A SWEET solution to rice blight

Bacterial blight is an important disease of rice that is particularly destructive in Southeast Asia and sub-Saharan Africa, exacerbated by the heavy rains of the monsoon seasons. Estimated crop loss due to bacterial blight may be as high as 75%, with millions of hectares of rice affected annually. In this issue, an international team of researchers describes the use of CRISPR editing to generate rice plants that are broadly resistant to the main pathogen that causes rice blight, Xanthomonas oryzae pv. oryzae (Xoo)1. To enhance the durability and management of resistance, the team has also developed a kit to trace the disease, and its virulence and resistance alleles2

New horizons for plant translational research

In this issue, we launch a new article collection "The Promise of Plant Translational Research," featuring articles from leading plant researchers and call for additional plant translational research to be submitted to PLOS Biology for inclusion in this collection. We also discuss in this Editorial why this field has a vital role to play in meeting the challenges of sustainably feeding a growing world population

Reap the crop wild relatives for breeding future crops

Crop wild relatives (CWRs) have provided breeders with several 'game-changing' traits or genes that have boosted crop resilience and global agricultural production. Advances in breeding and genomics have accelerated the identification of valuable CWRs for use in crop improvement. The enhanced genetic diversity of breeding pools carrying optimum combinations of favorable alleles for targeted crop-growing regions is crucial to sustain genetic gain. In parallel, growing sequence information on wild genomes in combination with precise gene-editing tools provide a fast-track route to transform CWRs into ideal future crops. Data-informed germplasm collection and management strategies together with adequate policy support will be equally important to improve access to CWRs and their sustainable use to meet food and nutrition security targets

USDA Plant Genome Research Program

The U.S. Congress appropriated funds in 1991 for the USDA Plant Genome Research Program, four years after its initial conception in 1987. The goal of the USDA Plant Genome Research Program is to improve plants (agronomic, horticultural, and forest tree species) by locating marker DNA or genes on chromosomes, determining gene structure, and transferring genes to improve plant performance with accompanying reduced environmental impact to meet marketplace needs and niches. The Plant Genome Research Program is one program with two parts: National Research Initiative and Plant Genome Database (PGD). The PGD is now a real and functioning information and data resource for agricultural and other plant science genome researchers, and it is in the public domain. Additional progress is given according to major plant groups. The PGD is a suite of several information products produced at the National Agricultural Library (NAL) in collaboration with the Agricultural Research Service and Forest Service species coordinators

Hybrid assembly with long and short reads improves discovery of gene family expansions

BACKGROUND: Long-read and short-read sequencing technologies offer competing advantages for eukaryotic genome sequencing projects. Combinations of both may be appropriate for surveys of within-species genomic variation. METHODS: We developed a hybrid assembly pipeline called "Alpaca" that can operate on 20X long-read coverage plus about 50X short-insert and 50X long-insert short-read coverage. To preclude collapse of tandem repeats, Alpaca relies on base-call-corrected long reads for contig formation. RESULTS: Compared to two other assembly protocols, Alpaca demonstrated the most reference agreement and repeat capture on the rice genome. On three accessions of the model legume Medicago truncatula, Alpaca generated the most agreement to a conspecific reference and predicted tandemly repeated genes absent from the other assemblies. CONCLUSION: Our results suggest Alpaca is a useful tool for investigating structural and copy number variation within de novo assemblies of sampled populations

Natural history of Arabidopsis thaliana and oomycete symbioses

Molecular ecology of plant–microbe interactions has immediate significance for filling a gap in knowledge between the laboratory discipline of molecular biology and the largely theoretical discipline of evolutionary ecology. Somewhere in between lies conservation biology, aimed at protection of habitats and the diversity of species housed within them. A seemingly insignificant wildflower called Arabidopsis thaliana has an important contribution to make in this endeavour. It has already transformed botanical research with deepening understanding of molecular processes within the species and across the Plant Kingdom; and has begun to revolutionize plant breeding by providing an invaluable catalogue of gene sequences that can be used to design the most precise molecular markers attainable for marker-assisted selection of valued traits. This review describes how A. thaliana and two of its natural biotrophic parasites could be seminal as a model for exploring the biogeography and molecular ecology of plant–microbe interactions, and specifically, for testing hypotheses proposed from the geographic mosaic theory of co-evolution

Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat 3 wild emmer wheat RIL population

Mineral nutrient malnutrition, and particularly deficiency in zinc and iron, afflicts over 3 billion people worldwide. Wild emmer wheat, Triticum turgidum ssp. dicoccoides, genepool harbors a rich allelic repertoire for mineral nutrients in the grain. The genetic and physiological basis of grain protein, micronutrients (zinc, iron, copper and manganese) and macronutrients (calcium, magnesium, potassium, phosphorus and sulfur) concentration was studied in tetraploid wheat population of 152 recombinant inbred lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Wide genetic variation was found among the RILs for all grain minerals, with considerable transgressive effect. A total of 82 QTLs were mapped for 10 minerals with LOD score range of 3.2–16.7. Most QTLs were in favor of the wild allele (50 QTLs). Fourteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. Significant positive correlation was found between grain protein concentration (GPC), Zn, Fe and Cu, which was supported by significant overlap between the respective QTLs, suggesting common physiological and/or genetic factors controlling the concentrations of these mineral nutrients. Few genomic regions (chromosomes 2A, 5A, 6B and 7A) were found to harbor clusters of QTLs for GPC and other nutrients. These identified QTLs may facilitate the use of wild alleles for improving grain nutritional quality of elite wheat cultivars, especially in terms of protein, Zn and Fe

Gramene database in 2010: updates and extensions

Now in its 10th year, the Gramene database (http://www.gramene.org) has grown from its primary focus on rice, the first fully-sequenced grass genome, to become a resource for major model and crop plants including Arabidopsis, Brachypodium, maize, sorghum, poplar and grape in addition to several species of rice. Gramene began with the addition of an Ensembl genome browser and has expanded in the last decade to become a robust resource for plant genomics hosting a wide array of data sets including quantitative trait loci (QTL), metabolic pathways, genetic diversity, genes, proteins, germplasm, literature, ontologies and a fully-structured markers and sequences database integrated with genome browsers and maps from various published studies (genetic, physical, bin, etc.). In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data

Open access resources for genome-wide association mapping in rice.

Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. To address the demand, plant breeders seek new sources of genetic variation to enhance the productivity, sustainability and resilience of crop varieties. Here we launch a high-resolution, open-access research platform to facilitate genome-wide association mapping in rice, a staple food crop. The platform provides an immortal collection of diverse germplasm, a high-density single-nucleotide polymorphism data set tailored for gene discovery, well-documented analytical strategies, and a suite of bioinformatics resources to facilitate biological interpretation. Using grain length, we demonstrate the power and resolution of our new high-density rice array, the accompanying genotypic data set, and an expanded diversity panel for detecting major and minor effect QTLs and subpopulation-specific alleles, with immediate implications for rice improvement.Article number:10532

An application of kernel methods to variety identification based on SSR markers genetic fingerprinting

<p>Abstract</p> <p>Background</p> <p>In crop production systems, genetic markers are increasingly used to distinguish individuals within a larger population based on their genetic make-up. Supervised approaches cannot be applied directly to genotyping data due to the specific nature of those data which are neither continuous, nor nominal, nor ordinal but only partially ordered. Therefore, a strategy is needed to encode the polymorphism between samples such that known supervised approaches can be applied. Moreover, finding a minimal set of molecular markers that have optimal ability to discriminate, for example, between given groups of varieties, is important as the genotyping process can be costly in terms of laboratory consumables, labor, and time. This feature selection problem also needs special care due to the specific nature of the data used.</p> <p>Results</p> <p>An approach encoding SSR polymorphisms in a positive definite kernel is presented, which then allows the usage of any kernel supervised method. The polymorphism between the samples is encoded through the Nei-Li genetic distance, which is shown to define a positive definite kernel between the genotyped samples. Additionally, a greedy feature selection algorithm for selecting SSR marker kits is presented to build economical and efficient prediction models for discrimination. The algorithm is a filter method and outperforms other filter methods adapted to this setting. When combined with kernel linear discriminant analysis or kernel principal component analysis followed by linear discriminant analysis, the approach leads to very satisfactory prediction models.</p> <p>Conclusions</p> <p>The main advantage of the approach is to benefit from a flexible way to encode polymorphisms in a kernel and when combined with a feature selection algorithm resulting in a few specific markers, it leads to accurate and economical identification models based on SSR genotyping.</p