Showing papers by "Colin A. Russell published in 2019"

Incomplete genetic reconstitution of B cell pools contributes to prolonged immunosuppression after measles

Abstract: Measles is a disease caused by the highly infectious measles virus (MeV) that results in both viremia and lymphopenia. Lymphocyte counts recover shortly after the disappearance of measles-associated rash, but immunosuppression can persist for months to years after infection, resulting in increased incidence of secondary infections. Animal models and in vitro studies have proposed various immunological factors underlying this prolonged immune impairment, but the precise mechanisms operating in humans are unknown. Using B cell receptor (BCR) sequencing of human peripheral blood lymphocytes before and after MeV infection, we identified two immunological consequences from measles underlying immunosuppression: (i) incomplete reconstitution of the naive B cell pool leading to immunological immaturity and (ii) compromised immune memory to previously encountered pathogens due to depletion of previously expanded B memory clones. Using a surrogate model of measles in ferrets, we investigated the clinical consequences of morbillivirus infection and demonstrated a depletion of vaccine-acquired immunity to influenza virus, leading to a compromised immune recall response and increased disease severity after secondary influenza virus challenge. Our results show that MeV infection causes changes in naive and memory B lymphocyte diversity that persist after the resolution of clinical disease and thus contribute to compromised immunity to previous infections or vaccinations. This work highlights the importance of MeV vaccination not only for the control of measles but also for the maintenance of herd immunity to other pathogens, which can be compromised after MeV infection.

Phylogenetic Clustering by Linear Integer Programming (PhyCLIP).

Abstract: Subspecies nomenclature systems of pathogens are increasingly based on sequence data. The use of phylogenetics to identify and differentiate between clusters of genetically similar pathogens is particularly prevalent in virology from the nomenclature of human papillomaviruses to highly pathogenic avian influenza (HPAI) H5Nx viruses. These nomenclature systems rely on absolute genetic distance thresholds to define the maximum genetic divergence tolerated between viruses designated as closely related. However, the phylogenetic clustering methods used in these nomenclature systems are limited by the arbitrariness of setting intra and intercluster diversity thresholds. The lack of a consensus ground truth to define well-delineated, meaningful phylogenetic subpopulations amplifies the difficulties in identifying an informative distance threshold. Consequently, phylogenetic clustering often becomes an exploratory, ad hoc exercise. Phylogenetic Clustering by Linear Integer Programming (PhyCLIP) was developed to provide a statistically principled phylogenetic clustering framework that negates the need for an arbitrarily defined distance threshold. Using the pairwise patristic distance distributions of an input phylogeny, PhyCLIP parameterizes the intra and intercluster divergence limits as statistical bounds in an integer linear programming model which is subsequently optimized to cluster as many sequences as possible. When applied to the hemagglutinin phylogeny of HPAI H5Nx viruses, PhyCLIP was not only able to recapitulate the current WHO/OIE/FAO H5 nomenclature system but also further delineated informative higher resolution clusters that capture geographically distinct subpopulations of viruses. PhyCLIP is pathogen-agnostic and can be generalized to a wide variety of research questions concerning the identification of biologically informative clusters in pathogen phylogenies. PhyCLIP is freely available at http://github.com/alvinxhan/PhyCLIP, last accessed March 15, 2019.

Genetic diversity and host adaptation of avian H5N1 influenza viruses during human infection.

Abstract: The continuing pandemic threat posed by avian influenza A/H5N1 viruses calls for improved insights into their evolution during human infection. We performed whole genome deep sequencing of respiratory specimens from 44 H5N1-infected individuals from Indonesia and found substantial within-host viral diversity. At nearly 30% of genome positions multiple amino acids were observed within or across samples, including positions implicated in aerosol transmission between ferrets. Amino acid variants detected our cohort were often found more frequently in available H5N1 sequences of human than avian isolates. We additionally identified previously unreported amino acid variants and multiple variants that increased in proportion over time in available sequential samples. Given the importance of the polymerase complex for host adaptation, we tested 121 amino acid variants found in the PB2, PB1 and PA subunits for their effects on polymerase activity in human cells. We identified multiple single amino acid variants in all three polymerase subunits that substantially increase polymerase activity including some with effects comparable to that of the widely recognized adaption and virulence marker PB2-E627 K. These results indicate highly dynamic evolutionary processes during human H5N1 virus infection and the potential existence of previously undocumented adaptive pathways.

Individual immune selection pressure has limited impact on seasonal influenza virus evolution.

Abstract: Seasonal influenza viruses are subjected to strong selection as seen by the sequential replacement of existing viral populations on the emergence of new antigenic variants. However, the process of within-host de novo mutant generation and evolutionary selection that underlies these antigenic sweeps is poorly understood. Here, we investigate mutational patterns between evolutionarily closely related human seasonal influenza viruses using host age as a proxy for immune experience. The systematic analysis of >25,000 virus sequences showed that individuals with substantially differing immune histories were frequently (30–62%) infected by viruses with identical amino acid sequences. Viruses from immunologically inexperienced individuals were as likely to possess substitutions with potential phenotypic relevance as highly experienced individuals. Mutations likely to cause antigenic changes were rare among closely related viruses and not associated with extent of host immune experience. These findings suggest that individual immune positive selection plays a limited role in the evolution of seasonal influenza viruses.

Inferring putative transmission clusters with Phydelity.

Abstract: Current phylogenetic clustering approaches for identifying pathogen transmission clusters are limited by their dependency on arbitrarily defined genetic distance thresholds for within-cluster divergence. Incomplete knowledge of a pathogen's underlying dynamics often reduces the choice of distance threshold to an exploratory, ad hoc exercise that is difficult to standardise across studies. Phydelity is a new tool for the identification of transmission clusters in pathogen phylogenies. It identifies groups of sequences that are more closely related than the ensemble distribution of the phylogeny under a statistically principled and phylogeny-informed framework, without the introduction of arbitrary distance thresholds. Relative to other distance threshold- and model-based methods, Phydelity outputs clusters with higher purity and lower probability of misclassification in simulated phylogenies. Applying Phydelity to empirical datasets of hepatitis B and C virus infections showed that Phydelity identified clusters with better correspondence to individuals that are more likely to be linked by transmission events relative to other widely used non-parametric phylogenetic clustering methods without the need for parameter calibration. Phydelity is generalisable to any pathogen and can be used to identify putative direct transmission events. Phydelity is freely available at https://github.com/alvinxhan/Phydelity.

Influenza A Hemagglutinin Passage Bias Sites and Host Specificity Mutations

Abstract: Animal studies aimed at understanding influenza virus mutations that change host specificity to adapt to replication in mammalian hosts are necessarily limited in sample numbers due to high cost and safety requirements. As a safe, higher-throughput alternative, we explore the possibility of using readily available passage bias data obtained mostly from seasonal H1 and H3 influenza strains that were differentially grown in mammalian (MDCK) and avian cells (eggs). Using a statistical approach over 80,000 influenza hemagglutinin sequences with passage information, we found that passage bias sites are most commonly found in three regions: (i) the globular head domain around the receptor binding site, (ii) the region that undergoes pH-dependent structural changes and (iii) the unstructured N-terminal region harbouring the signal peptide. Passage bias sites were consistent among different passage cell types as well as between influenza A subtypes. We also find epistatic interactions of site pairs supporting the notion of host-specific dependency of mutations on virus genomic background. The sites identified from our large-scale sequence analysis substantially overlap with known host adaptation sites in the WHO H5N1 genetic changes inventory suggesting information from passage bias can provide candidate sites for host specificity changes to aid in risk assessment for emerging strains.

Disparate evolution of virus populations in upper and lower airways of mechanically ventilated patients

Abstract: In routine surveillance and diagnostic testing, influenza virus samples are typically collected only from the upper respiratory tract (URT) due to the invasiveness of sample collection from the lower airways. Very little is known about virus variation in the lower respiratory tract (LRT) and it remains unclear if the virus populations at different sites of the human airways may develop to have divergent genetic signatures. We used deep sequencing of serially obtained matched nasopharyngeal swabs and endotracheal aspirates from four mechanically ventilated patients with influenza A/H3N2 infections. A physical barrier separating both compartments of the respiratory tract introduced as part of the medical procedures enabled us to track and compare the genetic composition of the virus populations during isolated evolution in the same host. Amino acid variants reaching majority proportions emerged during the course of infection in both nasopharyngeal swabs and endotracheal aspirates, and amino acid variation was observed in all influenza virus proteins. Genetic variation of the virus populations differed between the URT and LRT and variants were frequently uniquely present in either URT or LRT virus populations of a patient. These observations indicate that virus populations in spatially distinct parts of the human airways may follow different evolutionary trajectories. Selectively sampling from the URT may therefore fail to detect potentially important emerging variants. Importance Influenza viruses are rapidly mutating pathogens that easily adapt to changing environments. Although advances in sequencing technology make it possible to identify virus variants at very low proportions of the within-host virus population, several aspects of intrahost viral evolution have not been studied because sequentially collected samples and samples from the lower respiratory tract are not routinely obtained for influenza surveillance or clinical diagnostic purposes. Importantly, how virus populations evolve in different parts of the human respiratory tract remains unknown. Here we used serial clinical specimens collected from mechanically ventilated influenza patients to compare how virus populations develop in the upper and lower respiratory tract. We show that virus populations in the upper and lower respiratory tract may evolve along distinct evolutionary pathways, and that current sampling and surveillance regimens likely capture only part of the complete intrahost virus variation.

Cellular reproduction number, generation time and growth rate differ between human- and avian-adapted influenza strains

Abstract: When analysing in vitro data, growth kinetics of influenza strains are often compared by computing their growth rates, which are sometimes used as proxies for fitness. However, analogous to mechanistic epidemic models, the growth rate can be defined as a function of two parameters: the basic reproduction number (the average number of cells each infected cell infects) and the mean generation time (the average length of a replication cycle). Using a mechanistic model, previously published data from experiments in human lung cells, and newly generated data, we compared estimates of all three parameters for six influenza A strains. Using previously published data, we found that the two human-adapted strains (pre-2009 seasonal H1N1, and pandemic H1N1) had a lower basic reproduction number, shorter mean generation time and slower growth rate than the two avian-adapted strains (H5N1 and H7N9). These same differences were then observed in data from new experiments where two strains were engineered to have different internal proteins (pandemic H1N1 and H5N1), but the same surface proteins (PR8), confirming our initial findings and implying that differences between strains were driven by internal genes. Also, the model predicted that the human-adapted strains underwent more replication cycles than the avian-adapted strains by the time of peak viral load, potentially accumulating mutations more quickly. These results suggest that the in vitro reproduction number, generation time and growth rate differ between human-adapted and avian-adapted influenza strains, and thus could be used to assess host adaptation of internal proteins to inform pandemic risk assessment.