Meiotic Recombination: Mixing It Up in Plants

Wang, Yingxiang; Copenhaver, Gregory P.

doi:10.1146/annurev-arplant-042817-040431

Cited by 194 publications

(239 citation statements)

References 259 publications

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“…This bias in the distribution of recombination events is particularly pronounced in the large grass family (Poaceae), including cereal crops (Higgins et al , 2014). Meiotic function is evolutionarily well conserved even though the amino acid sequences of recombination and particularly SC proteins are variable in different species (Grishaeva and Bogdanov, 2017; Wang and Copenhaver, 2018). Phylogenomic, mutagenic, and immuno-cytological tools have been extensively used to clone and characterise plant orthologues of meiotic genes (Lambing et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Time-resolved transcriptome of barley anthers and meiocytes reveals robust and largely stable gene expression changes at meiosis entry

Barakate

Orr

Schreiber

et al. 2020

Preprint

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In flowering plants, successful germinal cell development and meiotic recombination depend upon a combination of environmental and genetic factors. To gain insights into this specialised reproductive development programme we used short- and long-read RNA-sequencing (RNA-seq) to study the temporal dynamics of transcript abundance in immuno-cytologically staged barley (Hordeum vulgare) anthers and meiocytes. We show that the most significant transcriptional changes occur at the transition from pre-meiosis to leptotene–zygotene, which is followed by largely stable transcript abundance throughout prophase I. Our analysis reveals that the developing anthers and meiocytes are enriched in long non-coding RNAs (lncRNAs) and that entry to meiosis is characterized by their robust and significant down regulation. Intriguingly, only 24% of a collection of putative meiotic gene orthologues showed differential transcript abundance in at least one stage or tissue comparison. Changes in the abundance of numerous transcription factors, representatives of the small RNA processing machinery, and post-translational modification pathways highlight the complexity of the regulatory networks involved. These developmental, time-resolved, and dynamic transcriptomes increase our understanding of anther and meiocyte development and will help guide future research.One sentence summaryAnalysis of RNA-seq data from meiotically staged barley anthers and meiocytes highlights the role of lncRNAs within a complex network of transcriptional and post-transcriptional regulation accompanied by a hiatus in differential gene expression during prophase I.The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Robbie Waugh (robbie.waugh@hutton.ac.uk)

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Section: Introductionmentioning

confidence: 99%

Time-resolved transcriptome of barley anthers and meiocytes reveals robust and largely stable gene expression changes at meiosis entry

Barakate

Orr

Schreiber

et al. 2020

Preprint

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“…Accurate separation of chromosomes depends on the successful completion of homologous recombination and formation of meiotic crossovers (COs). COs establish physical links between homologs, which play a vital role in balancing the opposite pulling force of the spindle at metaphase I (Wang and Copenhaver, 2018).…”

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confidence: 99%

“…In budding yeast (Saccharomyces cerevisiae), synaptonemal complex (SC) assembly and interference-sensitive CO formation rely on a group of functionally related proteins consisting of molecular zipper 1 (ZIP1), ZIP2, ZIP3, ZIP4, MutS homologue 4 (MSH4), MSH5, meiotic recombination 3, and sporulation 16 (SPO16; ZMM proteins; Lynn et al, 2007;Shinohara et al, 2008). It has been demonstrated that ZMM orthologs are evolutionarily conserved in many other eukaryotes, including the model plants Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa; Wang and Copenhaver, 2018). ZIP1 encodes the transverse filament protein of the SC.…”

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confidence: 99%

A Multiprotein Complex Regulates Interference-Sensitive Crossover Formation in Rice

et al. 2019

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“…At least two pathways (Type I and Type II), contribute to CO formation 2,3 . The Type I pathway leads to interfering COs that prevent the coincident occurrence of closely spaced CO on the same pair of chromosomes 2,8,9 .…”

Section: Mainmentioning

confidence: 99%

DeepTetrad: high-throughput analysis of meiotic tetrads by deep learning in plants

Lim

Kim

Park

et al. 2019

Preprint

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15Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles 16 in gametes. Crossover frequency varies along chromosomes and crossover interference limits 17 the coincidence of closely spaced crossovers. Crossovers can be measured by observing the 18 inheritance of linked transgenes expressing different colors of fluorescent protein in 19 Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image 20 recognition package for pollen tetrad analysis that enables high-throughput measurements of 21 crossover frequency and interference in individual plants. DeepTetrad will accelerate genetic 22 dissection of mechanisms that control meiotic recombination. 23 24 Main 25 Meiosis consists of two consecutive nuclear divisions and produces four haploid gametes from 26 a single diploid cell in sexually reproducing eukaryotes 1 . In Arabidopsis male meiosis, ~200-27 DeepTetrad 2 250 meiotic DNA double-strand breaks (DSBs) are induced in the genome by a DNA 28 topoisomerase VI-like complex to initiate meiotic recombination 2-4 . Of these DSBs, only ~8-29 11 are repaired as crossovers (COs) using a homologous chromosome (homolog). Thus, male 30 meiosis in the Arabidopsis genome, which comprises five chromosomes, results in an average 31 of ~1.8 crossovers between homologs. This low number suggests the existence of 32 mechanisms that limit crossovers, a phenomenon that is observed in most eukaryotes 2 .33 Meiotic DSB and CO frequencies are controlled by genetic and epigenetic factors and are 34 non-randomly distributed along chromosomes, with higher levels around gene promoters and 35 terminators and lower levels across the centromeres 5-7 . 36 At least two pathways (Type I and Type II), contribute to CO formation 2,3 . The Type I pathway 37 leads to interfering COs that prevent the coincident occurrence of closely spaced CO on the 38 same pair of chromosomes 2,8,9 . In plants, interfering COs represent ~80-85% of total COs and 39 are dependent on the ZMM proteins (ZIP4, MSH4, MSH5, MER3, HEI10, SHOC1, PTD, MLH1, 40 MLH3). The remaining ~10-15% of COs are non-interfering and occur via the Type II 41 pathway 10 . Non-interfering COs are resolved by the MUS81 endonuclease and are restricted 42 by anti-recombination factors such as FANCM, RECQ4A, RECQ4B, and FIGL1 11-15 . 43 Disruption of anti-recombination factors can increase the number of Type II COs in plants, 44 which has the potential to create new combinations of desirable alleles that can improve crop 45 varieties 13,16 . Therefore, high-throughput detection and understanding of CO frequency and 46 interference have important implications for our understanding of the control of meiotic 47 QRT1 gene encoding a pectin methylestrase results in the four pollen products of male 51 meiosis remaining attached to one another, allowing classical tetrad analysis. Each FTL has 52 a transgenes that expresses eYFP (Y), dsRed (R) or eCFP (C) fluorescent proteins in mature 53 pollen using the post-meiotic LAT52 promoter. Gen...

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Meiotic Recombination: Mixing It Up in Plants

Cited by 194 publications

References 259 publications

Time-resolved transcriptome of barley anthers and meiocytes reveals robust and largely stable gene expression changes at meiosis entry

Time-resolved transcriptome of barley anthers and meiocytes reveals robust and largely stable gene expression changes at meiosis entry

A Multiprotein Complex Regulates Interference-Sensitive Crossover Formation in Rice

DeepTetrad: high-throughput analysis of meiotic tetrads by deep learning in plants

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