Siobhan M. Brady

Cell Identity Mediates the Response of Arabidopsis Roots to Abiotic Stress

Little is known about the way developmental cues affect how cells interpret their environment. We characterized the transcriptional response to high salinity of different cell layers and developmental stages of the Arabidopsis root and found that transcriptional responses are highly constrained by developmental parameters. These transcriptional changes lead to the differential regulation of specific biological functions in subsets of cell layers, several of which correspond to observable physiological changes. We showed that known stress pathways primarily control semiubiquitous responses and used mutants that disrupt epidermal patterning to reveal cell-layer–specific and inter–cell-layer effects. By performing a similar analysis using iron deprivation, we identified common cell-type–specific stress responses and revealed the crucial role the environment plays in defining the transcriptional outcome of cell-fate decisions.

A stele-enriched gene regulatory network in the Arabidopsis root.

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one‐hybrid (Y1H) and two‐hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue‐specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65% of the TFs, but morphological phenotypes were associated with only 16%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered.

Protonophore‐ and pH‐insensitive glucose and sucrose accumulation detected by FRET nanosensors in Arabidopsis root tips

Although soil contains only traces of soluble carbohydrates, plant roots take up glucose and sucrose efficiently when supplied in artificial media. Soluble carbohydrates and other small metabolites found in soil are in part products from exudation from plant roots. The molecular nature of the transporters for uptake and exudation is unknown. Here, fluorescence resonance energy transfer (FRET) glucose and sucrose sensors were used to characterize accumulation and elimination of glucose and sucrose in Arabidopsis roots tips. Using an improved image acquisition set-up, FRET responses to perfusion with carbohydrates were detectable in roots within less than 10 sec and over a wide concentration range. Accumulation was fully reversible within 10-180 sec after glucose or sucrose had been withdrawn; elimination may be caused by metabolism and/or efflux. The rate of elimination was unaffected by pre-incubation with high concentrations of glucose, suggesting that elimination is not due to accumulation in a short-term buffer such as the vacuole. Glucose and sucrose accumulation was insensitive to protonophores, was comparable in media differing in potassium levels, and was similar at pH 5.8, 6.8 and 7.8, suggesting that both influx and efflux may be mediated by proton-independent transport systems. High-resolution expression mapping in root tips showed that only a few proton-dependent transport of the STP (Sugar Transport Protein) and SUT/SUC (Sucrose Transporter/Carrier) families are expressed in the external cell layers of root tips. The root expression maps may help to pinpoint candidate genes for uptake and release of carbohydrates from roots.

High-resolution metabolic mapping of cell types in plant roots

Significance Analyzing metabolite composition offers a powerful tool for understanding gene function and regulatory processes. Here, we present nontargeted metabolomics assays of five Arabidopsis GFP-tagged lines representing core cell types in the plant root, providing a metabolic map of an organ, composed of its different cell types. Fifty metabolites were putatively identified. The most prominent groups were glucosinolates, phenylpropanoids, and dipeptides. Metabolites were differentially abundant across root cell types and in many cases, this abundance did not correlate with transcript expression, suggesting non–cell-autonomous mechanisms responsible for their targeted localization. Metabolite composition offers a powerful tool for understanding gene function and regulatory processes. However, metabolomics studies on multicellular organisms have thus far been performed primarily on whole organisms, organs, or cell lines, losing information about individual cell types within a tissue. With the goal of profiling metabolite content in different cell populations within an organ, we used FACS to dissect GFP-marked cells from Arabidopsis roots for metabolomics analysis. Here, we present the metabolic profiles obtained from five GFP-tagged lines representing core cell types in the root. Fifty metabolites were putatively identified, with the most prominent groups being glucosinolates, phenylpropanoids, and dipeptides, the latter of which is not yet explored in roots. The mRNA expression of enzymes or regulators in the corresponding biosynthetic pathways was compared with the relative metabolite abundance. Positive correlations suggest that the rate-limiting steps in biosynthesis of glucosinolates in the root are oxidative modifications of side chains. The current study presents a work flow for metabolomics analyses of cell-type populations.

Hormone Cross-Talk in Seed Dormancy

The choice to become dormant versus continuing to grow is observed in a variety of organisms in response to specific developmental and environmental signals. In higher plants this is most obvious during both the establishment and breaking of seed dormancy. With the advent of molecular genetic analysis, particularly in Arabidopsis, genes involved in the establishment and breaking of seed dormancy have been identified. Genetic analysis suggests a web of hormone-derived information is required in the regulation of these processes. In this review we focus on examples of where hormones, and in particular cross-talk between hormones, is used to regulate both the establishment and release of seed dormancy. The use of multiple hormones that overlap in their control of specific developmental programs allows seeds to be flexible in making decisions in response to specific developmental and environmental cues.

Manipulating Large-Scale Arabidopsis Microarray Expression Data: Identifying Dominant Expression Patterns and Biological Process Enrichment

A series of large-scale Arabidopsis thaliana microarray expression experiments profiling genome-wide expression across different developmental stages, cell types, and environmental conditions have resulted in tremendous amounts of gene expression data. This gene expression is the output of complex transcriptional regulatory networks and provides a starting point for identifying the dominant transcriptional regulatory modules acting within the plant. Highly co-expressed groups of genes are likely to be regulated by similar transcription factors. Therefore, finding these co-expressed groups can reduce the dimensionality of complex expression data into a set of dominant transcriptional regulatory modules. Determining the biological significance of these patterns is an informatics challenge and has required the development of new methods. Using these new methods we can begin to understand the biological information contained within large-scale expression data sets.

Reconstructing spatiotemporal gene expression data from partial observations

MOTIVATIONnDevelopmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures.nnnRESULTSnGiven these partial observations, we present a novel method for reconstructing integrated high-resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations.nnnAVAILABILITYnSource code for solving bilinear equations is available at http://math.berkeley.edu/ approximately dustin/bilinear/. Visualizations of reconstructed patterns on a schematic Arabidopsis root are available at http://www.arexdb.org/.

Unraveling the Dynamic Transcriptome

The advent of large-scale transcriptional profiling techniques signalled a new age in biology. Instead of understanding the expression and action of single genes, the field of transcriptomics allows for the examination of whole transcriptome changes across a variety of biological conditions. These

Detecting separate time scales in genetic expression data.

BackgroundBiological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales.ResultsWe introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions.ConclusionsThe spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.