Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae.

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Inter-species gene flow drives ongoing evolution of Streptococcus pyogenes and Streptococcus dysgalactiae subsp. equisimilis.

Streptococcus dysgalactiae subsp. equisimilis (SDSE) is an emerging cause of human infection with invasive disease incidence and clinical manifestations comparable to the closely related species, Streptococcus pyogenes. Through systematic genomic analyses of 501 disseminated SDSE strains, we demonstrate extensive overlap between the genomes of SDSE and S. pyogenes. More than 75% of core genes are shared between the two species with one third demonstrating evidence of cross-species recombination. Twenty-five percent of mobile genetic element (MGE) clusters and 16 of 55 SDSE MGE insertion regions were shared across species. Assessing potential cross-protection from leading S. pyogenes vaccine candidates on SDSE, 12/34 preclinical vaccine antigen genes were shown to be present in >99% of isolates of both species. Relevant to possible vaccine evasion, six vaccine candidate genes demonstrated evidence of inter-species recombination. These findings demonstrate previously unappreciated levels of genomic overlap between these closely related pathogens with implications for streptococcal pathobiology, disease surveillance and prevention.

Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia.

Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.

fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing.

SUMMARY: Fastlin is a bioinformatics tool designed for rapid Mycobacterium tuberculosis complex (MTBC) lineage typing. It utilizes an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms associated with specific MTBC lineages. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. AVAILABILITY AND IMPLEMENTATION: fastlin is freely available at https://github.com/rderelle/fastlin and can easily be installed using Conda.

The mitoepigenome responds to stress, suggesting novel mito-nuclear interactions in vertebrates.

The mitochondria are central in the cellular response to changing environmental conditions resulting from disease states, environmental exposures or normal physiological processes. Although the influences of environmental stressors upon the nuclear epigenome are well characterized, the existence and role of the mitochondrial epigenome remains contentious. Here, by quantifying the mitochondrial epigenomic response of pineal gland cells to circadian stress, we confirm the presence of extensive cytosine methylation within the mitochondrial genome. Furthermore, we identify distinct epigenetically plastic regions (mtDMRs) which vary in cytosinic methylation, primarily in a non CpG context, in response to stress and in a sex-specific manner. Motifs enriched in mtDMRs contain recognition sites for nuclear-derived DNA-binding factors (ATF4, HNF4A) important in the cellular metabolic stress response, which we found to be conserved across diverse vertebrate taxa. Together, these findings suggest a new layer of mito-nuclear interaction in which the nuclear metabolic stress response could alter mitochondrial transcriptional dynamics through the binding of nuclear-derived transcription factors in a methylation-dependent context.

Accurate and fast graph-based pangenome annotation and clustering with ggCaller.

Bacterial genomes differ in both gene content and sequence mutations, which underlie extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated, and clustered, representing the "pangenome." Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow using population-wide de Bruijn graphs, removing redundancy in gene annotation and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial data sets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with data sets containing complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial evolution and epidemiology.

Genealogical inference and more flexible sequence clustering using iterative-PopPUNK.

Bacterial genome data are accumulating at an unprecedented speed due to the routine use of sequencing in clinical diagnoses, public health surveillance, and population genetics studies. Genealogical reconstruction is fundamental to many of these uses; however, inferring genealogy from large-scale genome data sets quickly, accurately, and flexibly is still a challenge. Here, we extend an alignment- and annotation-free method, PopPUNK, to increase its flexibility and interpretability across data sets. Our method, iterative-PopPUNK, rapidly produces multiple consistent cluster assignments across a range of sequence identities. By constructing a partially resolved genealogical tree with respect to these clusters, users can select a resolution most appropriate for their needs. We showed the accuracy of clusters at all levels of similarity and genealogical inference of iterative-PopPUNK based on simulated data and obtained phylogenetically concordant results in real data sets from seven bacterial species. Using two example sets of Escherichia/Shigella and Vibrio parahaemolyticus genomes, we show that iterative-PopPUNK can achieve cluster resolutions ranging from phylogroup down to sequence typing (ST). The iterative-PopPUNK algorithm is implemented in the "PopPUNK_iterate" program, available as part of the PopPUNK package.

Dissemination of carbapenemase-producing Enterobacterales in Ireland from 2012 to 2017: a retrospective genomic surveillance study.

The spread of carbapenemase-producing Enterobacterales (CPE) is of major public health concern. The transmission dynamics of CPE in hospitals, particularly at the national level, are not well understood. Here, we describe a retrospective nationwide genomic surveillance study of CPE in Ireland between 2012 and 2017. We sequenced 746 national surveillance CPE samples obtained between 2012 and 2017. After clustering the sequences, we used thresholds based on pairwise SNPs, and reported within-host diversity along with epidemiological data to infer recent putative transmissions. All clusters in circulating clones, derived from high-resolution phylogenies, of a species (Klebsiella pneumoniae, Escherichia coli, Klebsiella oxytoca, Enterobacter cloacae, Enterobacter hormaechei and Citrobacter freundii) were individually examined for evidence of transmission. Antimicrobial resistance trends over time were also assessed. We identified 352 putative transmission events in six species including widespread and frequent transmissions in three species. We detected putative outbreaks in 4/6 species with three hospitals experiencing prolonged outbreaks. The bla OXA-48 gene was the main cause of carbapenem resistance in Ireland in almost all species. An expansion in the number of sequence types carrying bla OXA-48 was an additional cause of the increasing prevalence of carbapenemase-producing K. pneumoniae and E. coli.

Inferring the heritability of bacterial traits in the era of machine learning.

Quantification of heritability is a fundamental desideratum in genetics, which allows an assessment of the contribution of additive genetic variation to the variability of a trait of interest. The traditional computational approaches for assessing the heritability of a trait have been developed in the field of quantitative genetics. However, the rise of modern population genomics with large sample sizes has led to the development of several new machine learning-based approaches to inferring heritability. In this article, we systematically summarize recent advances in machine learning which can be used to infer heritability. We focus on an application of these methods to bacterial genomes, where heritability plays a key role in understanding phenotypes such as antibiotic resistance and virulence, which are particularly important due to the rising frequency of antimicrobial resistance. By designing a heritability model incorporating realistic patterns of genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, we test the performance of a wide spectrum of different inference methods, including also GCTA. In addition to the synthetic data benchmark, we present a comparison of the methods for antibiotic resistance traits for multiple bacterial pathogens. Insights from the benchmarking and real data analyses indicate a highly variable performance of the different methods and suggest that heritability inference would likely benefit from tailoring of the methods to the specific genetic architecture of the target organism. Availability and implementation: The R codes and data used in the numerical experiments are available at: https://github.com/tienmt/her_MLs.

Novel Multilocus Sequence Typing and Global Sequence Clustering Schemes for Characterizing the Population Diversity of Streptococcus mitis.

Streptococcus mitis is a common oral commensal and an opportunistic pathogen that causes bacteremia and infective endocarditis; however, the species has received little attention compared to other pathogenic streptococcal species. Effective and easy-to-use molecular typing tools are essential for understanding bacterial population diversity and biology, but schemes specific for S. mitis are not currently available. We therefore developed a multilocus sequence typing (MLST) scheme and defined sequence clusters or lineages of S. mitis using a comprehensive global data set of 322 genomes (148 publicly available and 174 newly sequenced). We used internal 450-bp sequence fragments of seven housekeeping genes (accA, gki, hom, oppC, patB, rlmN, and tsf) to define the MLST scheme and derived the global S. mitis sequence clusters using the PopPUNK clustering algorithm. We identified an initial set of 259 sequence types (STs) and 258 global sequence clusters. The schemes showed high concordance (100%), capturing extensive S. mitis diversity with strains assigned to multiple unique STs and global sequence clusters. The tools also identified extensive within- and between-host S. mitis genetic diversity among isolates sampled from a cohort of healthy individuals, together with potential transmission events, supported by both phylogeny and pairwise single nucleotide polymorphism (SNP) distances. Our novel molecular typing and strain clustering schemes for S. mitis allow for the integration of new strain data, are electronically portable at the PubMLST database (https://pubmlst.org/smitis), and offer a standardized approach to understanding the population structure of S. mitis. These robust tools will enable new insights into the epidemiology of S. mitis colonization, disease and transmission.

BlpC-mediated selfish program leads to rapid loss of Streptococcus pneumoniae clonal diversity during infection.

Successful colonization of a host requires bacterial adaptation through genetic and population changes that are incompletely defined. Using chromosomal barcoding and high-throughput sequencing, we investigate the population dynamics of Streptococcus pneumoniae during infant mouse colonization. Within 1 day post inoculation, diversity was reduced >35-fold with expansion of a single clonal lineage. This loss of diversity was not due to immune factors, microbiota, or exclusive genetic drift. Rather, bacteriocins induced by the BlpC-quorum sensing pheromone resulted in predation of kin cells. In this intra-strain competition, the subpopulation reaching a quorum likely eliminates others that have yet to activate the blp locus. Additionally, this reduced diversity restricts the number of unique clones that establish colonization during transmission between hosts. Genetic variation in the blp locus was also associated with altered transmissibility in a human population, further underscoring the importance of BlpC in clonal selection and its role as a selfish element.

Mandrake: visualizing microbial population structure by embedding millions of genomes into a low-dimensional representation.

In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species, and the number of genomes is expected to continue to increase at an accelerated pace given the advances in sequencing technology and widespread genomic surveillance initiatives. This explosion of data calls for innovative methods to enable rapid exploration of the structure of a population based on different data modalities, such as multiple sequence alignments, assemblies and estimates of gene content across different genomes. Here, we present Mandrake, an efficient implementation of a dimensional reduction method tailored for the needs of large-scale population genomics. Mandrake is capable of visualizing population structure from millions of whole genomes, and we illustrate its usefulness with several datasets representing major pathogens. Our method is freely available both as an analysis pipeline (https://github.com/johnlees/mandrake) and as a browser-based interactive application (https://gtonkinhill.github.io/mandrake-web/). This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Effect of childhood vaccination and antibiotic use on pneumococcal populations and genome-wide associations with disease among children in Nepal: an observational study.

BACKGROUND: Pneumococcal disease is a leading cause of bacterial pneumonia and invasive bacterial disease among children globally. The reason some strains of pneumococci are more likely to cause disease, and how interventions such as vaccines and antibiotics affect pneumococcal strains is poorly understood. We aimed to identify genetic regions under selective pressure and those associated with disease through the analysis of pneumococcal whole-genome sequences. METHODS: Whole-genome sequencing was performed on pneumococcal isolates collected between January, 2005, and May, 2018, in Kathmandu, Nepal, which included programmatic ten-valent pneumococcal conjugate vaccine (PCV10) introduction in 2015. Isolates were from three distinct cohorts: nasopharyngeal swabs of healthy community-based children, nasopharyngeal swabs of children admitted to hospital with pneumonia, and sterile-site cultures from children admitted to hospital. Across these cohorts we examined serotype distribution, antibiotic resistance, strain distribution, and regions of recombination to determine genes that were undergoing diversifying selection. Genome-wide association studies comparing pneumonia and sterile-site isolates with healthy carriage were used to determine novel variants associated with disease. FINDINGS: After programmatic introduction of PCV10, there was a decline in vaccine covered serotypes; however, strains that had expressed these serotypes continued to exist in the post-PCV population. We identified GPSC9 to be a strain of concern due to its high prevalence in disease, multidrug resistance, and ability to switch to an unencapsulated phenotype via insertion of virulence factor pspC into the cps locus. Antibiotic resistance loci to co-trimoxazole were found to be prevalent (pre-PCV10 78% vs post-PCV10 81%; p=0·27) and increasingly prevalent to penicillin (pre-PCV10 15% vs post-PCV10 32%; p<0·0001). Regions with multiple recombinations were identified spanning the surface protein virulence factors pspA and pspC and antibiotic targets pbpX, folA, folC, folE, and folP. Furthermore, we identified variants in lacE2 to be strongly associated with isolates from children with pneumonia and PRIP to be strongly associated with isolates from sterile sites. INTERPRETATION: Our work highlights the effect of pneumococcal conjugate vaccines, antibiotics, and host-pathogen interaction in pneumococcal variation, and the pathogen's capability of adapting to these factors at both population-wide and strain-specific levels. Ongoing surveillance of disease-associated strains and further investigation of lacE2 and PRIP as serotype-independent targets for therapeutic interventions is required. FUNDING: Gavi, The Vaccine Alliance; WHO; Bill & Melinda Gates Foundation; Wellcome Sanger Institute; and US Centers for Disease Control and Prevention.

Pneumococcal genetic variability in age-dependent bacterial carriage.

The characteristics of pneumococcal carriage vary between infants and adults. Host immune factors have been shown to contribute to these age-specific differences, but the role of pathogen sequence variation is currently less well-known. Identification of age-associated pathogen genetic factors could leadto improved vaccine formulations. We therefore performed genome sequencing in a large carriage cohort of children and adults and combined this with data from an existing age-stratified carriage study. We compiled a dictionary of pathogen genetic variation, including serotype, strain, sequence elements, single-nucleotide polymorphisms (SNPs), and clusters of orthologous genes (COGs) for each cohort - all of which were used in a genome-wide association with host age. Age-dependent colonization showed weak evidence of being heritable in the first cohort (h2 = 0.10, 95% CI 0.00-0.69) and stronger evidence in the second cohort (h2 = 0.56, 95% CI 0.23-0.87). We found that serotypes and genetic background (strain) explained a proportion of the heritability in the first cohort (h2serotype = 0.07, 95% CI 0.04-0.14 and h2GPSC = 0.06, 95% CI 0.03-0.13) and the second cohort (h2serotype = 0.11, 95% CI 0.05-0.21 and h2GPSC = 0.20, 95% CI 0.12-0.31). In a meta-analysis of these cohorts, we found one candidate association (p=1.2 × 10-9) upstream of an accessory Sec-dependent serine-rich glycoprotein adhesin. Overall, while we did find a small effect of pathogen genome variation on pneumococcal carriage between child and adult hosts, this was variable between populations and does not appear to be caused by strong effects of individual genes. This supports proposals for adaptive future vaccination strategies that are primarily targeted at dominant circulating serotypes and tailored to the composition of the pathogen populations.

Intermittent fasting induces chronic changes in the hepatic gene expression of Red Jungle Fowl (Gallus gallus).

BACKGROUND: Intermittent fasting (IF), the implementation of fasting periods of at least 12 consecutive hours on a daily to weekly basis, has received a lot of attention in recent years for imparting the life-prolonging and health-promoting effects of caloric restriction with no or only moderate actual restriction of caloric intake. IF is also widely practiced in the rearing of broiler breeders, the parent stock of meat-type chickens, who require strict feed restriction regimens to prevent the serious health problems associated with their intense appetites. Although intermittent fasting has been extensively used in this context to reduce feed competition and its resulting stress, the potential of IF in chickens as an alternative and complementary model to rodents has received less investigation. In both mammals and birds, the liver is a key component of the metabolic response to IF, responding to variations in energy balance. Here we use a microarray analysis to examine the liver transcriptomics of wild-type Red Jungle Fowl chickens fed either ad libitum, chronically restricted to around 70% of ad libitum daily or intermittently fasted (IF) on a 2:1 (2 days fed, 1 day fasted) schedule without actual caloric restriction. As red junglefowl are ancestral to domestic chicken breeds, these data serve as a baseline to which existing and future transcriptomic results from farmed birds such as broiler breeders can be compared. RESULTS: We find large effects of feeding regimen on liver transcriptomics, with most of the affected genes relating to energy metabolism. A cluster analysis shows that IF is associated with large and reciprocal changes in genes related to lipid and carbohydrate metabolism, but also chronic changes in genes related to amino acid metabolism (generally down-regulated) and cell cycle progression (generally up-regulated). The overall transcription pattern appears to be one of promoting high proliferative plasticity in response to fluctuations in available energy substrates. A small number of inflammation-related genes also show chronically changed expression profiles, as does one circadian rhythm gene. CONCLUSIONS: The increase in proliferative potential suggested by the gene expression changes reported here indicates that birds and mammals respond similarly to intermittent fasting practices. Our findings therefore suggest that the health benefits of periodic caloric restriction are ubiquitous and not restricted to mammals alone. Whether a common fundamental mechanism, for example involving leptin, underpins these benefits remains to be elucidated.

Targeted control of pneumolysin production by a mobile genetic element in Streptococcus pneumoniae.

Streptococcus pneumoniae is a major human pathogen that can cause severe invasive diseases such as pneumonia, septicaemia and meningitis. Young children are at a particularly high risk, with an estimated 3-4 million cases of severe disease and between 300 000 and 500 000 deaths attributable to pneumococcal disease each year. The haemolytic toxin pneumolysin (Ply) is a primary virulence factor for this bacterium, yet despite its key role in pathogenesis, immune evasion and transmission, the regulation of Ply production is not well defined. Using a genome-wide association approach, we identified a large number of potential affectors of Ply activity, including a gene acquired horizontally on the antibiotic resistance-conferring Integrative and Conjugative Element (ICE) ICESp23FST81. This gene encodes a novel modular protein, ZomB, which has an N-terminal UvrD-like helicase domain followed by two Cas4-like domains with potent ATP-dependent nuclease activity. We found the regulatory effect of ZomB to be specific for the ply operon, potentially mediated by its high affinity for the BOX repeats encoded therein. Using a murine model of pneumococcal colonization, we further demonstrate that a ZomB mutant strain colonizes both the upper respiratory tract and lungs at higher levels when compared to the wild-type strain. While the antibiotic resistance-conferring aspects of ICESp23FST81 are often credited with contributing to the success of the S. pneumoniae lineages that acquire it, its ability to control the expression of a major virulence factor implicated in bacterial transmission is also likely to have played an important role.

Pneumococcal capsule blocks protection by immunization with conserved surface proteins.

Vaccines targeting Streptococcus pneumoniae (Spn) are limited by dependence on capsular polysaccharide and its serotype diversity. More broadly-based approaches using common protein antigens have not resulted in a licensed vaccine. Herein, we used an unbiased, genome-wide approach to find novel vaccine antigens to disrupt carriage modeled in mice. A Tn-Seq screen identified 198 genes required for colonization of which 16 are known to express conserved, immunogenic surface proteins. After testing defined mutants for impaired colonization of infant and adult mice, 5 validated candidates (StkP, PenA/Pbp2a, PgdA, HtrA, and LytD/Pce/CbpE) were used as immunogens. Despite induction of antibody recognizing the Spn cell surface, there was no protection against Spn colonization. There was, however, protection against an unencapsulated Spn mutant. This result correlated with increased antibody binding to the bacterial surface in the absence of capsule. Our findings demonstrate how the pneumococcal capsule interferes with mucosal protection by antibody to common protein targets.

Does posture explain the kinematic differences in a grounded running gait between male and female Svalbard rock ptarmigan (Lagopus muta hyperborea) moving on snow?

The majority of locomotor research is conducted on treadmills and few studies attempt to understand the differences between this and animals moving in the wild. For example, animals may adjust their gait kinematics or limb posture, to a more compliant limb, to increase stability of locomotion to prevent limb failure or falling on different substrates. Here, using video recordings, we compared locomotor parameters (speed range, stride length, stride frequency, stance duration, swing duration and duty factor) of female Svalbard rock ptarmigan (Lagopus muta hyperborea) moving in the wild over snow to previous treadmill-based research. We also compared the absolute and body size (body mass and limb length)-corrected values of kinematic parameters to published data from males to look for any sex differences across walking and grounded running gaits. Our findings indicate that the kinematics of locomotion are largely conserved between the field and laboratory in that none of the female gaits were drastically affected by moving over snow, except for a prolonged swing phase at very slow walking speeds, likely due to toe dragging. Comparisons between the sexes indicate that the differences observed during a walking gait are likely due to body size. However, sexual dimorphism in body size could not explain the disparate grounded running kinematics of the female and male ptarmigan, which might be linked to a more crouched posture in females. Our findings provide insight into how males and females moving in situ may use different strategies to alleviate the effects of a variable substrate. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00300-021-02872-x.

Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study.

BACKGROUND: England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories. METHODS: This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions. FINDINGS: The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69-83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500-5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700-1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness. INTERPRETATION: Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures. FUNDING: National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.