To respond or not to respond? Natural variation of root architectural responses to nutrient signals

The amino acid glutamate (Glu) acts as a fast excitatory neurotransmitter in mammals. Its importance in plant signalling was recognized with the discovery of channel proteins similar to mammalian Glu receptors, as well as distinct changes in root-system architecture in response to very small amounts of soil Glu. Based on natural genetic variation within Arabidopsis, Walch-Liu et al. (2017) have now identified a major locus underpinning this root response, as well as several loci controlling it through gene by environment interactions with nitrate and temperature. It is a significant step towards unraveling crosstalk between signalling pathways that enable plants to adjust their growth and development to multiple environmental stimuli

Food for thought: how nutrients regulate root system architecture

The spatial arrangement of the plant root system (root system architecture, RSA) is very sensitive to edaphic and endogenous signals that report on the nutrient status of soil and plant. Signalling pathways underpinning RSA responses to individual nutrients, particularly nitrate and phosphate, have been unravelled. Researchers have now started to investigate interactive effects between two or more nutrients on RSA. Several proteins enabling crosstalk between signalling pathways have recently been identified. RSA is potentially an important trait for sustainable and/or marginal agriculture. It is generally assumed that RSA responses are adaptive and optimise nutrient uptake in a given environment, but hard evidence for this paradigm is still sparse. Here we summarize recent advances made in these areas of research

Stomatal clustering in Begonia associates with the kinetics of leaf gaseous exchange and influences water use efficiency

Stomata are microscopic pores formed by specialized cells in the leaf epidermis and permit gaseous exchange between the interior of the leaf and the atmosphere. Stomata in most plants are separated by at least one epidermal pavement cell and, individually, overlay a single substomatal cavity within the leaf. This spacing is thought to enhance stomatal function. Yet, there are several genera naturally exhibiting stomata in clusters and therefore deviating from the one-cell spacing rule with multiple stomata overlaying a single substomatal cavity. We made use of two Begonia species to investigate whether clustering of stomata alters guard cell dynamics and gas exchange under different light and dark treatments. Begonia plebeja, which forms stomatal clusters, exhibited enhanced kinetics of stomatal conductance and CO2 assimilation upon light stimuli that in turn were translated into greater water use efficiency. Our findings emphasize the importance of spacing in stomatal clusters for gaseous exchange and plant performance under environmentally limited conditions

Plant responses to abiotic stress: the chromatin context of transcriptional regulation

The ability of plants to cope with abiotic environmental stresses such as drought, salinity, heat, cold or flooding relies on flexible mechanisms for re-programming gene expression. Over recent years it has become apparent that transcriptional regulation needs to be understood within its structural context. Chromatin, the assembly of DNA with histone proteins, generates a local higher-order structure that impacts on the accessibility and effectiveness of the transcriptional machinery, as well as providing a hub for multiple protein interactions. Several studies have shown that chromatin features such as histone variants and post-translational histone modifications are altered by environmental stress, and they could therefore be primary stress targets that initiate transcriptional stress responses. Alternatively, they could act downstream of stress-induced transcription factors as an integral part of transcriptional activity. A few experimental studies have addressed this ‘chicken-and-egg’ problem in plants and other systems, but to date the causal relationship between dynamic chromatin changes and transcriptional responses under stress is still unclear. In this review we have collated the existing information on concurrent epigenetic and transcriptional responses of plants to abiotic stress, and we have assessed the evidence using a simple theoretical framework of causality scenarios

Graph-based iterative Group Analysis enhances microarray interpretation

BACKGROUND: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. RESULTS: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response. CONCLUSIONS: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process

Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments

BACKGROUND: The biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process. RESULTS: Our Iterative Group Analysis approach (iGA) uses elementary statistics to identify those functional classes of genes that are significantly changed in an experiment and at the same time determines which of the class members are most likely to be differentially expressed. iGA does not require that all members of a class change and is therefore robust against imperfect class assignments, which can be derived from public sources (e.g. GeneOntologies) or automated processes (e.g. key word extraction from gene names). In contrast to previous non-iterative approaches, iGA does not depend on the availability of fixed lists of differentially expressed genes, and thus can be used to increase the sensitivity of gene detection especially in very noisy or small data sets. In the extreme, iGA can even produce statistically meaningful results without any experimental replication. The automated functional annotation provided by iGA greatly reduces the complexity of microarray results and facilitates the interpretation process. In addition, iGA can be used as a fast and efficient tool for the platform-independent comparison of a microarray experiment to the vast number of published results, automatically highlighting shared genes of potential interest. CONCLUSIONS: By applying iGA to a wide variety of data from diverse organisms and platforms we show that this approach enhances and accelerates the interpretation of microarray experiments

Analysis of the root system architecture of Arabidopsis provides a quantitative readout of crosstalk between nutritional signals

As plant roots forage the soil for food and water, they translate a multifactorial input of environmental stimuli into a multifactorial developmental output that manifests itself as root system architecture (RSA). Our current understanding of the underlying regulatory network is limited because root responses have traditionally been studied separately for individual nutrient deficiencies. In this study, we quantified 13 RSA parameters of Arabidopsis thaliana in 32 binary combinations of N, P, K, S, and light. Analysis of variance showed that each RSA parameter was determined by a typical pattern of environmental signals and their interactions. P caused the most important single-nutrient effects, while N-effects were strongly light dependent. Effects of K and S occurred mostly through nutrient interactions in paired or multiple combinations. Several RSA parameters were selected for further analysis through mutant phenotyping, which revealed combinations of transporters, receptors, and kinases acting as signaling modules in K–N interactions. Furthermore, nutrient response profiles of individual RSA features across NPK combinations could be assigned to transcriptionally coregulated clusters of nutrient-responsive genes in the roots and to ionome patterns in the shoots. The obtained data set provides a quantitative basis for understanding how plants integrate multiple nutritional stimuli into complex developmental programs

Cryptic variation in RNA-directed DNA-methylation controls lateral root development when auxin signaling is perturbed

Maintaining the right balance between plasticity and robustness in biological systems is important to allow adaptation while maintaining essential functions. Developmental plasticity of plant root systems has been the subject of intensive research, but the mechanisms underpinning robustness remain unclear. Here, we show that potassium deficiency inhibits lateral root organogenesis by delaying early stages in the formation of lateral root primordia. However, the severity of the symptoms arising from this perturbation varies within a natural population of Arabidopsis and is associated with the genetic variation in CLSY1, a key component of the RNA-directed DNA-methylation machinery. Mechanistically, CLSY1 mediates the transcriptional repression of a negative regulator of root branching, IAA27, and promotes lateral root development when the auxin-dependent proteolysis pathway fails. Our study identifies DNA-methylation-mediated transcriptional repression as a backup system for post-translational protein degradation which ensures robust development and performance of plants in a challenging environment

The Histone Deacetylase Complex (HDC) 1 protein of Arabidopsis thaliana has the capacity to interact with multiple proteins including histone 3-binding proteins and histone 1 variants

Intrinsically disordered proteins can adopt multiple conformations thereby enabling interaction with a wide variety of partners. They often serve as hubs in protein interaction networks. We have previously shown that the Histone Deacetylase Complex (HDC) 1 protein from Arabidopsis thaliana interacts with histone deacetylases and quantitatively determines histone acetylation levels, transcriptional activity and several phenotypes, including ABA-sensitivity during germination, vegetative growth rate and flowering time. HDC1-type proteins are ubiquitous in plants but they contain no known structural or functional domains. Here we explored the protein interaction spectrum of HDC1. In addition to binding histone deacetylases, HDC1 directly interacted with core histone H3-binding proteins and co-repressor associated proteins, but not with H3 or the co-repressors themselves. Surprisingly, HDC1 was also able to interact with variants of the linker histone H1. Truncation of HDC1 to the ancestral core sequence narrowed the spectrum of interactions and of phenotypic outputs but maintained binding to a H3-binding protein and to H1. The results indicate a potential link between H1 and histone modifying complexes