLR) containing Gly-Xaa-Yaa (GXY) repeats, and a 7-amino-acid carboxy-terminal domain. The CLR region is polymorphic among the two isolates with amino-acid substitutions and a variable number of GXY triplets. Collagen-like proteins are rare in prokaryotes, although they have been described in several pathogenic bacteria, including Bacillus cereus, Bacillus anthracis and Bacillus thuringiensis, closely related to Pasteuria species, in which they could be involved in the adherence of bacteria to host cells.

Characterisation of a large family of polymorphic collagen-like proteins in the endospore-forming bacterium Pasteuria ramosa

Collagen-like proteins containing glycine-X-Y repeats have been identified in several pathogenic bacteria potentially involved in virulence. Recently, a collagen-like surface protein, Pcl1a, was identified in Pasteuria ramosa, a spore-forming parasite of Daphnia. Here we characterise 37 novel putative P. ramosa collagen-like protein genes (PCLs). PCR amplification and sequencing across 10 P. ramosa strains showed they were polymorphic, distinguishing genotypes matching known differences in Daphnia/P. ramosa interaction specificity. Thirty PCLs could be divided into four groups based on sequence similarity, conserved N- and C-terminal regions and G-X-Y repeat structure. Group 1, Group 2 and Group 3 PCLs formed triplets within the genome, with one member from each group represented in each triplet. Maximum-likelihood trees suggested that these groups arose through multiple instances of triplet duplication. For Group 1, 2, 3 and 4 PCLs, X was typically proline and Y typically threonine, consistent with other bacterial collagen-like proteins. The amino acid composition of Pcl2 closely resembled Pcl1a, with X typically being glutamic acid or aspartic acid and Y typically being lysine or glutamine. Pcl2 also showed sequence similarity to Pcl1a and contained a predicted signal peptide, cleavage site and transmembrane domain, suggesting that it is a surface protein.

Draft Genome Sequence of the Chronic, Nonclonal Cystic Fibrosis Isolate Pseudomonas aeruginosa Strain 18A

ABSTRACT Pseudomonas aeruginosa strain 18A is a clinical, nonclonal isolate retrieved from the sputum of a chronically infected cystic fibrosis patient. The genome of 18A was sequenced for comparison with environmental and clinical isolates to identify genes that might facilitate its persistence during infection.

Bacteriophage evolution drives Pseudomonas aeruginosa PAO1 biofilm diversification

Background Pseudomonas aeruginosa infection is the leading cause of death for Cystic Fibrosis patients. Antibiotic resistance is rife, possibly due to high colonising population diversity. Our lab has replicated phenotypic diversification in a P. aeruginosa PAO1 biofilm model of lung infection. To reveal underlying genetic variants, we deep-sequenced PAO1 biofilm samples. Our analysis demonstrates several techniques for extracting meaningful biological information from error-prone sequencing data.