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      Host-driven subspeciation in the hedgehog fungus, Trichophyton erinacei, an emerging cause of human dermatophytosis

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          Abstract

          Trichophyton erinacei is a main cause of dermatophytosis in hedgehogs and is increasingly reported from human infections worldwide. This pathogen was originally described in the European hedgehog ( Erinaceus europaeus) but is also frequently found in the African four-toed hedgehog ( Atelerix albiventris), a popular pet animal worldwide. Little is known about the taxonomy and population genetics of this pathogen despite its increasing importance in clinical practice. Notably, whether there are different populations or even cryptic species associated with different hosts or geographic regions is not known. To answer these questions, we collected 161 isolates, performed phylogenetic and population-genetic analyses, determined mating-type, and characterised morphology and physiology. Multigene phylogeny and microsatellite analysis supported T. erinacei as a monophyletic species, in contrast to highly incongruent single-gene phylogenies. Two main subpopulations, one specific mainly to Atelerix and second to Erinaceus hosts, were identified inside T. erinacei, and slight differences in the size of microconidia and antifungal susceptibilities were observed among them. Although the process of speciation into two lineages is ongoing in T. erinacei, there is still gene flow between these populations. Thus, we present T. erinacei as a single species, with notable intraspecies variability in genotype and phenotype. The data from wild hedgehogs indicated that sexual reproduction in T. erinacei and de novo infection of hedgehogs from soil are probably rare events and that clonal horizontal spread strongly dominates. The molecular typing approach used in this study represents a suitable tool for further epidemiological surveillance of this emerging pathogen in both animals and humans. The results of this study also highlighted the need to use a multigene phylogeny ideally in combination with other independent molecular markers to understand the species boundaries of dermatophytes.

          Citation: Čmoková A, Kolařík M, Guillot J, et al. 2022. Host-driven subspeciation in the hedgehog fungus, Trichophyton erinacei, an emerging cause of human dermatophytosis. Persoonia 48: 203–218. https://doi.org/10.3767/persoonia.2022.48.06.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

            Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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              Detecting the number of clusters of individuals using the software structure: a simulation study

              The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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                Author and article information

                Journal
                Persoonia
                Persoonia
                Persoonia
                Persoonia : Molecular Phylogeny and Evolution of Fungi
                Nationaal Herbarium Nederland & Westerdijk Fungal Biodiversity Institute
                0031-5850
                1878-9080
                5 June 2022
                12 July 2022
                : 48
                : 203-218
                Affiliations
                [1 ]Department of Botany, Faculty of Science, Charles University, Prague, Czech Republic;
                [2 ]Laboratory of Fungal Genetics and Metabolism, Institute of Microbiology, Czech Academy of Sciences, Prague, Czech Republic.
                [3 ]Dynamyc Research Group EA 7380, Ecole Nationale Vétérinaire d’Alfort, UPEC, USC ANSES, Maisons-Alfort, France.
                [4 ]Department of Dermatology, Parasitology, Mycology, Ecole Nationale Vétérinaire, Agroalimentaire et de l’Alimentation, Oniris, Nantes, France.
                [5 ]Ecole Nationale Vétérinaire d’Alfort, Biopole Alfort, Service de Parasitologie-Mycologie, Maisons-Alfort, France.
                [6 ]Veterinary Mycology Group, Department of Animal Health and Anatomy, Autonomous University of Barcelona, Barcelona, Spain.
                [7 ]Laboratory of Medical Microbiology, Mölbis, Germany.
                [8 ]Public Health Institute in Ostrava, Ostrava, Czech Republic.
                [9 ]Institute of Laboratory Medicine, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic.
                [10 ]Laboratory of Medical Parasitology and Mycology, Hospital České Budě­jovice, České Budějovice, Czech Republic.
                [11 ]Medical Mycology Research Center, Chiba University, Chiba, Japan.
                [12 ]Teikyo University Institute of Medical Mycology (TIMM), Tokyo, Japan.
                [13 ]Department of Dermatology and Venereology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
                [14 ]Laboratory of Mycology, Department of Medical Microbiology Prague and Kladno, Prague, Czech Republic.
                [15 ]Pardubice Regional Hospital, Pardubice, Czech Republic.
                [16 ]Department of Microbiology, Palacký University and University hospital, Olomouc, Czech Republic.
                [17 ]Department of Veterinary Sciences, University of Turin, Turin, Italy.
                Author notes
                Article
                10.3767/persoonia.2023.48.06
                10792284
                38234687
                cbd60990-bcb3-4b0c-b9af-2db472499992
                © 2022 Naturalis Biodiversity Center & Westerdijk Fungal Biodiversity Institute

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                History
                : 6 January 2022
                : 20 April 2022
                Categories
                Research Article

                Plant science & Botany
                epizootic fungal infections,microsatellite typing,multigene phylogeny,population genetics,skin infections,trichophyton benhamiae complex,zoophilic dermatophytes

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