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Background:
Plant 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 combin‑
ing 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.
Results:
In 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 docu‑
ment 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.
Conclusions:
Acceptance of the rules described in this paper by the plant phenotyping community will help to
achieve findable, accessible, interoperable and reusable data.
Measures for interoperability of phenotypic data: minimum information requirements and formatting
(2016)
Background: Plant 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. Results: In 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. Conclusions: Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.
Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1- phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.
Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers
(2009)
A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers
(2009)
A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
The gene family of subtilisin-like serine proteases (subtilases) in Arabidopsis thaliana comprises 56 members, divided into six distinct subfamilies. Whereas the members of five subfamilies are similar to pyrolysins, two genes share stronger similarity to animal kexins. Mutant screens confirmed 144 T-DNA insertion lines with knockouts for 55 out of the 56 subtilases. Apart from SDD1, none of the confirmed homozygous mutants revealed any obvious visible phenotypic alteration during growth under standard conditions. Apart from this specific case, forward genetics gave us no hints about the function of the individual 54 non-characterized subtilase genes. Therefore, the main objective of our work was to overcome the shortcomings of the forward genetic approach and to infer alternative experimental approaches by using an integrative biolinformatics and biological approach. Computational analyses based on transcriptional co-expression and co-response pattern revealed at least two expression networks, suggesting that functional redundancy may exist among subtilases with limited similarity. Furthermore, two hubs were identified, which may be involved in signalling or may represent higher-order regulatory factors involved in responses to environmental cues. A particular enrichment of co- regulated genes with metabolic functions was observed for four subtilases possibly representing late responsive elements of environmental stress. The kexin homologs show stronger associations with genes of transcriptional regulation context. Based on the analyses presented here and in accordance with previously characterized subtilases, we propose three main functions of subtilases: involvement in (i) control of development, (ii) protein turnover, and (iii) action as downstream components of signalling cascades
Brassinosteroids (BRs) are steroidal plant hormones with important regulatory roles in various physiological processes, including growth, xylem differentiation, disease resistance, and stress tolerance. Several components of the BR signal transduction pathway have been identified. The extracellular domains of receptor kinases such as BRI1 perceive BRs and transduce the signal via intracellular kinase domains. Within the cell further kinases and phosphatases determine the phosphorylation status of transcription factors such as BES1 and BZR1. These factors mediate major BR effects. Studies of BR-regulated genes shed light on the molecular mode of BR action. Genes encoding cell-wall-modifying enzymes, enzymes of the BR biosynthetic pathway, transcription factors, and proteins involved in primary and secondary metabolism are subject to BR-regulation. Gene expression data also point at interactions with other phytohormones and a role of BR in stress responses. This article gives a survey of the BR-signaling pathway. Two BR-responsive genes, OPR3 and EXO, are described in detail
The nuclear SHL protein is composed of a N-terminal BAH domain and a C-terminal PHD finger. Both domains are found in transcriptional regulators and chromatin-modifying proteins. Arabidopsis plants over-expressing SHL showed earlier flowering and senescence phenotype. To identify SHL regulated genes, expression profiles of 35S::SHL plants were established with Affymetrix ATH1 microarrays. About 130 genes showed reduced transcript levels, and about 45 genes showed increased transcript levels in 35S:: SHL plants. The up-regulated genes included AGL20 and AGL9, which most likely cause the early flowering phenotype of 35S:: SHL plants. Late-flowering SHL-antisense lines showed reduced AGL20 mRNA levels, suggesting that AGL20 gene expression depends on the SHL protein. The stronger expression of senescence- and defence-related genes (such as DIN2, DIN11 and PR-1) is in line with the early senescence phenotype of SHL-over- expressing plants. SHL-down-regulated genes included stress response genes and the PSR3.2 gene (encoding a beta- glucosidase). SHL over-expression did not alter the tissue specificity of PSR3.2 gene expression, but resulted in reduced transcript levels in both shoots and roots. Plants with glucocorticoid-inducible SHL over-expression were established and used for expression profiling as well. A subset of genes was identified, which showed consistent changes in the inducible system and in plants with constitutive SHL over-expression