Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes

From bits to bites: Advancement of the Germinate platform to support prebreeding informatics for crop wild relatives

Management and distribution of experimental data from prebreeding projects is important to ensure uptake of germplasm into breeding and research programs. Being able to access and share this data in standard formats is essential. The adoption of a common informatics platform for crops that may have limited resources brings economies of scale, allowing common informatics components to be used across multiple species. The close integration of such a platform with commonly used breeding software, visualization, and analysis tools reduces the barrier for entry to researchers and provides a common framework to facilitate collaborations and data sharing. This work presents significant updates to the Germinate platform and highlights its value in distributing prebreeding data for 14 crops as part of the project ‘Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives’ (hereafter Crop Trust Crop Wild Relatives project) led by the Crop Trust (https://www.cwrdiversity.org). The addition of data on these species compliments data already publicly available in Germinate. We present a suite of updated Germinate features using examples from these crop species and their wild relatives. The use of Germinate within the Crop TrustCropWildRelatives project demonstrates the usefulness of the system and the benefits a shared informatics platform provides. These data resources provide a foundation on which breeding and research communities can develop additional online resources for their crops, harness new data as it becomes available, and benefit collectively from future developments of the Germinate platform

Low-density reference fingerprinting SNP dataset of CIMMYT maize lines for quality control and genetic diversity analyses

CIMMYT maize lines (CMLs), which represent the tropical maize germplasm, are freely available worldwide. All currently released 615 CMLs and fourteen temperate maize inbred lines were genotyped with 180 kompetitive allele-specific PCR single nucleotide polymorphisms to develop a reference fingerprinting SNP dataset that can be used to perform quality control (QC) and genetic diversity analyses. The QC analysis identified 25 CMLs with purity, identity, or mislabeling issues. Further field observation, purification, and re-genotyping of these CMLs are required. The reference fingerprinting SNP dataset was developed for all of the currently released CMLs with 152 high-quality SNPs. The results of principal component analysis and average genetic distances between subgroups showed a clear genetic divergence between temperate and tropical maize, whereas the three tropical subgroups partially overlapped with one another. More than 99% of the pairs of CMLs had genetic distances greater than 0.30, showing their high genetic diversity, and most CMLs are distantly related. The heterotic patterns, estimated with the molecular markers, are consistent with those estimated using pedigree information in two major maize breeding programs at CIMMYT. These research findings are helpful for ensuring the regeneration and distribution of the true CMLs, via QC analysis, and for facilitating the effective utilization of the CMLs, globally

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes

What is compassion and how can we measure it? A review of definitions and measures

The importance of compassion is widely recognized and it is receiving increasing research attention. Yet, there is lack of consensus on definition and a paucity of psychometrically robust measures of this construct. Without an agreed definition and adequate measures, we cannot study compassion, measure compassion or evaluate whether interventions designed to enhance compassion are effective. In response, this paper proposes a definition of compassion and offers a systematic review of self- and observer-rated measures. Following consolidation of existing definitions, we propose that compassion consists of five elements: recognizing suffering, understanding the universality of human suffering, feeling for the person suffering, tolerating uncomfortable feelings, and motivation to act/acting to alleviate suffering. Three databases were searched (Web of Science, PsycInfo, and Medline) and nine measures included and rated for quality. Quality ratings ranged from 2 to 7 out of 14 with low ratings due to poor internal consistency for subscales, insufficient evidence for factor structure and/or failure to examine floor/ceiling effects, test–retest reliability, and discriminant validity. We call our five-element definition, and if supported, the development of a measure of compassion based on this operational definition, and which demonstrates adequate psychometric properties

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism

Crop Ontology Governance and Stewardship Framework

A governance & stewardship framework for the Crop Ontology Project is required as this is a collaborative tool developed by a Community of Practice. Over the last 12 years of its existence, it has increased significantly in scope and use. Collecting and storing plant trait data and annotating the data with ontology terms is widely accepted by the crop science community to be critical to enable data interoperability and interexchange through tools such as the Breeding API (BrAPI). The Crop Ontology Community of Practice is organised around roles, curation principles and validation processes that require a formal description. A governance framework is defined by the various actors involved in the asset’s design, development and maintenance. It is complemented by a quality assurance process to ensure that trust levels, value creation, and sustainability objectives meet appropriate quality levels. The general principles underlying data governance are integrity, transparency, accountability and ownership, stewardship, standardization, change management and a robust data audit