A Vavilovian approach to discovering crop-associated microbes with potential to enhance plant immunity

Through active associations with a diverse community of largely non-pathogenic microbes, a plant may be thought of as possessing an “extended genotype,” an interactive cross-organismal genome with potential, exploitable implications for plant immunity. The successful enrichment of plant microbiomes with beneficial species has led to numerous commercial applications, and the hunt for new biocontrol organisms continues. Increasingly flexible and affordable sequencing technologies, supported by increasingly comprehensive taxonomic databases, make the characterization of non-model crop-associated microbiomes a widely accessible research method toward this end; and such studies are becoming more frequent. A summary of this emerging literature reveals, however, the need for a more systematic research lens in the face of what is already a metagenomics data deluge. Considering the processes and consequences of crop evolution and domestication, we assert that the judicious integration of in situ crop wild relatives into phytobiome research efforts presents a singularly powerful tool for separating signal from noise, thereby facilitating a more efficient means of identifying candidate plant-associated microbes with the potential for enhancing the immunity and fitness of crop species

GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

Background: With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference. Results: The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored “Mock Reference” from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available. Conclusions: By achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.gi

A Scale-Explicit Framework for Conceptualizing the Environmental Impacts of Agricultural Land Use Changes

Demand for locally-produced food is growing in areas outside traditionally dominant agricultural regions due to concerns over food safety, quality, and sovereignty; rural livelihoods; and environmental integrity. Strategies for meeting this demand rely upon agricultural land use change, in various forms of either intensification or extensification (converting non-agricultural land, including native landforms, to agricultural use). The nature and extent of the impacts of these changes on non-food-provisioning ecosystem services are determined by a complex suite of scale-dependent interactions among farming practices, site-specific characteristics, and the ecosystem services under consideration. Ecosystem modeling strategies which honor such complexity are often impenetrable by non-experts, resulting in a prevalent conceptual gap between ecosystem sciences and the field of sustainable agriculture. Referencing heavily forested New England as an example, we present a conceptual framework designed to synthesize and convey understanding of the scale- and landscape-dependent nature of the relationship between agriculture and various ecosystem services. By accounting for the total impact of multiple disturbances across a landscape while considering the effects of scale, the framework is intended to stimulate and support the collaborative efforts of land managers, scientists, citizen stakeholders, and policy makers as they address the challenges of expanding local agriculture

A Scale-Explicit Framework for Conceptualizing the Environmental Impacts of Agricultural Land Use Changes

Demand for locally-produced food is growing in areas outside traditionally dominant agricultural regions due to concerns over food safety, quality, and sovereignty; rural livelihoods; and environmental integrity. Strategies for meeting this demand rely upon agricultural land use change, in various forms of either intensification or extensification (converting non-agricultural land, including native landforms, to agricultural use). The nature and extent of the impacts of these changes on non-food-provisioning ecosystem services are determined by a complex suite of scale-dependent interactions among farming practices, site-specific characteristics, and the ecosystem services under consideration. Ecosystem modeling strategies which honor such complexity are often impenetrable by non-experts, resulting in a prevalent conceptual gap between ecosystem sciences and the field of sustainable agriculture. Referencing heavily forested New England as an example, we present a conceptual framework designed to synthesize and convey understanding of the scale- and landscape-dependent nature of the relationship between agriculture and various ecosystem services. By accounting for the total impact of multiple disturbances across a landscape while considering the effects of scale, the framework is intended to stimulate and support the collaborative efforts of land managers, scientists, citizen stakeholders, and policy makers as they address the challenges of expanding local agriculture

GBS-Based Deconvolution of the Surviving North American Collection of Cold-Hardy Kiwifruit (Actinidia spp.) Germplasm.

Plant germplasm collections can be invaluable resources to plant breeders, provided they are well-characterized. After 140 years of acquisition and curation efforts by a wide and largely non-coordinated array of private and institutional actors, the current US collection of cold-hardy kiwifruit (Actinidia spp.) is rife with misclassifications, misnomers, and mix-ups. To facilitate the systematic improvement and resource-efficient curation of these species of long-recognized horticultural potential, we used genotyping-by-sequencing (GBS) data to deconvolute this historic collection. Evaluation of a total of 138 accessions (103 A. arguta, 28 A. kolomikta, and 7 A. polygama) with an interspecific set of 1,040 high-quality SNPs resulted in clear resolution of the three species. Intraspecific analysis (2,964 SNPs) within A. arguta revealed a significant level of redundancy (41.7%; only 60 unique genotypes out of 103 analyzed) and a sub-population structure reflecting likely geographic provenance, phenotypic classes, and hybrid pedigree. For A. kolomikta (3,425 SNPs), the level of accession redundancy was even higher (53.6%; 13 unique genotypes out of 28 analyzed); but no sub-structure was detected. Numerous instances were discovered of distinct genotypes sharing a common name, different names assigned to the same genotype, mistaken species assignments, and incorrect gender records, all critical information for both breeders and curators. In terms of method, this study demonstrates the practical and cost-effective use of GBS data to characterize plant genetic resources, despite ploidy differences and the lack of reference genomes. With the recent prohibition on further imports of Actinidia plant material into the country and with the active eradication of historic vines looming, this analysis of the US cold-hardy kiwifruit germplasm collection provides a timely assessment of the genetic resource base of an emerging, high-value specialty crop

Additional file 3: of GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

The distribution of pre-filtered SNPs across three different depth classes: Low (<4), Acceptable (4–200), and Over-represented (>200). The bar plot in AdditionalFile3.pdf compares the distributions of average read depths for the pre-filtered SNPs called by the five evaluated pipelines. (PDF 43 kb

Additional file 4: of GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

List of the 48 A. arguta genotypes used for GBS-SNP-CROP development and analysis. AdditionalFile4.pdf presents the names of the genotypes from the USDA National Clonal Germplasm Repository used in this study, along with their barcodes and the number of parsed GBS reads obtained for each. (PDF 50 kb