Cytogenetic and molecular genotyping in the allotetraploid Festuca pratensis × Lolium perenne hybrids

Background: Species of the Festuca and Lolium genera, as well as intergeneric Festuca × Lolium (Festulolium) hybrids, are valuable fodder and turf grasses for agricultural and amenity purposes worldwide. Festulolium hybrids can merge in their genomes agronomically important characteristics. However, in polyploid plants, especially in allopolyploids, the hybridization of divergent genomes could contribute to various abnormalities, such as variability in chromosome number, structural rearrangements, and/or disorders in inheritance patterns. Here we studied these issues in allotetraploid Festuca pratensis × Lolium perenne hybrids. Results: Cytogenetic procedures, including fluorescent in situ hybridization, genomic in situ hybridization, and molecular markers – inter-simple sequence repeats (ISSR) were exploited. This cytogenetic approach indicated the dynamics in the number and distribution of ribosomal RNA genes and structural rearrangements for both parental genomes (Festuca and Lolium) in hybrid karyotypes. The separate analysis of F. pratensis and L. perenne chromosomes in hybrid plants (F2-F3 generations of F. pratensis × L. perenne) revealed the asymmetrical level of rearrangements. Recognized structural changes were mainly located in the distal part of chromosome arms, and in chromosomes bearing ribosomal DNA, they were more frequently mapped in arms without this sequence. Based on the ISSR markers distribution, we found that the tetrasomic type of inheritance was characteristic for the majority of ISSR loci, but the disomic type was also observed. Nonetheless, no preference in the transmission of either Festuca or Lolium alleles to the following generations of allotetraploid F. pratensis × L. perenne hybrid was observed. Conclusion: Our study reports cytogenetic and molecular genotyping of the F. pratensis × L. perenne hybrid and its following F2-F3 progenies. The analysis of 137 allotetraploid F. pratensis × L. perenne hybrids revealed the higher level of recombination in chromosomes derived from F. pratensis genome. The results of ISSR markers indicated a mixed model of inheritance, which may be characteristic for these hybrids

Identifying, naming and interoperating data in a Phenotyping platform network : the good, the bad and the ugly

The EPPN2020 is a research project funded by Horizon 2020 Programme of the EU that will provide European public and private scientific sectors with access to a wide range of state-of-the-art plant phenotyping installations, techniques and methods. Specifically, EPPN2020 includes access to 31 plant phenotyping installations, and joint research activities to develop: novel technologies and methods for environmental and plant measurements.Here we present the results of the discussions of the 2019 annual project meeting to adopt community-approved architectural choices. It focuses on persistent identification of data and real objects, the naming of variables and the priorities for increasing interoperability among phenotyping installations. We describe the main elements to prioritize (the good) in order to enhance Findable, Accessible, Interoperable and Reusable (FAIR) quality for each data management system with a pragmatic concern for all partners. The plant phenotyping community gathers different actors with various means and practices. Among all the recommendations (including the bad: avoiding bad practices), the community requests identification methods (including the use of ontologies) compatible with the ‘local’ pre-existing ones. The identification scheme being adopted is based on Uniform Resource Identifiers (URIs) with independant left and right parts for each identifier. It focuses on the associated objects and variables common to all EPPN2020 members, namely the experimental units (which can be a plant in a pot or a plot), sensors and variables. A common architecture for identifiers and variable names is presented in order to enable a first level of interoperation between information systems.In conclusion, we present some of the next challenges (the ugly) that need to be addressed by the EPPN2020 community related with i) the partial reuse of pre-existing ontologies, ii) the persistence of long-term access to data iii) interoperation between all potential users of the phenotyping data

Semantic concept schema of the linear mixed model of experimental observations

In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (STATO), to produce FAIR data summaries. We hope to improve the general understanding of statistical modelling and thus contribute to a better description of the statistical conclusions from data analysis, allowing their efficient exploration and automated processing.</p

High-throughput sequencing data revealed genotype-specific changes evoked by heat stress in crown tissue of barley sdw1 near-isogenic lines

Background: High temperature shock is becoming increasingly common in our climate, affecting plant growth and productivity. The ability of a plant to survive stress is a complex phenomenon. One of the essential tissues for plant performance under various environmental stimuli is the crown. However, the molecular characterization of this region remains poorly investigated. Gibberellins play a fundamental role in whole-plant stature formation. This study identified plant stature modifications and crown-specific transcriptome re-modeling in gibberellin-deficient barley sdw1.a (BW827) and sdw1.d (BW828) mutants exposed to increased temperature. Results: The deletion around the sdw1 gene in BW827 was found to encompass at least 13 genes with primarily regulatory functions. A bigger genetic polymorphism of BW828 than of BW827 in relation to wild type was revealed. Transcriptome-wide sequencing (RNA-seq) revealed several differentially expressed genes involved in gibberellin metabolism and heat response located outside of introgression regions. It was found that HvGA20ox4, a paralogue of the HvGA20ox2 gene, was upregulated in BW828 relative to other genotypes, which manifested as basal internode elongation. The transcriptome response to elevated temperature differed in the crown of sdw1.a and sdw1.d mutants; it was most contrasting for HvHsf genes upregulated under elevated temperature in BW828, whereas those specific to BW827 were downregulated. In-depth examination of sdw1 mutants revealed also some differences in their phenotypes and physiology. Conclusions: We concluded that despite the studied sdw1 mutants being genetically related, their heat response seemed to be genotype-specific and observed differences resulted from genetic background diversity rather than single gene mutation, multiple gene deletion, or allele-specific expression of the HvGA20ox2 gene. Differences in the expressional reaction of genes to heat in different sdw1 mutants, found to be independent of the polymorphism, could be further explained by in-depth studies of the regulatory factors acting in the studied system. Our findings are particularly important in genetic research area since molecular response of crown tissue has been marginally investigated, and can be useful for wide genetic research of crops since barley has become a model plant for them

From Dirty Data to Tidy Facts: Clustering Practices in Plant Phenomics and Business Cycle Analysis

This is the final version. Available on open access from Springer via the DOI in this recordThis chapter considers and compares the ways in which two types of data, economic observations and phenotypic data in plant science, are prepared for use as evidence for claims about phenomena such as business cycles and gene-environment interactions. We focus on what we call “cleaning by clustering” procedures, and investigate the principles underpinning this kind of cleaning. These cases illustrate the epistemic significance of preparing data for use as evidence in both the social and natural sciences. At the same time, the comparison points to differences and similarities between data cleaning practices, which are grounded in the characteristics of the objects of interests as well as the conceptual commitments, community standards and research tools used by economics and plant science towards producing and validating claims.European CommissionAlan Turing InstituteAustralian Research Counci

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs

Measures for interoperability of phenotypic data: minimum information requirements and formatting

BackgroundPlant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse.ResultsIn this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called “Minimum Information About a Plant Phenotyping Experiment”, which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented.ConclusionsAcceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data

Plant phenomics, from sensors to knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants’ high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts

BrAPI-an application programming interface for plant breeding applications

Motivation: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases