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      The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics

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          Abstract

          Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.

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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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            Natural products in drug discovery: advances and opportunities

            Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.
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              Nothing in Biology Makes Sense except in the Light of Evolution

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                Author and article information

                Contributors
                Journal
                Front Fungal Biol
                Front Fungal Biol
                Front. Fungal Biol.
                Frontiers in Fungal Biology
                Frontiers Media S.A.
                2673-6128
                30 June 2023
                2023
                : 4
                : 1214537
                Affiliations
                [1] 1Department of Biostatistics, Yale School of Public Health , New Haven, CT, United States
                [2] 2Korean Lichen Research Institute, Sunchon National University , Suncheon, Republic of Korea
                [3] 3Department of Plant Pathology and Microbiology, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem , Rehovot, Israel
                [4] 4Institute of Microbiology, Chinese Academy of Sciences , Beijing, China
                [5] 5Department of Plant Biology, Michigan State University , East Lansing, MI, United States
                [6] 6Department of Plant, Soil and Microbial Sciences, Michigan State University , East Lansing, MI, United States
                [7] 7Department of Ecology and Evolutionary Biology, Program in Microbiology, and Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT, United States
                Author notes

                Edited by: Andrei S. Steindorff, Berkeley Lab (DOE), United States

                Reviewed by: Paul Daly, Jiangsu Academy of Agricultural Sciences (JAAS), China; Lucia Ramirez, Public University of Navarre, Spain; Irina Druzhinina, Royal Botanic Gardens, Kew, United Kingdom

                *Correspondence: Zheng Wang, wang.zheng@ 123456yale.edu ; Oded Yarden, oded.yarden@ 123456mail.huji.ac.il
                Article
                10.3389/ffunb.2023.1214537
                10512317
                37746130
                8ed17ee9-6dc0-40cf-9d34-4ad35f64bdb5
                Copyright © 2023 Wang, Kim, Wang, Yakubovich, Dong, Trail, Townsend and Yarden

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 April 2023
                : 06 June 2023
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 277, Pages: 19, Words: 9026
                Funding
                Funded by: National Science Foundation , doi 10.13039/100000001;
                Award ID: IOS1457044
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 32272786
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Funded by: United States - Israel Binational Science Foundation , doi 10.13039/100006221;
                Funded by: National Research Foundation of Korea , doi 10.13039/501100003725;
                The work of ZW, Y-WW, and JT was supported by funding awarded to JT by the National Institutes of Health R01, grant AI146584, and by the National Science Foundation, grant IOS 1457044; the work of EY and OY was supported by funding award BSF-2018712 to OY. The work of CD was supported by the National Natural Science Foundation of China (32272786 and 31872163). FT was supported by the National Institutes of Health, R01 grant AI146584, and the Michigan State University AgBioResearch. The work of WK and FT was supported by the National Science Foundation, IOS 1456482, awarded to FT. WK was partly supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (2019R1I1A1A01057502).
                Categories
                Fungal Biology
                Review
                Custom metadata
                Fungal Genomics and Evolution

                sordariomycetes,evolution,genomics,transcriptomics,big data,neurospora,fusarium,trichoderma

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