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      Epicuticular wax accumulation and regulation of wax pathway gene expression during bioenergy Sorghum stem development

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          Abstract

          Bioenergy sorghum is a drought-tolerant high-biomass C4 grass targeted for production on annual cropland marginal for food crops due primarily to abiotic constraints. To better understand the overall contribution of stem wax to bioenergy sorghum’s resilience, the current study characterized sorghum stem cuticular wax loads, composition, morphometrics, wax pathway gene expression and regulation using vegetative phase Wray, R07020, and TX08001 genotypes. Wax loads on sorghum stems (~103-215 µg/cm 2) were much higher than Arabidopsis stem and leaf wax loads. Wax on developing sorghum stem internodes was enriched in C28/30 primary alcohols (~65%) while stem wax on fully developed stems was enriched in C28/30 aldehydes (~80%). Scanning Electron Microscopy showed minimal wax on internodes prior to the onset of elongation and that wax tubules first appear associated with cork-silica cell complexes when internode cell elongation is complete. Sorghum homologs of genes involved in wax biosynthesis/transport were differentially expressed in the stem epidermis. Expression of many wax pathway genes (i.e., SbKCS6, SbCER3-1, SbWSD1, SbABCG12, SbABCG11) is low in immature apical internodes then increases at the onset of stem wax accumulation. SbCER4 is expressed relatively early in stem development consistent with accumulation of C28/30 primary alcohols on developing apical internodes. High expression of two SbCER3 homologs in fully elongated internodes is consistent with a role in production of C28/30 aldehydes. Gene regulatory network analysis aided the identification of sorghum homologs of transcription factors that regulate wax biosynthesis (i.e., SbSHN1, SbWRI1/3, SbMYB94/96/30/60, MYS1) and other transcription factors that could regulate and specify expression of the wax pathway in epidermal cells during cuticle development.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            MEGA11: Molecular Evolutionary Genetics Analysis Version 11

            The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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              MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

              Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                23 October 2023
                2023
                : 14
                : 1227859
                Affiliations
                [1] 1 Department of Biochemistry & Biophysics, Texas A&M University , College Station, TX, United States
                [2] 2 Department of Soil and Crop Sciences, Texas A&M University , College Station, TX, United States
                Author notes

                Edited by: Laura Rossini, University of Milan, Italy

                Reviewed by: David Pot, Institut National de la Recherche Agronomique (INRA), France; Agnieszka Zienkiewicz, Nicolaus Copernicus University in Toruń, Poland

                *Correspondence: John E. Mullet, john.mullet@ 123456ag.tamu.edu

                †ORCID: Robert Chemelewski, orcid.org/0000-0001-7739-2741; John E. Mullet, orcid.org/0000-0003-2502-2671

                Article
                10.3389/fpls.2023.1227859
                10626490
                37936930
                4ccc3d23-dbaa-4b9d-bcae-8459c86dfb11
                Copyright © 2023 Chemelewski, McKinley, Finlayson and Mullet

                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
                : 26 May 2023
                : 11 September 2023
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 120, Pages: 18, Words: 10456
                Funding
                Funded by: Great Lakes Bioenergy Research Center , doi 10.13039/100015814;
                This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science Grant/Award: DE-SC0018409).
                Categories
                Plant Science
                Original Research
                Custom metadata
                Plant Development and EvoDevo

                Plant science & Botany
                sorghum,bioenergy,stem cuticular wax,scanning electron microscopy,wax load,gene regulatory network analysis,policosanol

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