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Active regions on the photosphere of a star have been the major obstacle for detecting Earth-like exoplanets using the radial velocity (RV) method. A commonly employed solution for addressing stellar activity is to assume a linear relationship between the RV observations and the activity indicators along the entire time series, and then remove the estimated contribution of activity from the variation in RV data (overall correction method). However, since active regions evolve on the photosphere over time, correlations between the RV observations and the activity indicators will correspondingly be anisotropic. We present an approach that recognizes the RV locations where the correlations between the RV and the activity indicators significantly change in order to better account for variations in RV caused by stellar activity. The proposed approach uses a general family of statistical breakpoint methods, often referred to as change point detection (CPD) algorithms; several implementations of which are available in R and python. A thorough comparison is made between the breakpoint-based approach and the overall correction method. To ensure wide representativity, we use measurements from real stars that have different levels of stellar activity and whose spectra have different signal-to-noise ratios. When the corrections for stellar activity are applied separately to each temporal segment identified by the breakpoint method, the corresponding residuals in the RV time series are typically much smaller than those obtained by the overall correction method. Consequently, the generalized Lomb-Scargle periodogram contains a smaller number of peaks caused by active regions. The CPD algorithm is particularly effective when focusing on active stars with long time series, such as alpha Cen B., Comment: 31 pages, 18 Figures

Background Pneumococcal disease outbreaks of vaccine preventable serotype 4 sequence type (ST)801 in shipyards have been reported in several countries. We aimed to use genomics to establish any international links between them. Methods Sequence data from ST801-related outbreak isolates from Norway (n = 17), Finland (n = 11) and Northern Ireland (n = 2) were combined with invasive pneumococcal disease surveillance from the respective countries, and ST801-related genomes from an international collection (n = 41 of > 40,000), totalling 106 genomes. Raw data were mapped and recombination excluded before phylogenetic dating. Results Outbreak isolates were relatively diverse, with up to 100 SNPs (single nucleotide polymorphisms) and a common ancestor estimated around the year 2000. However, 19 Norwegian and Finnish isolates were nearly indistinguishable (0–2 SNPs) with the common ancestor dated around 2017. Conclusion The total diversity of ST801 within the outbreaks could not be explained by recent transmission alone, suggesting that harsh environmental and associated living conditions reported in the shipyards may facilitate invasion of colonising pneumococci. However, near identical strains in the Norwegian and Finnish outbreaks does suggest that transmission between international shipyards also contributed to those outbreaks. This indicates the need for improved preventative measures in this working population including pneumococcal vaccination.

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415 S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively., Staphylococcus epidermidis is carried asymptomatically by virtually all humans but is also a major cause of nosocomial infection. Here, the authors study 141 isolates from healthy carriage and 274 isolates from clinical infections, and identify genes and genetic elements associated with pathogenicity.

Klebsiella oxytoca causes opportunistic human infections and post-antibiotic haemorrhagic diarrhea. This Enterobacteriaceae species is genetically heterogeneous and is currently subdivided into seven phylogroups (Ko1 to Ko4 and Ko6 to Ko8). Here we investigated the taxonomic status of phylogroups Ko3 and Ko4. Genomic sequence-based phylogenetic analyses demonstrate that Ko3 and Ko4 formed well-defined sequence clusters related to, but distinct from, Klebsiella michiganensis (Ko1), K. oxytoca (Ko2), K. huaxiensis (Ko8), and K. grimontii (Ko6). The average nucleotide identity (ANI) of Ko3 and Ko4 were 90.7% with K. huaxiensis and 95.5% with K. grimontii, respectively. In addition, three strains of K. huaxiensis, a species so far described based on a single strain from a urinary tract infection patient in China, were isolated from cattle and human feces. Biochemical and MALDI-ToF mass spectrometry analysis allowed differentiating Ko3, Ko4, and Ko8 from the other K. oxytoca species. Based on these results, we propose the names Klebsiella spallanzanii for the Ko3 phylogroup, with SPARK_775_C1T (CIP 111695T and DSM 109531T) as type strain, and Klebsiella pasteurii for Ko4, with SPARK_836_C1T (CIP 111696T and DSM 109530T) as type strain. Strains of K. spallanzanii were isolated from human urine, cow feces, and farm surfaces, while strains of K. pasteurii were found in fecal carriage from humans, cows, and turtles. MEDVETKLEBS, MedVetKlebs

Infections with multidrug-resistant (MDR) strains of Escherichia coli are a significant global public health concern. To combat these pathogens, we need a deeper understanding of how they evolved from their background populations. By understanding the processes that underpin their emergence, we can design new strategies to limit evolution of new clones and combat existing clones. By combining population genomics with modelling approaches, we show that dominant MDR clones of E. coli are under the influence of negative frequency-dependent selection, preventing them from rising to fixation in a population. Furthermore, we show that this selection acts on genes involved in anaerobic metabolism, suggesting that this key trait, and the ability to colonize human intestinal tracts, is a key step in the evolution of MDR clones of E. coli., Escherichia coli is a major cause of bloodstream and urinary tract infections globally. The wide dissemination of multidrug-resistant (MDR) strains of extraintestinal pathogenic E. coli (ExPEC) poses a rapidly increasing public health burden due to narrowed treatment options and increased risk of failure to clear an infection. Here, we present a detailed population genomic analysis of the ExPEC ST131 clone, in which we seek explanations for its success as an emerging pathogenic strain beyond the acquisition of antimicrobial resistance (AMR) genes. We show evidence for evolution toward separate ecological niches for the main clades of ST131 and differential evolution of anaerobic metabolism, key colonization, and virulence factors. We further demonstrate that negative frequency-dependent selection acting across accessory loci is a major mechanism that has shaped the population evolution of this pathogen.

Wild birds have been suggested to be reservoirs of antimicrobial resistant and/or pathogenic Enterococcus faecalis (Efs) strains, but the scarcity of studies and available sequences limit our understanding of the population structure of the species in these hosts. Here, we analysed the clonal and plasmid diversity of 97 Efs isolates from wild migratory birds. We found a high diversity, with most sequence types (STs) being firstly described here, while others were found in other hosts including some predominant in poultry. We found that pheromone-responsive plasmids predominate in wild bird Efs while 35% of the isolates entirely lack plasmids. Then, to better understand the ecology of the species, the whole genome of fivestrains with known STs (ST82, ST170, ST16 and ST55) were sequenced and compared with all the Efs genomes available in public databases. Using several methods to analyse core and accessory genomes (AccNET, PLACNET, hierBAPS and PANINI), we detected differences in the accessory genome of some lineages (e.g. ST82) demonstrating specific associations with birds. Conversely, the genomes of other Efs lineages exhibited divergence in core and accessory genomes, reflecting different adaptive trajectories in various hosts. This pangenome divergence, horizontal gene transfer events and occasional epidemic peaks could explain the population structure of the species.

The environmental bacterium $\textit{Burkholderia pseudomallei}$ causes an estimated 165,000 cases of human melioidosis per year worldwide and is also classified as a biothreat agent. We used whole genome sequences of 469 $\textit{B. pseudomallei}$isolates from 30 countries collected over 79 years to explore its geographic transmission. Our data point to Australia as an early reservoir, with transmission to Southeast Asia followed by onward transmission to South Asia and East Asia. Repeated reintroductions were observed within the Malay Peninsula and between countries bordered by the Mekong River. Our data support an African origin of the Central and South American isolates with introduction of B. pseudomallei into the Americas between 1650 and 1850, providing a temporal link with the slave trade. We also identified geographically distinct genes/variants in Australasian or Southeast Asian isolates alone, with virulence-associated genes being among those over-represented. This provides a potential explanation for clinical manifestations of melioidosis that are geographically restricted.

Streptococcus suis causes disease in pigs worldwide and is increasingly implicated in zoonotic disease in East and South-East Asia. To understand the genetic basis of disease in S. suis, we study the genomes of 375 isolates with detailed clinical phenotypes from pigs and humans from the United Kingdom and Vietnam. Here, we show that isolates associated with disease contain substantially fewer genes than non-clinical isolates, but are more likely to encode virulence factors. Human disease isolates are limited to a single-virulent population, originating in the 1920, s when pig production was intensified, but no consistent genomic differences between pig and human isolates are observed. There is little geographical clustering of different S. suis subpopulations, and the bacterium undergoes high rates of recombination, implying that an increase in virulence anywhere in the world could have a global impact over a short timescale., The bacterium Streptococcus suis causes respiratory tract infections in pigs and meningitis in humans. Here, the authors show that human disease isolates are limited to a single virulent population and find no consistent genomic differences between pig and human isolates.

Background: Haemophilus parasuis is the etiologic agent of Glässer’s disease in pigs and causes devastating losses\ud to the farming industry. Whilst some hyper-virulent isolates have been described, the relationship between genetics\ud and disease outcome has been only partially established. In particular, there is weak correlation between serovar\ud and disease phenotype. We sequenced the genomes of 212 isolates of H. parasuis and have used this to describe\ud the pan-genome and to correlate this with clinical and carrier status, as well as with serotype.\ud Results: Recombination and population structure analyses identified five groups with very high rates of recombination,\ud separated into two clades of H. parasuis with no signs of recombination between them. We used genome-wide\ud association methods including discriminant analysis of principal components (DAPC) and generalised linear\ud modelling (glm) to look for genetic determinants of this population partition, serovar and pathogenicity. We were able\ud to identify genes from the accessory genome that were significantly associated with phenotypes such as potential\ud serovar specific genes including capsule genes, and 48 putative virulence factors that were significantly different\ud between the clinical and non-clinical isolates. We also show that the presence of many previously suggested\ud virulence factors is not an appropriate marker of virulence.\ud Conclusions: These genes will inform the generation of new molecular diagnostics and vaccines, and refinement of\ud existing typing schemes and show the importance of the accessory genome of a diverse species when investigating\ud the relationship between genotypes and phenotypes.

We investigate the problem of learning the structure of a Markov network from data. It is shown that the structure of such networks can be described in terms of constraints which enables the use of existing solver technology with optimization capabilities to compute optimal networks starting from initial scores computed from the data. To achieve efficient encodings, we develop a novel characterization of Markov network structure using a balancing condition on the separators between cliques forming the network. The resulting translations into propositional satisfiability and its extensions such as maximum satisfiability, satisfiability modulo theories, and answer set programming, enable us to prove optimal certain network structures which have been previously found by stochastic search.

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics.

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The Glanville fritillary butterfly (Melitaea cinxia) has been studied in the Aland Islands in Finland since 1991, where it occurs as a classic metapopulation in a large network of 4000 dry meadows. Much ecological work has been conducted on this species, but population genetic studies have been hampered by paucity of suitable genetic markers. Here, using single nucleotide polymorphisms and microsatellites developed for the Glanville fritillary, we examine the correspondence between the demographic and genetic spatial structures. Given the dynamic nature of the metapopulation, the current genetic spatial structure may bear a signal of past changes in population sizes and past patterns of gene flow rather than reflect the current demographic structure or landscape structure. We analyse this question with demographic data for 10 years, using the Rand index to assess the similarity between the genetic, demographic, and landscape spatial structures. Our results show that the current genetic spatial structure is better explained by the past rather than by the current demographic spatial structure or by the spatial configuration of the habitat in the landscape. Furthermore, current genetic diversity is significantly explained by past metapopulation sizes. The time lag between major demographic events and change in the genetic spatial structure and diversity has implications for the study of spatial dynamics. ispartof: Molecular ecology vol:17 issue:11 pages:2629-42 ispartof: location:England status: published