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      Combinations of Spok genes create multiple meiotic drivers in Podospora

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          Abstract

          Meiotic drive is the preferential transmission of a particular allele during sexual reproduction. The phenomenon is observed as spore killing in multiple fungi. In natural populations of Podospora anserina, seven spore killer types ( Psks) have been identified through classical genetic analyses. Here we show that the Spok gene family underlies the Psks. The combination of Spok genes at different chromosomal locations defines the spore killer types and creates a killing hierarchy within a population. We identify two novel Spok homologs located within a large (74–167 kbp) region (the Spok block) that resides in different chromosomal locations in different strains. We confirm that the SPOK protein performs both killing and resistance functions and show that these activities are dependent on distinct domains, a predicted nuclease and kinase domain. Genomic and phylogenetic analyses across ascomycetes suggest that the Spok genes disperse through cross-species transfer, and evolve by duplication and diversification within lineages.

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          In many organisms, most cells carry two versions of a given gene, one coming from the mother and the other from the father. An exception is sexual cells such as eggs, sperm, pollen or spores, which should only contain one variant of a gene. During their formation, these cells usually have an equal chance of inheriting one of the two gene versions.

          However, a certain class of gene variants called meiotic drivers can cheat this process and end up in more than half of the sexual cells; often, the cells that contain the drivers can kill sibling cells that do not carry these variants. This results in the selfish genetic elements spreading through populations at a higher rate, sometimes with severe consequences such as shifting the ratio of males to females.

          Meiotic drivers have been discovered in a wide range of organisms, from corn to mice to fruit flies and bread mold. They also exist in the fungus Podospora anserina, where they are called ‘spore killers’. Fungi are often used to study complex genetic processes, yet the identity and mode of action of spore killers in P. anserina were still unknown.

          Vogan, Ament-Velásquez et al. used a combination of genetic methods to identify three genes from the Spok family which are responsible for certain spores being able to kill their siblings. Two of these were previously unknown, and they could be found in different locations throughout the genome as part of a larger genetic region. Depending on the combination of Spok genes it carries, a spore can kill or be protected against other spores that contain different permutations of the genes. Copies of these genes were also shown to be present in other fungi, including species that are a threat to crops.

          Scientists have already started to create synthetic meiotic drivers to manipulate how certain traits are inherited within a population. This could be useful to control or eradicate pests and insects that transmit dangerous diseases. The results by Vogan, Ament-Velásquez et al. shine a light on the complex ways that natural meiotic drivers work, including how they can be shared between species; this knowledge could inform how to safely deploy synthetic drivers in the wild.

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          Most cited references73

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          Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

          We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.
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            PrDOS: prediction of disordered protein regions from amino acid sequence

            PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.
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              MACSE v2: Toolkit for the Alignment of Coding Sequences Accounting for Frameshifts and Stop Codons

              Abstract Multiple sequence alignment is a prerequisite for many evolutionary analyses. Multiple Alignment of Coding Sequences (MACSE) is a multiple sequence alignment program that explicitly accounts for the underlying codon structure of protein-coding nucleotide sequences. Its unique characteristic allows building reliable codon alignments even in the presence of frameshifts. This facilitates downstream analyses such as selection pressure estimation based on the ratio of nonsynonymous to synonymous substitutions. Here, we present MACSE v2, a major update with an improved version of the initial algorithm enriched with a complete toolkit to handle multiple alignments of protein-coding sequences. A graphical interface now provides user-friendly access to the different subprograms.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                26 July 2019
                2019
                : 8
                : e46454
                Affiliations
                [1 ]deptOrganismal biology Uppsala University UppsalaSweden
                [2 ]University of Bordeaux BordeauxFrance
                [3 ]Wageningen University WageningenNetherlands
                Vanderbilt University United States
                Max-Planck Institute for Evolutionary Biology Germany
                Vanderbilt University United States
                Stowers Institute for Medical Research United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-2013-7445
                https://orcid.org/0000-0003-3371-9292
                https://orcid.org/0000-0001-6359-9856
                Article
                46454
                10.7554/eLife.46454
                6660238
                31347500
                28b13465-2621-4572-a6de-527a9420399f
                © 2019, Vogan et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 27 February 2019
                : 09 June 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: ERC-2014-CoG
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Swedish Research Council;
                Award ID: Spore killer genomics
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004722, Lars Hierta Memorial Foundation;
                Award ID: Podospora genomics
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100005753, Royal Physiographic Society in Lund;
                Award ID: Podospora genomics
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Evolutionary Biology
                Genetics and Genomics
                Custom metadata
                Members of a single gene family determine the genomic basis of multiple coexisting meiotic drive elements in natural populations of Podospora.

                Life sciences
                podospora,selfish genetic element,spore killer,genomic conflict,fungi,gene drive,other
                Life sciences
                podospora, selfish genetic element, spore killer, genomic conflict, fungi, gene drive, other

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