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      Convergent selection of a WD40 protein that enhances grain yield in maize and rice

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          Abstract

          A better understanding of the extent of convergent selection among crops could greatly improve breeding programs. We found that the quantitative trait locus KRN2 in maize and its rice ortholog, OsKRN2 , experienced convergent selection. These orthologs encode WD40 proteins and interact with a gene of unknown function, DUF1644, to negatively regulate grain number in both crops. Knockout of KRN2 in maize or OsKRN2 in rice increased grain yield by ~10% and ~8%, respectively, with no apparent trade-offs in other agronomic traits. Furthermore, genome-wide scans identified 490 pairs of orthologous genes that underwent convergent selection during maize and rice evolution, and these were enriched for two shared molecular pathways. KRN2 , together with other convergently selected genes, provides an excellent target for future crop improvement.

          Amazing grains

          Maize and rice are important sources of human calories and have been, mostly independently, subject to human selection for thousands of years, often for similar traits such as grain yield. W. Chen et al . examined the genomes of accessions of domestic maize and its wild relative, teosinte, for evolutionary signals of selection. From these sequences, the authors identified a quantitative trait locus in maize that increased kernel row number. Fine mapping determined that this locus contains a candidate gene, KRN2 . Gene-editing experiments of KRN2 and its homolog in rice determined that a similar phenotype increasing grain number per plant could be recapitulated. Thus, identifying genes under selection in one cereal provides useful fodder for crop improvements. —LMZ

          Abstract

          Convergent selection of KRN2 and other genes in maize and rice provide insight for crop improvement.

          Abstract

          INTRODUCTION

          During the independent process of cereal evolution, many trait shifts appear to have been under convergent selection to meet the specific needs of humans. Identification of convergently selected genes across cereals could help to clarify the evolution of crop species and to accelerate breeding programs. In the past several decades, researchers have debated whether convergent phenotypic selection in distinct lineages is driven by conserved molecular changes or by diverse molecular pathways. Two of the most economically important crops, maize and rice, display some conserved phenotypic shifts—including loss of seed dispersal, decreased seed dormancy, and increased grain number during evolution—even though they experienced independent selection. Hence, maize and rice can serve as an excellent system for understanding the extent of convergent selection among cereals.

          RATIONALE

          Despite the identification of a few convergently selected genes, our understanding of the extent of molecular convergence on a genome-wide scale between maize and rice is very limited. To learn how often selection acts on orthologous genes, we investigated the functions and molecular evolution of the grain yield quantitative trait locus KRN2 in maize and its rice ortholog OsKRN2 . We also identified convergently selected genes on a genome-wide scale in maize and rice, using two large datasets.

          RESULTS

          We identified a selected gene, KRN2 ( kernel row number2 ), that differs between domesticated maize and its wild ancestor, teosinte. This gene underlies a major quantitative trait locus for kernel row number in maize. Selection in the noncoding upstream regions resulted in a reduction of KRN2 expression and an increased grain number through an increase in kernel rows. The rice ortholog, OsKRN2 , also underwent selection and negatively regulates grain number via control of secondary panicle branches. These orthologs encode WD40 proteins and function synergistically with a gene of unknown function, DUF1644, which suggests that a conserved protein interaction controls grain number in maize and rice. Field tests show that knockout of KRN2 in maize or OsKRN2 in rice increased grain yield by ~10% and ~8%, respectively, with no apparent trade-off in other agronomic traits. This suggests potential applications of KRN2 and its orthologs for crop improvement.

          On a genome-wide scale, we identified a set of 490 orthologous genes that underwent convergent selection during maize and rice evolution, including KRN2/OsKRN2 . We found that the convergently selected orthologous genes appear to be significantly enriched in two specific pathways in both maize and rice: starch and sucrose metabolism, and biosynthesis of cofactors. A deep analysis of convergently selected genes in the starch metabolic pathway indicates that the degree of genetic convergence via convergent selection is related to the conservation and complexity of the gene network for a given selection.

          CONCLUSION

          Our findings show that common phenotypic shifts during maize and rice evolution acting on conserved genes are driven at least in part by convergent selection, which in maize and rice likely occurred both during and after domestication. We provide evolutionary and functional evidence on the convergent selection of KRN2/OsKRN2 for grain number between maize and rice. We further found that a complete loss-of-function allele of KRN2/OsKRN2 increased grain yield without an apparent negative impact on other agronomic traits. Exploring the role of KRN2/OsKRN2 and other convergently selected genes across the cereals could provide new opportunities to enhance the production of other global crops.

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          Most cited references82

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
            Bookmark
            • Record: found
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            Is Open Access

            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
                Bookmark

                Author and article information

                Contributors
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                March 25 2022
                March 25 2022
                : 375
                : 6587
                Affiliations
                [1 ]State Key Laboratory of Plant Physiology and Biochemistry and National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China.
                [2 ]National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
                [3 ]Hubei Hongshan Laboratory, Wuhan 430070, China.
                [4 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
                [5 ]Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing 100193, China.
                [6 ]Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, Jiangsu 225009, China.
                [7 ]Department of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
                Article
                10.1126/science.abg7985
                35324310
                05854eac-6f5c-469b-bdc0-399ec17202f0
                © 2022
                History

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