Genetic heterogeneity in the Salmonella Typhi Vi capsule locus: a population genomic study from Fiji.

Typhoid fever is endemic in many parts of the world and remains a major public health concern in tropical and sub-tropical developing nations, including Fiji. To address high rates of typhoid fever, the Northern Division of Fiji implemented a mass vaccination with typhoid conjugate vaccine (Vi-polysaccharide conjugated to tetanus toxoid) as a public health control measure in 2023. In this study we define the genomic epidemiology of Salmonella Typhi in the Northern Division prior to island-wide vaccination, sequencing 85% (n=419) of the total cases from the Northern and Central Divisions of Fiji that occurred in the period 2017-2019. We found elevated rates of nucleotide polymorphisms in the tviD and tviE genes (responsible for Vi-polysaccharide synthesis) relative to core genome levels within the Fiji endemic S. Typhi genotype 4.2. Expansion of these findings within a globally representative database of 12 382 S. Typhi (86 genotyphi clusters) showed evidence of convergent evolution of the same tviE mutations across the S. Typhi population, indicating that tvi selection has occurred both independently and globally. The functional impact of tvi mutations on the Vi-capsular structure and other phenotypic characteristics are not fully elucidated, yet commonly occurring tviE polymorphisms localize adjacent to predicted active site residues when overlayed against the predicted TviE protein structure. Given the central role of the Vi-polysaccharide in S. Typhi biology and vaccination, further integrated epidemiological, genomic and phenotypic surveillance is required to determine the spread and functional implications of these mutations.

Longitudinal genomic analysis of Neisseria gonorrhoeae transmission dynamics in Australia.

N. gonorrhoeae, which causes the sexually transmissible infection gonorrhoea, remains a significant public health threat globally, with challenges posed by increasing transmission and antimicrobial resistance (AMR). The COVID-19 pandemic introduced exceptional circumstances into communicable disease control, impacting the transmission of gonorrhoea and other infectious diseases. Through phylogenomic and phylodynamic analysis of 5881 N. gonorrhoeae genomes from Australia, we investigated N. gonorrhoeae transmission over five years, including a time period during the COVID-19 pandemic. Using a novel cgMLST-based genetic threshold, we demonstrate persistence of large N. gonorrhoeae genomic clusters over several years, with some persistent clusters associated with heterosexual transmission. We observed a decline in both N. gonorrhoeae transmission and genomic diversity during the COVID-19 pandemic, suggestive of an evolutionary bottleneck. The longitudinal, occult transmission of N. gonorrhoeae over many years further highlights the urgent need for improved diagnostic, treatment, and prevention strategies for gonorrhoea.

Genome-wide patterns of selection-drift variation strongly associate with organismal traits across the green plant lineage.

There are many gaps in our knowledge of how life cycle variation and organismal body architecture associate with molecular evolution. Using the diverse range of green algal body architectures and life cycle types as a test case, we hypothesize that increases in cytomorphological complexity are likely to be associated with a decrease in the effective population size, because larger-bodied organisms typically have smaller populations, resulting in increased drift. For life cycles, we expect haploid-dominant lineages to evolve under stronger selection intensity relative to diploid-dominant life cycles owing to masking of deleterious alleles in heterozygotes. We use a genome-scale data set spanning the phylogenetic diversity of green algae and phylogenetic comparative approaches to measure the relative selection intensity across different trait categories. We show stronger signatures of drift in lineages with more complex body architectures compared with unicellular lineages, which we consider to be a consequence of smaller effective population sizes of the more complex algae. Significantly higher rates of synonymous as well as nonsynonymous substitutions relative to other algal body architectures highlight that siphonous and siphonocladous body architectures, characteristic of many green seaweeds, form an interesting test case to study the potential impacts of genome redundancy on molecular evolution. Contrary to expectations, we show that levels of selection efficacy do not show a strong association with life cycle types in green algae. Taken together, our results underline the prominent impact of body architecture on the molecular evolution of green algal genomes.

Characterising HIV-1 transmission in Victoria, Australia: a molecular epidemiological study.

Background: In Australia the incidence of HIV has declined steadily, yet sustained reduction of HIV transmission in this setting requires improved public health responses. As enhanced public health responses and prioritisation of resources may be guided by molecular epidemiological data, here we aimed to assess the applicability of these approaches in Victoria, Australia. Methods: A comprehensive collection of HIV-1 pol sequences from individuals diagnosed with HIV in Victoria, Australia, between January 1st 2000 and December 31st 2020 were deidentified and used as the basis of our assessment. These sequences were subtyped and surveillance drug resistance mutations (SDRMs) identified, before definition of transmission groups was performed using HIV-TRACE (0.4.4). Phylodynamic methods were applied using BEAST (2.6.6), assessing effective reproductive numbers for large groups, and additional demographic data were integrated to provide a high resolution view of HIV transmission in Victoria on a decadal time scale. Findings: Based on standard settings for HIV-TRACE, 70% (2438/3507) of analysed HIV-1 pol sequences were readily assigned to a transmission group. Individuals in transmission groups were more commonly males (aOR 1.50), those born in Australia (aOR 2.13), those with probable place of acquisition as Victoria (aOR 6.73), and/or those reporting injectable drug use (aOR 2.13). SDRMs were identified in 375 patients (10.7%), with sustained transmission of these limited to a subset of smaller groups. Informative patterns of epidemic growth, stabilisation, and decline were observed; many transmission groups showed effective reproductive numbers (R e ) values reaching greater than 4.0, representing considerable epidemic growth, while others maintained low R e values. Interpretation: This study provides a high resolution view of HIV transmission in Victoria, Australia, and highlights the potential of molecular epidemiology to guide and enhance public health responses in this setting. This informs ongoing discussions with community groups on the acceptability and place of molecular epidemiological approaches in Australia. Funding: National Health and Medical Research Council, Australian Research Council.

ClockstaRX: Testing Molecular Clock Hypotheses With Genomic Data.

Phylogenomic data provide valuable opportunities for studying evolutionary rates and timescales. These analyses require theoretical and statistical tools based on molecular clocks. We present ClockstaRX, a flexible platform for exploring and testing evolutionary rate signals in phylogenomic data. Here, information about evolutionary rates in branches across gene trees is placed in Euclidean space, allowing data transformation, visualization, and hypothesis testing. ClockstaRX implements formal tests for identifying groups of loci and branches that make a large contribution to patterns of rate variation. This information can then be used to test for drivers of genomic evolutionary rates or to inform models for molecular dating. Drawing on the results of a simulation study, we recommend forms of data exploration and filtering that might be useful prior to molecular-clock analyses.

Clockor2: Inferring Global and Local Strict Molecular Clocks Using Root-to-Tip Regression.

Molecular sequence data from rapidly evolving organisms are often sampled at different points in time. Sampling times can then be used for molecular clock calibration. The root-to-tip (RTT) regression is an essential tool to assess the degree to which the data behave in a clock-like fashion. Here, we introduce Clockor2, a client-side web application for conducting RTT regression. Clockor2 allows users to quickly fit local and global molecular clocks, thus handling the increasing complexity of genomic datasets that sample beyond the assumption of homogeneous host populations. Clockor2 is efficient, handling trees of up to the order of 104 tips, with significant speed increases compared with other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leave the user's computer. Clockor2 is freely available at https://clockor2.github.io/.

Tracing the origin of virulence.

Microbial genomes from ancient chickens uncover the drivers of pathogenicity.

Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.

Molecular evolutionary rate variation is a key aspect of the evolution of many organisms that can be modeled using molecular clock models. For example, fixed local clocks revealed the role of episodic evolution in the emergence of SARS-CoV-2 variants of concern. Like all statistical models, however, the reliability of such inferences is contingent on an assessment of statistical evidence. We present a novel Bayesian phylogenetic approach for detecting episodic evolution. It consists of computing Bayes factors, as the ratio of posterior and prior odds of evolutionary rate increases, effectively quantifying support for the effect size. We conducted an extensive simulation study to illustrate the power of this method and benchmarked it to formal model comparison of a range of molecular clock models using (log) marginal likelihood estimation, and to inference under a random local clock model. Quantifying support for the effect size has higher sensitivity than formal model testing and is straight-forward to compute, because it only needs samples from the posterior and prior distribution. However, formal model testing has the advantage of accommodating a wide range molecular clock models. We also assessed the ability of an automated approach, known as the random local clock, where branches under episodic evolution may be detected without their a priori definition. In an empirical analysis of a data set of SARS-CoV-2 genomes, we find "very strong" evidence for episodic evolution. Our results provide guidelines and practical methods for Bayesian detection of episodic evolution, as well as avenues for further research into this phenomenon.

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia.

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.

Decoding the Fundamental Drivers of Phylodynamic Inference.

Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a method to visualize and quantify the relative impact of pathogen genome sequence and sampling times-two fundamental sources of data for phylodynamics under birth-death-sampling models-to understand how each drives phylodynamic inference. Applying our method to simulated data and real-world SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.

Emergence, continuity, and evolution of Yersinia pestis throughout medieval and early modern Denmark.

The historical epidemiology of plague is controversial due to the scarcity and ambiguity of available data.1,2 A common source of debate is the extent and pattern of plague re-emergence and local continuity in Europe during the 14th-18th century CE.3 Despite having a uniquely long history of plague (∼5,000 years), Scandinavia is relatively underrepresented in the historical archives.4,5 To better understand the historical epidemiology and evolutionary history of plague in this region, we performed in-depth (n = 298) longitudinal screening (800 years) for the plague bacterium Yersinia pestis (Y. pestis) across 13 archaeological sites in Denmark from 1000 to 1800 CE. Our genomic and phylogenetic data captured the emergence, continuity, and evolution of Y. pestis in this region over a period of 300 years (14th-17th century CE), for which the plague-positivity rate was 8.3% (3.3%-14.3% by site). Our phylogenetic analysis revealed that the Danish Y. pestis sequences were interspersed with those from other European countries, rather than forming a single cluster, indicative of the generation, spread, and replacement of bacterial variants through communities rather than their long-term local persistence. These results provide an epidemiological link between Y. pestis and the unknown pestilence that afflicted medieval and early modern Europe. They also demonstrate how population-scale genomic evidence can be used to test hypotheses on disease mortality and epidemiology and help pave the way for the next generation of historical disease research.

Promiscuous evolution of Group A Streptococcal M and M-like proteins.

Group A Streptococcus (GAS) M and M-like proteins are essential virulence factors and represent the primary epidemiological marker of this pathogen. Protein sequences encoding 1054 M, Mrp and Enn proteins, from 1668 GAS genomes, were analysed by SplitsTree4, partitioning around medoids and co-occurrence. The splits network and groups-based analysis of all M and M-like proteins revealed four large protein groupings, with multiple evolutionary histories as represented by multiple edges for most splits, leading to 'M-family-groups' (FG) of protein sequences: FG I, Mrp; FG II, M protein and Protein H; FG III, Enn; and FG IV, M protein. M and Enn proteins formed two groups with nine sub-groups and Mrp proteins formed four groups with ten sub-groups. Discrete co-occurrence of M and M-like proteins were identified suggesting that while dynamic, evolution may be constrained by a combination of functional and virulence attributes. At a granular level, four distinct family-groups of M, Enn and Mrp proteins are observable, with Mrp representing the most genetically distinct of the family-group of proteins. While M and Enn protein families generally group into three distinct family-groups, horizontal and vertical gene flow between distinct GAS strains is ongoing.

Evolutionary rate of SARS-CoV-2 increases during zoonotic infection of farmed mink.

To investigate genetic signatures of adaptation to the mink host, we characterised the evolutionary rate heterogeneity in mink-associated severe acute respiratory syndrome coronaviruses (SARS-CoV-2). In 2020, the first detected anthropozoonotic spillover event of SARS-CoV-2 occurred in mink farms throughout Europe and North America. Both spill-back of mink-associated lineages into the human population and the spread into the surrounding wildlife were reported, highlighting the potential formation of a zoonotic reservoir. Our findings suggest that the evolutionary rate of SARS-CoV-2 underwent an episodic increase upon introduction into the mink host before returning to the normal range observed in humans. Furthermore, SARS-CoV-2 lineages could have circulated in the mink population for a month before detection, and during this period, evolutionary rate estimates were between 3 × 10-3 and 1.05 × 10-2 (95 per cent HPD, with a mean rate of 6.59 × 10-3) a four- to thirteen-fold increase compared to that in humans. As there is evidence for unique mutational patterns within mink-associated lineages, we explored the emergence of four mink-specific Spike protein amino acid substitutions Y453F, S1147L, F486L, and Q314K. We found that mutation Y453F emerged early in multiple mink outbreaks and that mutations F486L and Q314K may co-occur. We suggest that SARS-CoV-2 undergoes a brief, but considerable, increase in evolutionary rate in response to greater selective pressures during species jumps, which may lead to the occurrence of mink-specific mutations. These findings emphasise the necessity of ongoing surveillance of zoonotic SARS-CoV-2 infections in the future.

Plagued by a cryptic clock: insight and issues from the global phylogeny of Yersinia pestis.

Plague has an enigmatic history as a zoonotic pathogen. This infectious disease will unexpectedly appear in human populations and disappear just as suddenly. As a result, a long-standing line of inquiry has been to estimate when and where plague appeared in the past. However, there have been significant disparities between phylogenetic studies of the causative bacterium, Yersinia pestis, regarding the timing and geographic origins of its reemergence. Here, we curate and contextualize an updated phylogeny of Y. pestis using 601 genome sequences sampled globally. Through a detailed Bayesian evaluation of temporal signal in subsets of these data we demonstrate that a Y. pestis-wide molecular clock is unstable. To resolve this, we developed a new approach in which each Y. pestis population was assessed independently, enabling us to recover substantial temporal signal in five populations, including the ancient pandemic lineages which we now estimate may have emerged decades, or even centuries, before a pandemic was historically documented from European sources. Despite this methodological advancement, we only obtain robust divergence dates from populations sampled over a period of at least 90 years, indicating that genetic evidence alone is insufficient for accurately reconstructing the timing and spread of short-term plague epidemics.

Phylodynamic signatures in the emergence of community-associated MRSA.

Community-associated, methicillin-resistant Staphylococcus aureus (MRSA) lineages have emerged in many geographically distinct regions around the world during the past 30 y. Here, we apply consistent phylodynamic methods across multiple community-associated MRSA lineages to describe and contrast their patterns of emergence and dissemination. We generated whole-genome sequencing data for the Australian sequence type (ST) ST93-MRSA-IV from remote communities in Far North Queensland and Papua New Guinea, and the Bengal Bay ST772-MRSA-V clone from metropolitan communities in Pakistan. Increases in the effective reproduction number (Re) and sustained transmission (Re > 1) coincided with spread of progenitor methicillin-susceptible S. aureus (MSSA) in remote northern Australian populations, dissemination of the ST93-MRSA-IV genotype into population centers on the Australian East Coast, and subsequent importation into the highlands of Papua New Guinea and Far North Queensland. Applying the same phylodynamic methods to existing lineage datasets, we identified common signatures of epidemic growth in the emergence and epidemiological trajectory of community-associated S. aureus lineages from America, Asia, Australasia, and Europe. Surges in Re were observed at the divergence of antibiotic-resistant strains, coinciding with their establishment in regional population centers. Epidemic growth was also observed among drug-resistant MSSA clades in Africa and northern Australia. Our data suggest that the emergence of community-associated MRSA in the late 20th century was driven by a combination of antibiotic-resistant genotypes and host epidemiology, leading to abrupt changes in lineage-wide transmission dynamics and sustained transmission in regional population centers.

Phytest: quality control for phylogenetic analyses.

MOTIVATION: The ability to automatically conduct quality control checks on phylogenetic analyses is becoming more important with the increase in genetic sequencing and the use of real-time pipelines e.g. in the SARS-CoV-2 era. Implementations of real-time phylogenetic analyses require automated testing to make sure that problems in the data are caught automatically within analysis pipelines and in a timely manner. Here, we present Phytest (version 1.1) a tool for automating quality control checks on sequences, trees and metadata during phylogenetic analyses. RESULTS: Phytest is a phylogenetic analysis testing program that easily integrates into existing phylogenetic pipelines. We demonstrate the utility of Phytest with real-world examples. AVAILABILITY AND IMPLEMENTATION: Phytest source code is available on GitHub (https://github.com/phytest-devs/phytest) and can be installed via PyPI with the command 'pip install phytest'. Extensive documentation can be found at https://phytest-devs.github.io/phytest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Persistence of Rare Salmonella Typhi Genotypes Susceptible to First-Line Antibiotics in the Remote Islands of Samoa.

For decades, the remote island nation of Samoa (population ~200,000) has faced endemic typhoid fever despite improvements in water quality, sanitation, and economic development. We recently described the epidemiology of typhoid fever in Samoa from 2008 to 2019 by person, place, and time; however, the local Salmonella enterica serovar Typhi (S. Typhi) population structure, evolutionary origins, and genomic features remained unknown. Herein, we report whole genome sequence analyses of 306 S. Typhi isolates from Samoa collected between 1983 and 2020. Phylogenetics revealed a dominant population of rare genotypes 3.5.4 and 3.5.3, together comprising 292/306 (95.4%) of Samoan versus 2/4934 (0.04%) global S. Typhi isolates. Three distinct 3.5.4 genomic sublineages were identified, and their defining polymorphisms were determined. These dominant Samoan genotypes, which likely emerged in the 1970s, share ancestry with other 3.5 clade isolates from South America, Southeast Asia, and Oceania. Additionally, a 106-kb pHCM2 phenotypically cryptic plasmid, detected in a 1992 Samoan S. Typhi isolate, was identified in 106/306 (34.6%) of Samoan isolates; this is more than double the observed proportion of pHCM2-containing isolates in the global collection. In stark contrast with global S. Typhi trends, resistance-conferring polymorphisms were detected in only 15/306 (4.9%) of Samoan S. Typhi, indicating overwhelming susceptibility to antibiotics that are no longer effective in most of South and Southeast Asia. This country-level genomic framework can help local health authorities in their ongoing typhoid surveillance and control efforts, as well as fill a critical knowledge gap in S. Typhi genomic data from Oceania. IMPORTANCE In this study, we used whole genome sequencing and comparative genomics analyses to characterize the population structure, evolutionary origins, and genomic features of S. Typhi associated with decades of endemic typhoid fever in Samoa. Our analyses of Samoan isolates from 1983 to 2020 identified a rare S. Typhi population in Samoa that likely emerged around the early 1970s and evolved into sublineages that are presently dominant. The dominance of these endemic genotypes in Samoa is not readily explained by genomic content or widespread acquisition of antimicrobial resistance. These data establish the necessary framework for future genomic surveillance of S. Typhi in Samoa for public health benefit.

Genomic characterisation reveals a dominant lineage of SARS-CoV-2 in Papua New Guinea.

The coronavirus disease pandemic has highlighted the utility of pathogen genomics as a key part of comprehensive public health response to emerging infectious diseases threats, however, the ability to generate, analyse, and respond to pathogen genomic data varies around the world. Papua New Guinea (PNG), which has limited in-country capacity for genomics, has experienced significant outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with initial genomics data indicating a large proportion of cases were from lineages that are not well defined within the current nomenclature. Through a partnership between in-country public health agencies and academic organisations, industry, and a public health genomics reference laboratory in Australia a system for routine SARS-CoV-2 genomics from PNG was established. Here we aim to characterise and describe the genomics of PNG's second wave and examine the sudden expansion of a lineage that is not well defined but very prevalent in the Western Pacific region. We generated 1797 sequences from cases in PNG and performed phylogenetic and phylodynamic analyses to examine the outbreak and characterise the circulating lineages and clusters present. Our results reveal the rapid expansion of the B.1.466.2 and related lineages within PNG, from multiple introductions into the country. We also highlight the difficulties that unstable lineage assignment causes when using genomics to assist with rapid cluster definitions.

Lymphocytic Choriomeningitis Virus Infection, Australia.

During a mouse plague in early 2021, a farmer from New South Wales, Australia, sought treatment for aseptic meningitis and was subsequently diagnosed with locally acquired lymphocytic choriomeningitis virus infection. Whole-genome sequencing identified a divergent and geographically distinct lymphocytic choriomeningitis virus strain compared with other published sequences.