Breeding Research and Education Needs Assessment for Organic Vegetable Growers in the Northeast

This work was supported by U.S. Department of Agriculture (USDA) Agriculture and Food Research Initiative (AFRI) Competitive Grant 2014-67013-22409

Genome-wide SNP identification, linkage map construction and QTL mapping for seed mineral concentrations and contents in pea (Pisum sativum L.)

Linkage map. This file contains the details of the linkage map. (XLSX 38 kb

Cucurbit Genomics Database (CuGenDB): a central portal for comparative and functional genomics of cucurbit crops

[EN] The Cucurbitaceae family (cucurbit) includes several economically important crops, such as melon, cucumber, watermelon, pumpkin, squash and gourds. During the past several years, genomic and genetic data have been rapidly accumulated for cucurbits. To store, mine, analyze, integrate and disseminate these large-scale datasets and to provide a central portal for the cucurbit research and breeding community, we have developed the Cucurbit Genomics Database (CuGenDB; http://cucurbitgenomics.org) using the Tripal toolkit. The database currently contains all available genome and expressed sequence tag (EST) sequences, genetic maps, and transcriptome profiles for cucurbit species, as well as sequence annotations, biochemical pathways and comparative genomic analysis results such as synteny blocks and homologous gene pairs between different cucurbit species. A set of analysis and visualization tools and user-friendly query interfaces have been implemented in the database to facilitate the usage of these large-scale data by the community. In particular, two new tools have been developed in the database, a `SyntenyViewer¿ to view genome synteny between different cucurbit species and an `RNA-Seq¿ module to analyze and visualize gene expression profiles. Both tools have been packed as Tripal extension modules that can be adopted in other genomics databases developed using the Tripal system.USDA National Institute of Food and Agriculture Specialty Crop Research Initiative [2015-51181-24285]; US-Israel Binational Agricultural Research and Development Fund [IS-3333-02, IS-3877-06CR and IS-4223-09C]; USDA Agricultural Research Service, and by SNC Laboratoire ASL, de Ruiter Seeds B.V., Enza Zaden B.V., Gautier Semences S.A., Nunhems B.V., Rijk Zwaan B.V., Sakata Seed Inc, Semillas Fito S.A., Seminis Vegetable Seeds Inc, Syngenta Seeds B.V., Takii and Company Ltd, Vilmorin and Cie S.A. and Zeraim Gedera Ltd, all of them as part of the support to the International Cucurbit Genomics Initiative (ICuGI). Funding for open access charge: USDA National Institute of Food and Agriculture.Zheng, Y.; Wu, S.; Bai, Y.; Sun, H.; Jiao, C.; Guo, S.; Zhao, K.... (2018). Cucurbit Genomics Database (CuGenDB): a central portal for comparative and functional genomics of cucurbit crops. Nucleic Acids Research. 47(D1):D1128-D1136. https://doi.org/10.1093/nar/gky944SD1128D113647D

A Sustainable Agricultural Future Relies on the Transition to Organic Agroecological Pest Management

The need to improve agricultural sustainability to secure yields, minimize environmental impacts and buffer environmental change is widely recognized. Investment in conventional agriculture has supported its present yield advantage. However, organic agriculture with agroecological management has nascent capacity for sustainable production and for increasing yields in the future. Conventional systems have leveraged reductionist approaches to address pests, primarily through pesticides that seek to eliminate biological factors that reduce yield, but come at a cost to human and ecosystem health, and leave production systems vulnerable to the development of pest resistance to these chemicals or traits. Alternatives are needed, and are found in organic production approaches. Although both organic and agroecology approaches encompass more than pest management, this aspect is a pivotal element of our agricultural future. Through increased investment and application of emerging analytical approaches to improve plant breeding for and management of these systems, yields and resilience will surpass approaches that address components alone

Evaluation of Selection Methods for Resistance to a Specialist Insect Pest of Squash (Cucurbita pepo)

Plant varieties resistant to insect pests are a critical component of integrated pest management, but challenges associated with plant breeding for insect resistance, such as a long breeding cycle duration and low trait heritability, slow progress in the field. In this study, we tested two novel selection schemes to improve genetic gain for resistance to the major pest, the striped cucumber beetle (Acalymma vittatum), in squash (Cucurbita pepo, e.g., zucchini). First, we tested an indirect selection scheme using a proxy insect with correlated resistance phenotypes, Trichoplusia ni, in place of the seasonally available A. vittatum. We found that while resistance to herbivory by T. ni was heritable, there was no reciprocal benefit for resistance to A. vittatum. Second, we tested genomic selection, a method that allows for selection without phenotyping every generation, for both resistance to A. vittatum directly and resistance to the proxy T. ni. Although there was moderate genomic predictive ability, we did not observe realized gains from selection in field trials. Overall, strategies that minimize investment in direct phenotyping, leverage efficiencies from phenotyping correlated traits, and shorten breeding cycle duration are needed to develop insect resistant varieties, and this study provides examples and empirical data of two such approaches deployed in an applied breeding program

Genomic Prediction and Selection for Fruit Traits in Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop

Analysis of strains of Saccharomyces cerevisiae with amino acid substitutions in the Cu(A)-binding region of subunit II of cytochrome c oxidase

Cytochrome c oxidase accepts electrons from cytochrome c and transfers them to oxygen to form water. Electrons enter the complex through the Cu(A) site, formed by two copper atoms bound to mitochondrially encoded subunit II. The effect of amino-acid alterations in one of the Cu(A) ligands and in an amino acid adjacent to another of the ligands in the yeast enzyme is examined. Substitution of tyrosine for the Cu(A) ligand, cysteine 221, completely abolishes enzyme activity. In addition, 19 independent revertants of this mutant yeast strain recover function by restoring the cysteine codon. Replacement of a non-conserved glycine at position 228 by valine, adjacent to the Cu(A)-ligand histidine 229, virtually blocks enzyme activity. Activity is restored by inserting alanine or phenylalanine at position 228 or by amino-acid substitutions at nearby codons. Our results demonstrate that the Cu(A) ligand appears to be essential for enzyme function while other residues in the copper-binding region are less functionally constrained