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      Transcriptional reprogramming in the entomopathogenic fungus Metarhizium brunneum and its aphid host Myzus persicae during the switch between saprophytic and parasitic lifestyles

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          Abstract

          Background

          The fungus Metarhizium brunneum has evolved a remarkable ability to switch between different lifestyles. It develops as a saprophyte, an endophyte establishing mutualistic relationships with plants, or a parasite, enabling its use for the control of insect pests such as the aphid Myzus persicae. We tested our hypothesis that switches between lifestyles must be accompanied by fundamental transcriptional reprogramming, reflecting adaptations to different environmental settings.

          Results

          We combined high throughput RNA sequencing of M. brunneum in vitro and at different stages of pathogenesis to validate the modulation of genes in the fungus and its host during the course of infection. In agreement with our hypothesis, we observed transcriptional reprogramming in M. brunneum following conidial attachment, germination on the cuticle, and early-stage growth within the host. This involved the upregulation of genes encoding degrading enzymes and gene clusters involved in synthesis of secondary metabolites that act as virulence factors. The transcriptional response of the aphid host included the upregulation of genes potentially involved in antifungal activity, but antifungal peptides were not induced. We also observed the induction of a host flightin gene, which may be involved in wing formation and flight muscle development.

          Conclusions

          The switch from saprophytic to parasitic development in M. brunneum is accompanied by fundamental transcriptional reprogramming during the course of the infection. The aphid host responds to fungal infection with its own transcriptional reprogramming, reflecting its inability to express antifungal peptides but featuring the induction of genes involved in winged morphs that may enable offspring to avoid the contaminated environment.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-024-10824-y.

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          Most cited references107

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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              Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

              The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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                Author and article information

                Contributors
                danam@volcani.agri.gov.il
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                2 October 2024
                2 October 2024
                2024
                : 25
                : 917
                Affiliations
                [1 ]Department of Plant Pathology and Weed Research, Agricultural Research Organization, Volcani Center, ( https://ror.org/05hbrxp80) Rishon LeZion, Israel
                [2 ]The Robert H. Smith Faculty of Agriculture, The Hebrew University of Jerusalem, ( https://ror.org/03qxff017) Food & Environment, Rehovot, Israel
                [3 ]GRID grid.410498.0, ISNI 0000 0001 0465 9329, Institute of Plant Science, , ARO, The Volcani Institute, ; Rishon Le Zion, Israel
                [4 ]Institute for Insect Biotechnology, Justus Liebig Universität Giessen, ( https://ror.org/033eqas34) Giessen, 35392 Germany
                [5 ]GRID grid.418010.c, ISNI 0000 0004 0573 9904, Branch Bioresources of the Fraunhofer Institute for Molecular Biology and Applied Ecology, ; Giessen, 35392 Germany
                Article
                10824
                10.1186/s12864-024-10824-y
                11446092
                39358701
                1656da73-874d-4414-82c4-0c138b8d6e36
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 15 June 2024
                : 23 September 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                mycopathogen,host–pathogen interaction,transcriptomics,live imaging,fungal infection,early infection

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