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 to other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leaves 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.

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.

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.

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.

Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

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.

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.

Genomic epidemiology of Salmonella Typhi in Central Division, Fiji, 2012 to 2016.

Background: Typhoid fever is endemic in some Pacific Island Countries including Fiji and Samoa yet genomic surveillance is not routine in such settings. Previous studies suggested imports of the global H58 clade of Salmonella enterica var Typhi (Salmonella Typhi) contribute to disease in these countries which, given the MDR potential of H58, does not auger well for treatment. The objective of the study was to define the genomic epidemiology of Salmonella Typhi in Fiji. Methods: Genomic sequencing approaches were implemented to study the distribution of 255 Salmonella Typhi isolates from the Central Division of Fiji. We augmented epidemiological surveillance and Bayesian phylogenomic approaches with a multi-year typhoid case-control study to define geospatial patterns among typhoid cases. Findings: Genomic analyses showed Salmonella Typhi from Fiji resolved into 2 non-H58 genotypes with isolates from the two dominant ethnic groups, the Indigenous (iTaukei) and non-iTaukei genetically indistinguishable. Low rates of international importation of clones was observed and overall, there were very low levels an antibiotic resistance within the endemic Fijian typhoid genotypes. Genomic epidemiological investigations were able to identify previously unlinked case clusters. Bayesian phylodynamic analyses suggested that genomic variation within the larger endemic Salmonella Typhi genotype expanded at discreet times, then contracted. Interpretation: Cyclones and flooding drove 'waves' of typhoid outbreaks in Fiji which, through population aggregation, poor sanitation and water safety, and then mobility of the population, spread clones more widely. Minimal international importations of new typhoid clones suggest that targeted local intervention strategies may be useful in controlling endemic typhoid infection. These findings add to our understanding of typhoid transmission networks in an endemic island country with broad implications, particularly across Pacific Island Countries. Funding: This work was supported by the Coalition Against Typhoid through the Bill and Melinda Gates Foundation [grant number OPP1017518], the Victorian Government, the National Health and Medical Research Council Australia, the Australian Research Council, and the Fiji Ministry of Health and Medical Services.

A 16th century Escherichia coli draft genome associated with an opportunistic bile infection.

Escherichia coli - one of the most characterized bacteria and a major public health concern - remains invisible across the temporal landscape. Here, we present the meticulous reconstruction of the first ancient E. coli genome from a 16th century gallstone from an Italian mummy with chronic cholecystitis. We isolated ancient DNA and reconstructed the ancient E. coli genome. It consisted of one chromosome of 4446 genes and two putative plasmids with 52 genes. The E. coli strain belonged to the phylogroup A and an exceptionally rare sequence type 4995. The type VI secretion system component genes appears to be horizontally acquired from Klebsiella aerogenes, however we could not identify any pathovar specific genes nor any acquired antibiotic resistances. A sepsis mouse assay showed that a closely related contemporary E. coli strain was avirulent. Our reconstruction of this ancient E. coli helps paint a more complete picture of the burden of opportunistic infections of the past.

Niche-specific genome degradation and convergent evolution shaping Staphylococcus aureus adaptation during severe infections.

During severe infections, Staphylococcus aureus moves from its colonising sites to blood and tissues and is exposed to new selective pressures, thus, potentially driving adaptive evolution. Previous studies have shown the key role of the agr locus in S. aureus pathoadaptation; however, a more comprehensive characterisation of genetic signatures of bacterial adaptation may enable prediction of clinical outcomes and reveal new targets for treatment and prevention of these infections. Here, we measured adaptation using within-host evolution analysis of 2590 S. aureus genomes from 396 independent episodes of infection. By capturing a comprehensive repertoire of single nucleotide and structural genome variations, we found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. These invasive strains had up to 20-fold enrichments for genome degradation signatures and displayed significantly convergent mutations in a distinctive set of genes, linked to antibiotic response and pathogenesis. In addition to agr-mediated adaptation, we identified non-canonical, genome-wide significant loci including sucA-sucB and stp1. The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform correlation of infection outcomes with adaptation signatures.

The bacterium Staphylococcus aureus lives harmlessly on our skin and noses. However, occasionally, it gets into our blood and internal organs, such as our bones and joints, where it causes severe, long-lasting infections that are difficult to treat. Over time, S. aureus acquire characteristics that help them to adapt to different locations, such as transitioning from the nose to the blood, and avoid being killed by antibiotics. Previous studies have identified changes, or 'mutations', in genes that are likely to play an important role in this evolutionary process. One of these genes, called accessory gene regulator (or agr for short), has been shown to control the mechanisms S. aureus use to infect cells and disseminate in the body. However, it is unclear if there are changes in other genes that also help S. aureus adapt to life inside the human body. To help resolve this mystery, Giulieri et al. collected 2,500 samples of S. aureus from almost 400 people. This included bacteria harmlessly living on the skin or in the nose, as well as strains that caused an infection. Gene sequencing revealed a small number of genes, referred to as 'adaptive genes', that often acquire mutations during infection. Of these, agr was the most commonly altered. However, mutations in less well-known genes were also identified: some of these genes are related to resistance to antibiotics, while others are involved in chemical processes that help the bacteria to process nutrients. Most mutations were caused by random errors being introduced in to the bacteria's genetic code which stopped genes from working. However, in some cases, genes were turned off by small fragments of DNA moving around and inserting themselves into different parts of the genome. This study highlights a group of genes that help S. aureus to thrive inside the body and cause severe and prolonged infections. If these results can be confirmed, it may help to guide which antibiotics are used to treat different infections. Furthermore, understanding which genes are important for infection could lead to new strategies for eliminating this dangerous bacterium.