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      A Robust Phylogenomic Time Tree for Biotechnologically and Medically Important Fungi in the Genera Aspergillus and Penicillium

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          Abstract

          Understanding the evolution of traits across technologically and medically significant fungi requires a robust phylogeny. Even though species in the Aspergillus and Penicillium genera (family Aspergillaceae, class Eurotiomycetes) are some of the most significant technologically and medically relevant fungi, we still lack a genome-scale phylogeny of the lineage or knowledge of the parts of the phylogeny that exhibit conflict among analyses. Here, we used a phylogenomic approach to infer evolutionary relationships among 81 genomes that span the diversity of Aspergillus and Penicillium species, to identify conflicts in the phylogeny, and to determine the likely underlying factors of the observed conflicts. Using a data matrix comprised of 1,668 genes, we found that while most branches of the phylogeny of the Aspergillaceae are robustly supported and recovered irrespective of method of analysis, a few exhibit various degrees of conflict among our analyses. Further examination of the observed conflict revealed that it largely stems from incomplete lineage sorting and hybridization or introgression. Our analyses provide a robust and comprehensive evolutionary genomic roadmap for this important lineage, which will facilitate the examination of the diverse technologically and medically relevant traits of these fungi in an evolutionary context.

          ABSTRACT

          The filamentous fungal family Aspergillaceae contains >1,000 known species, mostly in the genera Aspergillus and Penicillium. Several species are used in the food, biotechnology, and drug industries (e.g., Aspergillus oryzae and Penicillium camemberti), while others are dangerous human and plant pathogens (e.g., Aspergillus fumigatus and Penicillium digitatum). To infer a robust phylogeny and pinpoint poorly resolved branches and their likely underlying contributors, we used 81 genomes spanning the diversity of Aspergillus and Penicillium to construct a 1,668-gene data matrix. Phylogenies of the nucleotide and amino acid versions of this full data matrix as well as of several additional data matrices were generated using three different maximum likelihood schemes (i.e., gene-partitioned, unpartitioned, and coalescence) and using both site-homogenous and site-heterogeneous models (total of 64 species-level phylogenies). Examination of the topological agreement among these phylogenies and measures of internode certainty identified 11/78 (14.1%) bipartitions that were incongruent and pinpointed the likely underlying contributing factors, which included incomplete lineage sorting, hidden paralogy, hybridization or introgression, and reconstruction artifacts associated with poor taxon sampling. Relaxed molecular clock analyses suggest that Aspergillaceae likely originated in the lower Cretaceous and that the Aspergillus and Penicillium genera originated in the upper Cretaceous. Our results shed light on the ongoing debate on Aspergillus systematics and taxonomy and provide a robust evolutionary and temporal framework for comparative genomic analyses in Aspergillaceae. More broadly, our approach provides a general template for phylogenomic identification of resolved and contentious branches in densely genome-sequenced lineages across the tree of life.

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          TimeTree: a public knowledge-base of divergence times among organisms.

          Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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            Fungal secondary metabolism - from biochemistry to genomics.

            Much of natural product chemistry concerns a group of compounds known as secondary metabolites. These low-molecular-weight metabolites often have potent physiological activities. Digitalis, morphine and quinine are plant secondary metabolites, whereas penicillin, cephalosporin, ergotrate and the statins are equally well known fungal secondary metabolites. Although chemically diverse, all secondary metabolites are produced by a few common biosynthetic pathways, often in conjunction with morphological development. Recent advances in molecular biology, bioinformatics and comparative genomics have revealed that the genes encoding specific fungal secondary metabolites are clustered and often located near telomeres. In this review, we address some important questions, including which evolutionary pressures led to gene clustering, why closely related species produce different profiles of secondary metabolites, and whether fungal genomics will accelerate the discovery of new pharmacologically active natural products.
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              ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes

              Motivation: The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed ‘bipartitions’. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent. Results: We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL’s running time is O ( n 2 k | X | 2 ) , and ASTRAL-II’s running time is O ( n k | X | 2 ) , where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space. Availability and implementation: ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/. Contact: smirarab@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                9 July 2019
                Jul-Aug 2019
                : 10
                : 4
                : e00925-19
                Affiliations
                [a ]Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
                [b ]Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
                [c ]Gladstone Institute for Data Science and Biotechnology, San Francisco, California, USA
                [d ]Departamento de Ciências Farmacêuticas, Faculdade de Ciências Farmacêuticas de Ribeirão Prêto, Universidade de São Paulo, São Paulo, Brazil
                University of Pittsburgh
                Author notes
                Address correspondence to Antonis Rokas, antonis.rokas@ 123456vanderbilt.edu .
                Author information
                https://orcid.org/0000-0002-8436-595X
                https://orcid.org/0000-0001-5765-1419
                https://orcid.org/0000-0002-9579-4178
                https://orcid.org/0000-0002-2986-350X
                https://orcid.org/0000-0002-7248-6551
                Article
                mBio00925-19
                10.1128/mBio.00925-19
                6747717
                31289177
                28873055-185a-446b-b2ed-c5793657b2fe
                Copyright © 2019 Steenwyk et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 11 April 2019
                : 12 June 2019
                Page count
                Figures: 6, Tables: 1, Equations: 4, References: 154, Pages: 25, Words: 16779
                Funding
                Funded by: Guggenheim Foundation;
                Award Recipient :
                Funded by: National Science Foundation (NSF), https://doi.org/10.13039/100000001;
                Award ID: DEB-1442113
                Award Recipient :
                Categories
                Research Article
                Ecological and Evolutionary Science
                Custom metadata
                July/August 2019

                Life sciences
                ascomycota,eurotiales,eurotiomycetes,genomics,incongruence,international code of nomenclature,narrow aspergillus,phylogenetics,phylogenomics,secondary metabolism

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