The early molecular epidemiology of the swine-origin A/H1N1 human influenza pandemic

Spillover: Mechanisms, Genetic Barriers, and the Role of Reservoirs in Emerging Pathogens

Viral spillover represents the transmission of pathogen viruses from one species to another that can give rise to an outbreak. It is a critical concept that has gained increasing attention, particularly after the SARS-CoV-2 pandemic. However, the term is often used inaccurately to describe events that do not meet the true definition of spillover. This review aims to clarify the proper use of the term and provides a detailed analysis of the mechanisms driving zoonotic spillover, with a focus on the genetic and environmental factors that enable viruses to adapt to new hosts. Key topics include viral genetic variability in reservoir species, biological barriers to cross-species transmission, and the factors that influence viral adaptation and spread in novel hosts. The review also examines the role of evolutionary processes such as mutation and epistasis, alongside ecological conditions that facilitate the emergence of new pathogens. Ultimately, it underscores the need for more accurate predictive models and improved surveillance to better anticipate and mitigate future spillover events.

Genetic and potential antigenic evolution of influenza A(H1N1)pdm09 viruses circulating in Kenya during 2009-2018 influenza seasons

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines.

Purifying Selection Determines the Short-Term Time Dependency of Evolutionary Rates in SARS-CoV-2 and pH1N1 Influenza

High-throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Crucially, there are an increasing number of molecular clock analyses using external evolutionary rate priors to infer evolutionary parameters. However, it is not clear which rate prior is appropriate for a given time window of observation due to the time-dependent nature of evolutionary rate estimates. Here, we characterize the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and data set sizes affect the accuracy of parameter estimation. We further use a generalized McDonald-Kreitman test to estimate the number of segregating nonneutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ∼50% and ∼100%, respectively, over the course of 1 year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2-4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating nonneutral sites, demonstrating the role of purifying selection in generating the time dependency of evolutionary parameters during pandemics.

Characterizing the Countrywide Epidemic Spread of Influenza A(H1N1)pdm09 Virus in Kenya between 2009 and 2018

The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Environment, phylogeny, and photosynthetic pathway as determinants of leaf traits in savanna and forest graminoid species in central Brazil

Leaf traits are closely linked to plant responses to the environment and can provide important information on adaptation and evolution. These traits may also result from common ancestry, so phylogenetic relationships also play an important role in adaptive evolution. We evaluated the effects of the closed forest environment (gallery forest) and the open savanna environment (cerrado) on the selection of leaf traits of graminoid species. The two plant communities differ in light, nutrients, and water availability, which are important drivers in the selection and differentiation of these traits. We also investigated the functional structure and the role of phylogeny in the functional organization of species, considering leaf traits. Patterns of leaf trait variation differed between forest and savanna species suggesting habitat specialization. Wider and longer leaves, with higher values of specific leaf area, chlorophyll, and nitrogen, seem to be an advantage for graminoid species growing in forest environments, while thicker leaves, with higher values of leaf dry-matter content and carbon, benefit species growing in savanna environments. We found few phylogenetic signals related to leaf traits in each environment. Therefore, the functional similarity that the gallery forest and cerrado graminoid species share within their group is independent of their phylogenetic proximity. Environmental filters affect the functional structure of communities differently, generating communities with trait values that are more distant than expected by chance in cerrado (functional dispersion), and closer than expected by chance in the gallery forest (functional convergence).

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador

Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.

Log Transformation Improves Dating of Phylogenies

Phylogenetic trees inferred from sequence data often have branch lengths measured in the expected number of substitutions and therefore, do not have divergence times estimated. These trees give an incomplete view of evolutionary histories since many applications of phylogenies require time trees. Many methods have been developed to convert the inferred branch lengths from substitution unit to time unit using calibration points, but none is universally accepted as they are challenged in both scalability and accuracy under complex models. Here, we introduce a new method that formulates dating as a nonconvex optimization problem where the variance of log-transformed rate multipliers is minimized across the tree. On simulated and real data, we show that our method, wLogDate, is often more accurate than alternatives and is more robust to various model assumptions.

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador

Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.

Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19

We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8–6.9 days, serial interval 4.0–7.5 days, and doubling time 2.3–7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available.

Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

Influenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography

Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25 per cent of the taxa had different amounts of sampling uncertainty including 10, 30, and 50 per cent uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10, 30, and 50); (ii) assigned the remaining uncertainty to just one other location; thus ‘splitting’ the uncertainty among two locations (i.e. 10/90, 30/70, and 50/50); and (iii) eliminated uncertainty via two predefined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard (RS) in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25 per cent of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the RS than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005–0.047 for scenarios with sampling uncertainty—(i) and (ii) above—versus a range of 0.063–0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the RS on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a web interface for researchers to use our language processing methods to find and assign uncertainty to alternative potential locations for virus phylogeography.

Tracking virus outbreaks in the twenty-first century

Emerging viruses have the potential to impose substantial mortality, morbidity and economic burdens on human populations. Tracking the spread of infectious diseases to assist in their control has traditionally relied on the analysis of case data gathered as the outbreak proceeds. Here, we describe how many of the key questions in infectious disease epidemiology, from the initial detection and characterization of outbreak viruses, to transmission chain tracking and outbreak mapping, can now be much more accurately addressed using recent advances in virus sequencing and phylogenetics. We highlight the utility of this approach with the hypothetical outbreak of an unknown pathogen, ‘Disease X’, suggested by the World Health Organization to be a potential cause of a future major epidemic. We also outline the requirements and challenges, including the need for flexible platforms that generate sequence data in real-time, and for these data to be shared as widely and openly as possible.

This Review Article describes how recent advances in viral genome sequencing and phylogenetics have enabled key issues associated with outbreak epidemiology to be more accurately addressed, and highlights the requirements and challenges for generating, sharing and using such data when tackling a viral outbreak.

Capturing variation in Lens (Fabaceae): Development and utility of an exome capture array for lentil

PREMISE OF THE STUDY

Lentil is an important legume crop with reduced genetic diversity caused by domestication bottlenecks. Due to its large and complex genome, tools for reduced representation sequencing are needed. We developed an exome capture array for use in various genetic diversity studies.

METHODS

Based on the CDC Redberry draft genome, we developed an exome capture array using multiple sources of transcript resources. The probes were designed to target not only the cultivated lentil, but also wild species. We assessed the utility of the developed method by applying the generated data set to population structure and phylogenetic analyses.

RESULTS

The data set includes 16 wild lentils and 22 cultivar accessions of lentil. Alignment rates were over 90%, and the genic regions were well represented in the capture array. After stringent filtering, 6.5 million high-quality variants were called, and the data set was used to assess the interspecific relationships within the genus Lens.

DISCUSSION

The developed exome capture array provides large amounts of genomic data to be used in many downstream analyses. The method will have useful applications in marker-assisted breeding programs aiming to improve the quality of cultivated lentil.

Evolutionary history of genus Macrobrachium inferred from mitochondrial markers: a molecular clock approach

Caridea, an infraorder of shrimps coming under Pleocyemata was first reported from the oceans before 417 million years followed by their radiation recorded during the Permian period. Hitherto, about 3877 extant caridean species were accounted within which one quarter constitute freshwater species. Freshwater prawns of genus Macrobrachium (Infraorder Caridea; Family Palaemonidae), with more than 240 species are inhabitants of diverse aquatic habitats like coastal lagoons, lakes, tropical streams, ponds and rivers. Previous studies on Macrobrachium relied on the highly variable morphological characters which were insufficient for accurate diagnosis of natural species groups. Present study focuses on the utility of molecular markers (viz. COI and 16S rRNA) for resolving the evolutionary history of genus Macrobrachium using a combination of phylogeny and timescale components. It is for the first time a molecular clock approach had been carried out towards genus Macrobrachium in a broad aspect with the incorporation of congeners inhabiting diverse geographical realms including endemic species M. striatum from South West coast of India. Molecular results obtained revealed the phylogenetic relationships between congeners of genus Macrobrachium at intra/inter-continental level along with the corresponding evolutionary time estimates.

When are pathogen genome sequences informative of transmission events?

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.

The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events in an infectious disease outbreak. However, such methodologies rely on pathogen genomes mutating rapidly enough to discriminate between infected individuals, an assumption that remains to be investigated. To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs. We characterise transmission divergence of ten major outbreak causing pathogens using simulations and find significant variation between diseases, with viral outbreaks generally exhibiting higher transmission divergence than bacterial ones. We reconstruct these outbreaks using the R-packages outbreaker and phybreak and find that genetic sequence data, though useful for rapidly evolving pathogens, provides little to no information about outbreaks with low transmission divergence, such as Streptococcus pneumoniae and Shigella sonnei. Our results demonstrate the need to incorporate other sources of outbreak data, such as contact tracing data and spatial location data, into outbreak reconstruction tools.

Protective effect of a polyvalent influenza DNA vaccine in pigs

BACKGROUND

Influenza A virus in swine herds represents a major problem for the swine industry and poses a constant threat for the emergence of novel pandemic viruses and the development of more effective influenza vaccines for pigs is desired. By optimizing the vector backbone and using a needle-free delivery method, we have recently demonstrated a polyvalent influenza DNA vaccine that induces a broad immune response, including both humoral and cellular immunity.

OBJECTIVES

To investigate the protection of our polyvalent influenza DNA vaccine approach in a pig challenge study.

METHODS

By intradermal needle-free delivery to the skin, we immunized pigs with two different doses (500μg and 800μg) of an influenza DNA vaccine based on six genes of pandemic origin, including internally expressed matrix and nucleoprotein and externally expressed hemagglutinin and neuraminidase as previously demonstrated. Two weeks following immunization, the pigs were challenged with the 2009 pandemic H1N1 virus.

RESULTS

When challenged with 2009 pandemic H1N1, 0/5 vaccinated pigs (800μg DNA) became infected whereas 5/5 unvaccinated control pigs were infected. The pigs vaccinated with the low dose (500μg DNA) were only partially protected. The DNA vaccine elicited binding-, hemagglutination inhibitory (HI) - as well as cross-reactive neutralizing antibody activity and neuraminidase inhibiting antibodies in the immunized pigs, in a dose-dependent manner.

CONCLUSION

The present data, together with the previously demonstrated immunogenicity of our influenza DNA vaccine, indicate that naked DNA vaccine technology provides a strong approach for the development of improved pig vaccines, applying realistic low doses of DNA and a convenient delivery method for mass vaccination.

Fishes in the desert: mitochondrial variation and phylogeography of Danakilia (Actinopterygii: Cichlidae) and Aphanius (Actinopterygii: Cyprinodontidae) in the Danakil Depression of northeastern Africa

The Danakil Depression in northeastern Africa represents one of the harshest arid environments on Earth, yet two genera of fishes, Danakilia (Cichlidae) and Aphanius (Cyprinodontidae), share its sparse aquatic habitats. The evolutionary history of these fishes is investigated here in the context of genetic, geological and paleoenvironmental information. We collected samples from seven sites and assessed phylogeographic relationships using concatenated COI and cytb mtDNA genes. Danakilia morphospecies show low differentiation at mitochondrial markers, but variation is partitioned between a northern cluster containing D. dinicolai plus three undescribed riverine populations, and a southern cluster including two creek populations of D. franchettii separated by the hypersaline waters of Lake Afrera. Aphanius displayed four genetically distinct clades (A. stiassnyae in Lake Afrera; one distributed across the entire area; one in Lake Abaeded; and one in the Shukoray River), but without clear large-scale geographic structure. However, Danakil Aphanius are clearly differentiated from A. dispar sensu stricto from the Sinai Peninsula. Geological evidence suggests that after the Late Pleistocene closure of the Danakil-Red Sea connection, increased post-glacial groundwater availability caused the formation of a brackish paleo-lake flooding the entire region below the -50 m contour. Fish populations previously isolated in coastal oases during glaciation were able to mix in the paleo-lake. Subsequently, in a more arid phase starting ∼7300 BP, paleo-lake regression isolated fishes in separate drainages, triggering their still ongoing diversification.

Evidence of increasing diversification of Zika virus strains isolated in the American continent

Zika virus (ZIKV) is a member of the family Flaviviridae. ZIKV emerged in Brazil in 2015, causing an unprecedented epidemic and since then the virus has rapidly spread throughout the Americas. These facts highlight the need of detailed phylogenetic studies to understand the emergence, spread, and evolution of ZIKV populations. For these reasons, a Bayesian coalescent Markov Chain Monte Carlo analysis of complete genome sequences of ZIKV strains recently isolated in the American continent was performed. The results of these studies revealed an increasing diversification of ZIKV strains in different genetic lineages and co-circulation of distinct genetic lineages in several countries in the region. The time of the most recent common ancestor (tMRCA) was established to be around February 20, 2014 for ZIKV strains circulating in the American region. A mean rate of evolution of 1.55 × 10^-3 substitutions/site/year was obtained for ZIKV strains included in this study. A Bayesian skyline plot indicate a sharp increase in population size from February 2014 to July 2015 and a decline during 2016. These results are discussed in terms of the emergence and evolution of ZIKV populations in the American continent.

Multiple Genome Constellations of Similar and Distinct Influenza A Viruses Co-Circulate in Pigs During Epidemic Events

Swine play a key role in the ecology and transmission of influenza A viruses (IAVs) between species. However, the epidemiology and diversity of swine IAVs is not completely understood. In this cohort study, we sampled on a weekly basis 132 3-week old pigs for 15 weeks. We found two overlapping epidemic events of infection in which most pigs (98.4%) tested PCR positive for IAVs. The prevalence rate of infection ranged between 0 and 86% per week and the incidence density ranged between 0 and 71 cases per 100 pigs-week. Three distinct influenza viral groups (VGs) replicating as a "swarm" of viruses were identified (swine H1-gamma, H1-beta, and H3-cluster-IV IAVs) and co-circulated at different proportions over time suggesting differential allele fitness. Furthermore, using deep genome sequencing 13 distinct viral genome constellations were differentiated. Moreover, 78% of the pigs had recurrent infections with IAVs closely related to each other or IAVs clearly distinct. Our results demonstrated the molecular complexity of swine IAVs during natural infection of pigs in which novel strains of IAVs with zoonotic and pandemic potential can emerge. These are key findings to design better health interventions to reduce the transmission of swine IAVs and minimize the public health risk.

Complete Genome Sequencing of Influenza A Viruses within Swine Farrow-to-Wean Farms Reveals the Emergence, Persistence, and Subsidence of Diverse Viral Genotypes

Influenza A viruses (IAVs) are endemic in swine and represent a public health risk. However, there is limited information on the genetic diversity of swine IAVs within farrow-to-wean farms, which is where most pigs are born. In this longitudinal study, we sampled 5 farrow-to-wean farms for a year and collected 4,190 individual nasal swabs from three distinct pig subpopulations. Of these, 207 (4.9%) samples tested PCR positive for IAV, and 124 IAVs were isolated. We sequenced the complete genomes of 123 IAV isolates and found 31 H1N1, 26 H1N2, 63 H3N2, and 3 mixed IAVs. Based on the IAV hemagglutinin, seven different influenza A viral groups (VGs) were identified. Most of the remaining IAV gene segments allowed us to differentiate the same VGs, although an additional viral group was identified for gene segment 3 (PA). Moreover, the codetection of more than one IAV VG was documented at different levels (farm, subpopulation, and individual pigs), highlighting the environment for potential IAV reassortment. Additionally, 3 out of 5 farms contained IAV isolates (n = 5) with gene segments from more than one VG, and 79% of all the IAVs sequenced contained a signature mutation (S31N) in the matrix gene that has been associated with resistance to the antiviral amantadine. Within farms, some IAVs were detected only once, while others were detected for 283 days. Our results illustrate the maintenance and subsidence of different IAVs within swine farrow-to-wean farms over time, demonstrating that pig subpopulation dynamics are important to better understand the diversity and epidemiology of swine IAVs.

IMPORTANCE On a global scale, swine are one of the main reservoir species for influenza A viruses (IAVs) and play a key role in the transmission of IAVs between species. Additionally, the 2009 IAV pandemics highlighted the role of pigs in the emergence of IAVs with pandemic potential. However, limited information is available regarding the diversity and distribution of swine IAVs on farrow-to-wean farms, where novel IAVs can emerge. In this study, we studied 5 swine farrow-to-wean farms for a year and characterized the genetic diversity of IAVs among three different pig subpopulations commonly housed on this type of farm. Using next-generation-sequencing technologies, we demonstrated the complex distribution and diversity of IAVs among the pig subpopulations studied. Our results demonstrated the dynamic evolution of IAVs within farrow-to-wean farms, which is crucial to improve health interventions to reduce the risk of transmission between pigs and from pigs to people.

Molecular Evidence of Transmission of Influenza A/H1N1 2009 on a University Campus

Background

In the recent years, the data on the molecular epidemiology of influenza viruses have expanded enormously because of the availability of cutting-edge sequencing technologies. However, much of the information is from the temperate regions with few studies from tropical regions such as South-east Asia. Despite the fact that influenza has been known to transmit rapidly within semi-closed communities, such as military camps and educational institutions, data are limited from these communities.

Objectives

To determine the phylogeography of influenza viruses on a university campus, we examined the spatial distribution of influenza virus on the National University of Singapore (NUS) campus.

Methods

Consenting students from the NUS who sought medical attention at the UHC provided two nasopharyngeal swabs and demographic data. PCR was used for detection of influenza viruses. 34 full-genomes of pH1N1/09 viruses were successfully sequenced by Sanger method and concatenated using Geneious R7. Phylogenetic analysis was conducted using these 34 sequences and 1518 global sequences. Phylogeographic analysis was done using BaTS software and Association index and Fitch parsimony scores were determined.

Results

Integrating whole genome sequencing data with epidemiological data, we found strong evidence of influenza transmission on campus as isolates from students residing on-campus were highly similar to each other (AI, P value = 0.009; PS, P value = 0.04). There was also evidence of multiple introductions from the community.

Conclusions

Such data are useful in formulating pandemic preparedness plans which can use these communities as sentinel sites for detection and monitoring of emerging respiratory viral infections.

Genomic Analysis of the Emergence, Evolution, and Spread of Human Respiratory RNA Viruses

The emergence and reemergence of rapidly evolving RNA viruses-particularly those responsible for respiratory diseases, such as influenza viruses and coronaviruses-pose a significant threat to global health, including the potential of major pandemics. Importantly, recent advances in high-throughput genome sequencing enable researchers to reveal the genomic diversity of these viral pathogens at much lower cost and with much greater precision than they could before. In particular, the genome sequence data generated allow inferences to be made on the molecular basis of viral emergence, evolution, and spread in human populations in real time. In this review, we introduce recent computational methods that analyze viral genomic data, particularly in combination with metadata such as sampling time, geographic location, and virulence. We then outline the insights these analyses have provided into the fundamental patterns and processes of evolution and emergence in human respiratory RNA viruses, as well as the major challenges in such genomic analyses.

MERS-CoV recombination: implications about the reservoir and potential for adaptation

Recombination is a process that unlinks neighboring loci allowing for independent evolutionary trajectories within genomes of many organisms. If not properly accounted for, recombination can compromise many evolutionary analyses. In addition, when dealing with organisms that are not obligately sexually reproducing, recombination gives insight into the rate at which distinct genetic lineages come into contact. Since June 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has caused 1,106 laboratory-confirmed infections, with 421 MERS-CoV-associated deaths as of 16 April 2015. Although bats are considered as the likely ultimate source of zoonotic betacoronaviruses, dromedary camels have been consistently implicated as the source of current human infections in the Middle East. In this article, we use phylogenetic methods and simulations to show that MERS-CoV genome has likely undergone numerous recombinations recently. Recombination in MERS-CoV implies frequent co-infection with distinct lineages of MERS-CoV, probably in camels given the current understanding of MERS-CoV epidemiology.

Phylogenetic Exploration of Nosocomial Transmission Chains of 2009 Influenza A/H1N1 among Children Admitted at Red Cross War Memorial Children's Hospital, Cape Town, South Africa in 2011

Traditional modes of investigating influenza nosocomial transmission have entailed a combination of confirmatory molecular diagnostic testing and epidemiological investigation. Common hospital-acquired infections like influenza require a discerning ability to distinguish between viral isolates to accurately identify patient transmission chains. We assessed whether influenza hemagglutinin sequence phylogenies can be used to enrich epidemiological data when investigating the extent of nosocomial transmission over a four-month period within a paediatric Hospital in Cape Town South Africa. Possible transmission chains/channels were initially determined through basic patient admission data combined with Maximum likelihood and time-scaled Bayesian phylogenetic analyses. These analyses suggested that most instances of potential hospital-acquired infections resulted from multiple introductions of Influenza A into the hospital, which included instances where virus hemagglutinin sequences were identical between different patients. Furthermore, a general inability to establish epidemiological transmission linkage of patients/viral isolates implied that identified isolates could have originated from asymptomatic hospital patients, visitors or hospital staff. In contrast, a traditional epidemiological investigation that used no viral phylogenetic analyses, based on patient co-admission into specific wards during a particular time-frame, suggested that multiple hospital acquired infection instances may have stemmed from a limited number of identifiable index viral isolates/patients. This traditional epidemiological analysis by itself could incorrectly suggest linkage between unrelated cases, underestimate the number of unique infections and may overlook the possible diffuse nature of hospital transmission, which was suggested by sequencing data to be caused by multiple unique introductions of influenza A isolates into individual hospital wards. We have demonstrated a functional role for viral sequence data in nosocomial transmission investigation through its ability to enrich traditional, non-molecular observational epidemiological investigation by teasing out possible transmission pathways and working toward more accurately enumerating the number of possible transmission events.

Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria

Background

Phylogeography improves our understanding of spatial epidemiology. However, application to practical problems requires choices among computational tools to balance statistical rigor, computational complexity, sensitivity to sampling strategy and interpretability.

Methods

We introduce a fast, heuristic algorithm to reconstruct partially-observed transmission networks (POTN) that combines features of phylogenetic and transmission tree approaches. We compare the transmission network generated by POTN with existing algorithms (BEAST and SeqTrack), and discuss the benefits and challenges of phylogeographic analysis on examples of epidemic and endemic diseases: Ebola virus, H1N1 pandemic influenza and polio.

Results

For the 2014 Sierra Leone Ebola virus outbreak and the 2009 H1N1 outbreak, all three methods provide similarly plausible transmission histories but differ in detail. For polio in northern Nigeria, we discuss performance trade-offs between the POTN and discrete phylogeography in BEAST and conclude that spatial history reconstruction is limited by under-sampling.

Conclusions

POTN is complementary to available tools on densely-sampled data, fails gracefully on under-sampled data and is scalable to accommodate larger datasets. We provide further evidence for the utility of phylogeography for understanding transmission networks of rapidly evolving epidemics. We propose simple heuristic criteria to identify how sampling rates and disease dynamics interact to determine fundamental limitations of phylogeographic inference.

Genome plasticity of triple-reassortant H1N1 influenza A virus during infection of vaccinated pigs

To gain insight into the evolution of influenza A viruses (IAVs) during infection of vaccinated pigs, we experimentally infected a 3-week-old naive pig with a triple-reassortant H1N1 IAV and placed the seeder pig in direct contact with a group of age-matched vaccinated pigs (n = 10). We indexed the genetic diversity and evolution of the virus at an intra-host level by deep sequencing the entire genome directly from nasal swabs collected at two separate samplings during infection. We obtained 13 IAV metagenomes from 13 samples, which included the virus inoculum and two samples from each of the six pigs that tested positive for IAV during the study. The infection produced a population of heterogeneous alleles (sequence variants) that was dynamic over time. Overall, 794 polymorphisms were identified amongst all samples, which yielded 327 alleles, 214 of which were unique sequences. A total of 43 distinct haemagglutinin proteins were translated, two of which were observed in multiple pigs, whereas the neuraminidase (NA) was conserved and only one dominant NA was found throughout the study. The genetic diversity of IAVs changed dynamically within and between pigs. However, most of the substitutions observed in the internal gene segments were synonymous. Our results demonstrated remarkable IAV diversity, and the complex, rapid and dynamic evolution of IAV during infection of vaccinated pigs that can only be appreciated with repeated sampling of individual animals and deep sequence analysis.

Fast Dating Using Least-Squares Criteria and Algorithms

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley–Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley–Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3.

Phylodynamics of H1N1/2009 influenza reveals the transition from host adaptation to immune-driven selection

Influenza A H1N1/2009 virus that emerged from swine rapidly replaced the previous seasonal H1N1 virus. Although the early emergence and diversification of H1N1/2009 is well characterized, the ongoing evolutionary and global transmission dynamics of the virus remain poorly investigated. To address this we analyse >3,000 H1N1/2009 genomes, including 214 full genomes generated from our surveillance in Singapore, in conjunction with antigenic data. Here we show that natural selection acting on H1N1/2009 directly after introduction into humans was driven by adaptation to the new host. Since then, selection has been driven by immunological escape, with these changes corresponding to restricted antigenic diversity in the virus population. We also show that H1N1/2009 viruses have been subject to regular seasonal bottlenecks and a global reduction in antigenic and genetic diversity in 2014.

In Silico Identification of Highly Conserved Epitopes of Influenza A H1N1, H2N2, H3N2, and H5N1 with Diagnostic and Vaccination Potential

The unpredictable, evolutionary nature of the influenza A virus (IAV) is the primary problem when generating a vaccine and when designing diagnostic strategies; thus, it is necessary to determine the constant regions in viral proteins. In this study, we completed an in silico analysis of the reported epitopes of the 4 IAV proteins that are antigenically most significant (HA, NA, NP, and M2) in the 3 strains with the greatest world circulation in the last century (H1N1, H2N2, and H3N2) and in one of the main aviary subtypes responsible for zoonosis (H5N1). For this purpose, the HMMER program was used to align 3,016 epitopes reported in the Immune Epitope Database and Analysis Resource (IEDB) and distributed in 34,294 stored sequences in the Pfam database. Eighteen epitopes were identified: 8 in HA, 5 in NA, 3 in NP, and 2 in M2. These epitopes have remained constant since they were first identified (~91 years) and are present in strains that have circulated on 5 continents. These sites could be targets for vaccination design strategies based on epitopes and/or as markers in the implementation of diagnostic techniques.

Genomic variation in tomato, from wild ancestors to contemporary breeding accessions

BACKGROUND

Domestication modifies the genomic variation of species. Quantifying this variation provides insights into the domestication process, facilitates the management of resources used by breeders and germplasm centers, and enables the design of experiments to associate traits with genes. We described and analyzed the genetic diversity of 1,008 tomato accessions including Solanum lycopersicum var. lycopersicum (SLL), S. lycopersicum var. cerasiforme (SLC), and S. pimpinellifolium (SP) that were genotyped using 7,720 SNPs. Additionally, we explored the allelic frequency of six loci affecting fruit weight and shape to infer patterns of selection.

RESULTS

Our results revealed a pattern of variation that strongly supported a two-step domestication process, occasional hybridization in the wild, and differentiation through human selection. These interpretations were consistent with the observed allele frequencies for the six loci affecting fruit weight and shape. Fruit weight was strongly selected in SLC in the Andean region of Ecuador and Northern Peru prior to the domestication of tomato in Mesoamerica. Alleles affecting fruit shape were differentially selected among SLL genetic subgroups. Our results also clarified the biological status of SLC. True SLC was phylogenetically positioned between SP and SLL and its fruit morphology was diverse. SLC and "cherry tomato" are not synonymous terms. The morphologically-based term "cherry tomato" included some SLC, contemporary varieties, as well as many admixtures between SP and SLL. Contemporary SLL showed a moderate increase in nucleotide diversity, when compared with vintage groups.

CONCLUSIONS

This study presents a broad and detailed representation of the genomic variation in tomato. Tomato domestication seems to have followed a two step-process; a first domestication in South America and a second step in Mesoamerica. The distribution of fruit weight and shape alleles supports that domestication of SLC occurred in the Andean region. Our results also clarify the biological status of SLC as true phylogenetic group within tomato. We detect Ecuadorian and Peruvian accessions that may represent a pool of unexplored variation that could be of interest for crop improvement.

Time dependence of evolutionary metrics during the 2009 pandemic influenza virus outbreak

With the expansion of DNA sequencing technology, quantifying evolution in emerging viral outbreaks has become an important tool for scientists and public health officials. Although it is known that the degree of sequence divergence significantly affects the calculation of evolutionary metrics in viral outbreaks, the extent and duration of this effect during an actual outbreak remains unclear. We have analyzed how limited divergence time during an early viral outbreak affects the accuracy of molecular evolutionary metrics. Using sequence data from the first 25 months of the 2009 pandemic H1N1 (pH1N1) outbreak, we calculated each of three different standard evolutionary metrics-molecular clock rate (i.e., evolutionary rate), whole gene dN/dS, and site-wise dN/dS-for hemagglutinin and neuraminidase, using increasingly longer time windows, from 1 month to 25 months. For the molecular clock rate, we found that at least three to four months of temporal divergence from the start of sampling was required to make precise estimates that also agreed with long-term values. For whole gene dN/dS, we found that at least two months of data were required to generate precise estimates, but six to nine months were required for estimates to approach their long term values. For site-wise dN/dS estimates, we found that at least six months of sampling divergence was required before the majority of sites had at least one mutation and were thus evolutionarily informative. Furthermore, eight months of sampling divergence was required before the site-wise estimates appropriately reflected the distribution of values expected from known protein-structure-based evolutionary pressure in influenza. In summary, we found that evolutionary metrics calculated from gene sequence data in early outbreaks should be expected to deviate from their long-term estimates for at least several months after the initial emergence and sequencing of the virus.

Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models

Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman’s coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent susceptible–infected–removed (SIR) tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with a recently published birth–death-sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. Additionally, we analyze influenza A (H1N1) sequence data sampled in the Canterbury region of New Zealand and HIV-1 sequence data obtained from known United Kingdom infection clusters. We show that both coalescent SIR models are effective at estimating epidemiological parameters from data with large fundamental reproductive number R0 and large population size S0. Furthermore, we find that the stochastic variant generally outperforms its deterministic counterpart in terms of error, bias, and highest posterior density coverage, particularly for smaller R0 and S0. However, each of these inference models is shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with R0 close to one or with small effective susceptible populations.

Epidemiologic data and pathogen genome sequences: a powerful synergy for public health

Epidemiologists aim to inform the design of public health interventions with evidence on the evolution, emergence and spread of infectious diseases. Sequencing of pathogen genomes, together with date, location, clinical manifestation and other relevant data about sample origins, can contribute to describing nearly every aspect of transmission dynamics, including local transmission and global spread. The analyses of these data have implications for all levels of clinical and public health practice, from institutional infection control to policies for surveillance, prevention and treatment. This review highlights the range of epidemiological questions that can be addressed from the combination of genome sequence and traditional ‘line lists’ (tables of epidemiological data where each line includes demographic and clinical features of infected individuals). We identify opportunities for these data to inform interventions that reduce disease incidence and prevalence. By considering current limitations of, and challenges to, interpreting these data, we aim to outline a research agenda to accelerate the genomics-driven transformation in public health microbiology.

Antigenic drift of H1N1 influenza A virus in pigs with and without passive immunity

BACKGROUND

The genetic and antigenic characteristics of influenza A viruses (IAV) within and between species change over time due to antigenic shift and drift. Although pigs are known to play a key role in the epidemiology of IAV between species, little is known about the molecular evolution of IAV hemagglutinin (HA) in pigs.

OBJECTIVES

The aim of this study was to evaluate the HA drift of an H1N1 IAV after infecting weaned pigs with or without maternally derived passive immunity.

METHODS

Three- to four-week-old piglets born either to vaccinated or unvaccinated sows were contact-infected upon exposure with an IAV-infected pig. Nasal swabs were collected daily from each pig and tested for IAV by RRT-PCR. Full-length HA sequences were obtained directly from positive nasal swabs and compared between groups.

RESULTS

Synonymous and non-synonymous mutations were detected in pigs with and without passive immunity. Most of the non-synonymous mutations occurred within the HA1 region of the HA. Changes within HA1 region were only identified in antigenic site B in pigs without passive immunity and in antigenic sites A, B, and D in pigs with passive immunity. However, there was no association between the immune status of the pig and the amino acid substitutions observed.

CONCLUSIONS

Overall, we demonstrated that amino acid substitutions within antigenic sites can happen in weaned pigs with or without passive immunity shortly after infection.

The influence of sampling design on species tree inference: a new relationship for the New World chickadees (Aves: Poecile)

In this study, we explore the long-standing issue of how many loci are needed to infer accurate phylogenetic relationships, and whether loci with particular attributes (e.g., parsimony informativeness, variability, gene tree resolution) outperform others. To do so, we use an empirical data set consisting of the seven species of chickadees (Aves: Paridae), an analytically tractable, recently diverged group, and well-studied ecologically but lacking a nuclear phylogeny. We estimate relationships using 40 nuclear loci and mitochondrial DNA using four coalescent-based species tree inference methods (BEST, *BEAST, STEM, STELLS). Collectively, our analyses contrast with previous studies and support a sister relationship between the Black-capped and Carolina Chickadee, two superficially similar species that hybridize along a long zone of contact. Gene flow is a potential source of conflict between nuclear and mitochondrial gene trees, yet we find a significant, albeit low, signal of gene flow. Our results suggest that relatively few loci with high information content may be sufficient for estimating an accurate species tree, but that substantially more loci are necessary for accurate parameter estimation. We provide an empirical reference point for researchers designing sampling protocols with the purpose of inferring phylogenies and population parameters of closely related taxa.

Genetic characterization of influenza virus circulating in Brazilian pigs during 2009 and 2010 reveals a high prevalence of the pandemic H1N1 subtype

BACKGROUND

Influenza A viruses circulating in pigs in Brazil are still not characterized, and only limited data are available about swine influenza epidemiology in the country. Therefore, we characterized the hemagglutinin (HA) and neuraminidase (NA) genes of influenza viruses isolated from Brazilian pigs. We also evaluated one case of probable swine-to-human transmission.

METHODS

Twenty influenza viruses isolated from pigs during 2009-2010 in five Brazilian states (Minas Gerais, Sao Paulo, Parana, Rio Grande do Sul, and Mato Grosso) were used. One human isolate, from a technician who became ill after visiting a swineherd going through a respiratory disease outbreak, was also used in the study. Phylogenetic analysis for the HA and NA genes and hemagglutinin amino acid sequence alignment were performed.

RESULTS

All isolates clustered with pandemic H1N1 2009 (pH1N1) viruses and appeared to have a common ancestor. Genetic diversity was higher in the HA than in the NA gene, and the amino acid substitution S203T in one of HA's antigenic sites was found in most of the samples. The human isolate was more related to swine isolates from the same herd visited by the technician than to other human isolates, suggesting swine-to-human transmission.

CONCLUSION

Our results show that pH1N1 was disseminated and the predominant subtype in Brazilian pigs in 2009-2010.

Real-time characterization of the molecular epidemiology of an influenza pandemic

Early characterization of the epidemiology and evolution of a pandemic is essential for determining the most appropriate interventions. During the 2009 H1N1 influenza A pandemic, public databases facilitated widespread sharing of genetic sequence data from the outset. We use Bayesian phylogenetics to simulate real-time estimates of the evolutionary rate, date of emergence and intrinsic growth rate (r₀) of the pandemic from whole-genome sequences. We investigate the effects of temporal range of sampling and dataset size on the precision and accuracy of parameter estimation. Parameters can be accurately estimated as early as two months after the first reported case, from 100 genomes and the choice of growth model is important for accurate estimation of r₀. This demonstrates the utility of simple coalescent models to rapidly inform intervention strategies during a pandemic.

Parallel screening of wild-type and drug-resistant targets for anti-resistance neuraminidase inhibitors

Infection with influenza virus is a major public health problem, causing serious illness and death each year. Emergence of drug-resistant influenza virus strains limits the effectiveness of drug treatment. Importantly, a dual H275Y/I223R mutation detected in the pandemic influenza A 2009 virus strain results in multidrug resistance to current neuraminidase (NA) drugs. Therefore, discovery of new agents for treating multiple drug-resistant (MDR) influenza virus infections is important. Here, we propose a parallel screening strategy that simultaneously screens wild-type (WT) and MDR NAs, and identifies inhibitors matching the subsite characteristics of both NA-binding sites. These may maintain their potency when drug-resistant mutations arise. Initially, we analyzed the subsite of the dual H275Y/I223R NA mutant. Analysis of the site-moiety maps of NA protein structures show that the mutant subsite has a relatively small volume and is highly polar compared with the WT subsite. Moreover, the mutant subsite has a high preference for forming hydrogen-bonding interactions with polar moieties. These changes may drive multidrug resistance. Using this strategy, we identified a new inhibitor, Remazol Brilliant Blue R (RB19, an anthraquinone dye), which inhibited WT NA and MDR NA with IC(50) values of 3.4 and 4.5 µM, respectively. RB19 comprises a rigid core scaffold and a flexible chain with a large polar moiety. The former interacts with highly conserved residues, decreasing the probability of resistance. The latter forms van der Waals contacts with the WT subsite and yields hydrogen bonds with the mutant subsite by switching the orientation of its flexible side chain. Both scaffolds of RB19 are good starting points for lead optimization. The results reveal a parallel screening strategy for identifying resistance mechanisms and discovering anti-resistance neuraminidase inhibitors. We believe that this strategy may be applied to other diseases with high mutation rates, such as cancer and human immunodeficiency virus type 1.

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.

Unraveling the phylogenetic relationships of the Eccoptochilinae, an enigmatic array of ordovician cheirurid trilobites

The Cheiruridae are a diverse group of trilobites and several subfamilies within the clade have been the focus of recent phylogenetic studies. This paper focuses on the relationships of one of those subfamilies, the Ordovician Eccoptochilinae. We analyze sixteen species from six genera within the traditionally defined group, using the pilekiid Anacheirurus frederici as an outgroup. To assess the monophyly of the Eccoptochilinae seven sphaerexochine species, Kawina arnoldi, Sphaerexochus arenosus, S. atacius, S. latifrons, S. mirus, S. parvus, and S. scabridus were included in the analysis as well. The results of this analysis show that the genus Eccoptochile represents a paraphyletic grade and species traditionally assigned to Parasphaerexochus and Skelipyx plot within Pseudosphaerexochus. Also, representative species of Sphaerexochinae plot within the traditionally defined Eccoptochilinae, suggesting Eccoptochilinae itself is paraphyletic. To resolve this, we propose all species of Pseudosphaerexochus be placed within Sphaerexochinae and Eccoptochilinae be restricted to a monotypic Eccoptochile clavigera.

Characterisation of four major histocompatibility complex class II genes of the koala (Phascolarctos cinereus)

Major histocompatibility complex (MHC) class II molecules have an integral role in the adaptive immune response, as they bind and present antigenic peptides to T helper lymphocytes. In this study of koalas, species-specific primers were designed to amplify exon 2 of the MHC class II DA and DB genes, which contain much of the peptide-binding regions of the α and β chains. A total of two DA α1 domain variants and eight DA β1 (DAB), three DB α1 and five DB β1 variants were amplified from 20 koalas from two free-living populations from South East Queensland and the Port Macquarie region in northern New South Wales. We detected greater variation in the β1 than in the α1 domains as well as evidence of positive selection in DAB. The present study provides a springboard to future investigation of the role of MHC in disease susceptibility in koalas.

Bayesian coalescent analysis of pandemic H1N1 influenza A virus circulating in the South American region

The first influenza pandemic of this century was declared in April of 2009, with the emergence of a novel H1N1 influenza A virus strain (H1N1pdm). Understanding the evolution of H1N1pdm populations within the South American region is essential for studying global diversification, emergence, resistance and vaccine efficacy. In order to gain insight into these matters, we have performed a Bayesian coalescent Markov Chain Monte Carlo analysis of hemagglutinin (HA) and neuraminidase (NA) gene sequences of all available and comparable HA and NA sequences obtained from H1N1pdm IAV circulating in the South American region. High evolutionary rates and fast population growths characterize the population dynamics of H1N1pdm strains in this region of the world. A significant contribution of first codon position to the mean evolutionary rate was found for both genes studied, revealing a high contribution of non-synonymous substitutions to the mean substitution rate. In the 178days period covered by these studies, substitutions in all HA epitope regions can be observed. HA substitutions D239G/N and Q310H have been observed only in Brazilian patients. While substitution D239G/N is not particularly associated to a specific genetic lineage, all strains bearing substitution Q310H were assigned to clade 6, suggesting a founder effect. None of the substitutions found in the NA proteins of H1N1pdm strains isolated in South America appears sufficiently close to affect the drug binding pocket for the three NA inhibitor antivirals tested. A more detailed analysis of NA proteins revealed epitope differences among 2010 vaccine and H1N1pdm IAV strains circulating in the South American region.

Pandemic Influenza A H1N1 (2009) Virus: Lessons from the Past and Implications for the Future

The recent pandemic by novel influenza A (H1N1) 2009 (pH1N1) virus is an emerging viral infection, being of significant international concern and requires intensive research. The virus spread in pandemic proportions, and continues to be in the post-pandemic phase. Since, the pH1N1 is still circulating in the community, monitoring is required during the post-pandemic period. The pH1N1 defied influenza seasonality and rapidly became dominant over the seasonal influenza viruses. This new strain was antigenically different from the seasonal H1N1 influenza strains due to the genetic re-assortment. Surprisingly, this new reassortant virus emerged at the end of influenza season, caused a sudden toll of mild illness and is now co-circulating with the seasonal strains. The recent outbreak of pH1N1 consolidates the fact that a new reassortant virus may have originated in animal reservoirs and got transferred to human who were in close contact with these animals. There is a continued need for multisite surveillance to detect potentially dangerous influenza strains, which may emerge and establish themselves in human population. This review is an attempt to address the lessons learnt from the recent influenza pandemic and the future implications for prevention and control of influenza.

Inferring pandemic growth rates from sequence data

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

Viral genetic sequence variations in pandemic H1N1/2009 and seasonal H3N2 influenza viruses within an individual, a household and a community

BACKGROUND

There are few data in the literature on viral sequence variation between host generations/successive transmission events. Relatively little is known about the sequence heterogeneity of the influenza viruses transmitted within families.

OBJECTIVES

To study the molecular epidemiology of influenza virus and to determine the sequence variation within an individual, a household and a community during the first wave of influenza pandemic in 2009.

STUDY DESIGN

A prospective study of household transmission of influenza A in Hong Kong was conducted during the pandemic in 2009. The HA and NA sequences of pandemic and seasonal influenza A viral isolates identified in this household transmission study were sequences and analyzed.

RESULTS

Our results indicated that there were multiple introductions of influenza viruses into Hong Kong. Sequence analysis of these isolates suggested that members of these family clusters acquired the infection by household transmissions. Interestingly, unlike those concluded from previous household transmission studies, we observed sequence variations between sequential samples from the same person and also within the same household.

CONCLUSIONS

Family clusters of influenza A viral infection are predominantly the result of secondary transmission within a household. Our results also suggested that the intra-host viral sequence variation might be more common that than previously thought.

Influenza A viruses: new research developments

Influenza A viruses are zoonotic pathogens that continuously circulate and change in several animal hosts, including birds, pigs, horses and humans. The emergence of novel virus strains that are capable of causing human epidemics or pandemics is a serious possibility. Here, we discuss the value of surveillance and characterization of naturally occurring influenza viruses, and review the impact that new developments in the laboratory have had on our understanding of the host tropism and virulence of viruses. We also revise the lessons that have been learnt from the pandemic viruses of the past 100 years.