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      Comparative genome analysis indicates high evolutionary potential of pathogenicity genes in Colletotrichum tanaceti

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          Abstract

          Colletotrichum tanaceti is an emerging foliar fungal pathogen of commercially grown pyrethrum ( Tanacetum cinerariifolium). Despite being reported consistently from field surveys in Australia, the molecular basis of pathogenicity of C. tanaceti on pyrethrum is unknown. Herein, the genome of C. tanaceti (isolate BRIP57314) was assembled de novo and annotated using transcriptomic evidence. The inferred putative pathogenicity gene suite of C. tanaceti comprised a large array of genes encoding secreted effectors, proteases, CAZymes and secondary metabolites. Comparative analysis of its putative pathogenicity gene profiles with those of closely related species suggested that C. tanaceti likely has additional hosts to pyrethrum. The genome of C. tanaceti had a high repeat content and repetitive elements were located significantly closer to genes inferred to influence pathogenicity than other genes. These repeats are likely to have accelerated mutational and transposition rates in the genome, resulting in a rapid evolution of certain CAZyme families in this species. The C. tanaceti genome showed strong signals of Repeat Induced Point (RIP) mutation which likely caused its bipartite nature consisting of distinct gene-sparse, repeat and A-T rich regions. Pathogenicity genes within these RIP affected regions were likely to have a higher evolutionary rate than the rest of the genome. This “two-speed” genome phenomenon in certain Colletotrichum spp. was hypothesized to have caused the clustering of species based on the pathogenicity genes, to deviate from taxonomic relationships. The large repertoire of pathogenicity factors that potentially evolve rapidly due to the plasticity of the genome, indicated that C. tanaceti has a high evolutionary potential. Therefore, C. tanaceti poses a high-risk to the pyrethrum industry. Knowledge of the evolution and diversity of the putative pathogenicity genes will facilitate future research in disease management of C. tanaceti and other Colletotrichum spp.

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          Pathogen population genetics, evolutionary potential, and durable resistance.

          We hypothesize that the evolutionary potential of a pathogen population is reflected in its population genetic structure. Pathogen populations with a high evolutionary potential are more likely to overcome genetic resistance than pathogen populations with a low evolutionary potential. We propose a flexible framework to predict the evolutionary potential of pathogen populations based on analysis of their genetic structure. According to this framework, pathogens that pose the greatest risk of breaking down resistance genes have a mixed reproduction system, a high potential for genotype flow, large effective population sizes, and high mutation rates. The lowest risk pathogens are those with strict asexual reproduction, low potential for gene flow, small effective population sizes, and low mutation rates. We present examples of high-risk and low-risk pathogens. We propose general guidelines for a rational approach to breed durable resistance according to the evolutionary potential of the pathogen.
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            Hidden Markov model speed heuristic and iterative HMM search procedure

            Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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              GeneMark.hmm: new solutions for gene finding.

              The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark. hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: SupervisionRole: ValidationRole: Writing – review & editing
                Role: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 May 2019
                2019
                : 14
                : 5
                : e0212248
                Affiliations
                [1 ] Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia
                [2 ] Tasmanian Institute of Agriculture, University of Tasmania, Burnie, Tasmania, Australia
                [3 ] Faculty of Science, The University of Melbourne, Parkville, Victoria, Australia
                Brigham Young University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-7873-1386
                http://orcid.org/0000-0001-9443-4384
                Article
                PONE-D-19-02240
                10.1371/journal.pone.0212248
                6544218
                31150449
                d24b108c-1039-4c62-97d5-8141095fd430
                © 2019 Lelwala et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 24 January 2019
                : 2 May 2019
                Page count
                Figures: 6, Tables: 4, Pages: 34
                Funding
                This research was funded by Botanical Resources Australia - Agricultural Services, Pty. Ltd- ( https://www.botanicalresources.com/). Ruvini V. Lelwala received Melbourne International Fee Remission Scholarship and Melbourne International Research Scholarship from the University of Melbourne, Australia ( https://www.unimelb.edu.au/). ND Young was supported by NHMRC Career Development Fellowship - APP1109829 ( https://nhmrc.gov.au/). PK Korhonen was supported by NHMRC Early Career Research Fellowship - APP1127033 ( https://nhmrc.gov.au/). The funders had no role in study design, data collection and analysis, decision to publish or Preparation of the manuscript.
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                Data are available from NCBI (accession number PJEX00000000).

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