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      Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis

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          Abstract

          Background

          The dominant fungi in arid grasslands and shrublands are members of the Ascomycota phylum. Ascomycota fungi are important drivers in carbon and nitrogen cycling in arid ecosystems. These fungi play roles in soil stability, plant biomass decomposition, and endophytic interactions with plants. They may also form symbiotic associations with biocrust components or be latent saprotrophs or pathogens that live on plant tissues. However, their functional potential in arid soils, where organic matter, nutrients and water are very low or only periodically available, is poorly characterized.

          Results

          Five Ascomycota fungi were isolated from different soil crust microhabitats and rhizosphere soils around the native bunchgrass Pleuraphis jamesii in an arid grassland near Moab, UT, USA. Putative genera were Coniochaeta, isolated from lichen biocrust, Embellisia from cyanobacteria biocrust , Chaetomium from below lichen biocrust, Phoma from a moss microhabitat, and Aspergillus from the soil. The fungi were grown in replicate cultures on different carbon sources (chitin, native bunchgrass or pine wood) relevant to plant biomass and soil carbon sources. Secretomes produced by the fungi on each substrate were characterized. Results demonstrate that these fungi likely interact with primary producers (biocrust or plants) by secreting a wide range of proteins that facilitate symbiotic associations. Each of the fungal isolates secreted enzymes that degrade plant biomass, small secreted effector proteins, and proteins involved in either beneficial plant interactions or virulence. Aspergillus and Phoma expressed more plant biomass degrading enzymes when grown in grass- and pine-containing cultures than in chitin. Coniochaeta and Embellisia expressed similar numbers of these enzymes under all conditions, while Chaetomium secreted more of these enzymes in grass-containing cultures.

          Conclusions

          This study of Ascomycota genomes and secretomes provides important insights about the lifestyles and the roles that Ascomycota fungi likely play in arid grassland, ecosystems. However, the exact nature of those interactions, whether any or all of the isolates are true endophytes, latent saprotrophs or opportunistic phytopathogens, will be the topic of future studies.

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          Most cited references135

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          antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

          Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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            A new generation of homology search tools based on probabilistic inference.

            Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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              Novel enzymes for the degradation of cellulose

              The bulk terrestrial biomass resource in a future bio-economy will be lignocellulosic biomass, which is recalcitrant and challenging to process. Enzymatic conversion of polysaccharides in the lignocellulosic biomass will be a key technology in future biorefineries and this technology is currently the subject of intensive research. We describe recent developments in enzyme technology for conversion of cellulose, the most abundant, homogeneous and recalcitrant polysaccharide in lignocellulosic biomass. In particular, we focus on a recently discovered new type of enzymes currently classified as CBM33 and GH61 that catalyze oxidative cleavage of polysaccharides. These enzymes promote the efficiency of classical hydrolytic enzymes (cellulases) by acting on the surfaces of the insoluble substrate, where they introduce chain breaks in the polysaccharide chains, without the need of first “extracting” these chains from their crystalline matrix.
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                Author and article information

                Contributors
                Jean.Challacombe@colostate.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                12 December 2019
                12 December 2019
                2019
                : 20
                : 976
                Affiliations
                [1 ]ISNI 0000 0004 0428 3079, GRID grid.148313.c, Bioscience Division, , Los Alamos National Laboratory, ; Los Alamos, NM 87545 USA
                [2 ]ISNI 0000 0004 1936 8083, GRID grid.47894.36, Present address: Colorado State University, College of Agricultural Sciences, ; 301 University Ave, Fort Collins, CO 80523 USA
                [3 ]ISNI 0000 0004 0404 0958, GRID grid.463419.d, Horticultural Crops Research, USDA ARS, ; Corvallis, OR USA
                [4 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Applied Statistics & Computational Modeling, , Pacific Northwest National Laboratory, ; Richland, Washington, USA
                [5 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Biological Sciences Division, , Pacific Northwest National Laboratory, ; Richland, Washington, 99352 USA
                [6 ]ISNI 0000 0001 2179 1284, GRID grid.268180.5, Department of Biology, , Western Illinois University, ; Macomb, IL 61455 USA
                Author information
                http://orcid.org/0000-0002-8624-0413
                Article
                6358
                10.1186/s12864-019-6358-x
                6909477
                31830917
                abd2029a-18e8-4e02-8c37-d6b08d7621da
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 4 February 2019
                : 1 December 2019
                Funding
                Funded by: EMSL High-Performance Mass Spectrometry Facility
                Award ID: 47926
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Award ID: Science Focus Area Grant
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                ascomycota,fungi,arid,grassland,soil,biocrust,genome,secretome,lifestyle,plants
                Genetics
                ascomycota, fungi, arid, grassland, soil, biocrust, genome, secretome, lifestyle, plants

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