High-throughput phenotyping of multicellular organisms: finding the link between genotype and phenotype

High-throughput phenotyping approaches (phenomics) are being combined with genome-wide genetic screens to identify alterations in phenotype that result from gene inactivation. Here we highlight promising technologies for 'phenome-scale' analyses in multicellular organisms

Insights into root development from Arabidopsis root mutants

The root provides a useful system for the analysis of plant organ formation. Mutations that affect root development and physiology have been identified in Arabidopsis thaliana. Affected processes include embryonic root formation, cell expansion, cell differentiation and response to environmental stimuli. Analysis of these mutations is providing insight into fundamental questions of plant development.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75185/1/j.1365-3040.1994.tb00159.x.pd

A SIMPLE Pipeline for Mapping Point Mutations

A forward genetic screen is one of the best methods for revealing the function of genes. In plants, this technique is highly efficient, as it is relatively easy to grow and screen hundreds or thousands of individuals. The cost efficiency and ease of data production afforded by next-generation sequencing have created new opportunities for rapid mapping of induced mutations. Current mapping tools are often not user friendly, are complicated, or require extensive preparation steps. To simplify the process of mapping new mutations, we developed a pipeline that takes next-generation sequencing fastq files as input, calls on several well-established and freely available genome-analysis tools, and outputs the most likely causal DNA changes. The pipeline has been validated in Arabidopsis thaliana (Arabidopsis) and can be readily applied to other species, with the possibility of mapping either dominant or recessive mutations

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. <it>Arabidopsis thaliana</it>, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.</p> <p>Results</p> <p>Here, we provide a compendium of functional relationship networks for <it>Arabidopsis thaliana </it>leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. These include tissue, biological process, and development stage specific networks, each predicting relationships specific to an individual biological context. These biological networks enable the rapid investigation of uncharacterized genes in specific tissues and developmental stages of interest and summarize a very large collection of <it>A. thaliana </it>data for biological examination. We found validation in the literature for many of our predicted networks, including those involved in disease resistance, root hair patterning, and auxin homeostasis.</p> <p>Conclusions</p> <p>These context-specific networks demonstrate that highly specific biological hypotheses can be generated for a diversity of individual processes, developmental stages, and plant tissues in <it>A. thaliana</it>. All predicted functional networks are available online at <url>http://function.princeton.edu/arathGraphle</url>.</p

Reconstructing Spatiotemporal Gene Expression Data from Partial Observations

Developmental 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. Given 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.Comment: 19 pages, 4 figure

Detecting separate time scales in genetic expression data.

BACKGROUND: Biological 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. RESULTS: We 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. CONCLUSIONS: The 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.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

MicroRNA MIR396 regulates the switch between stem cells and transit-amplifying cells in arabidopsis roots

To ensure an adequate organ mass, the daughters of stem cells progress through a transit-amplifying phase displaying rapid cell division cycles before differentiating. Here, we show that Arabidopsis thaliana microRNA miR396 regulates the transition of root stem cells into transit-amplifying cells by interacting with GROWTH-REGULATING FACTORs (GRFs). The GRFs are expressed in transit-amplifying cells but are excluded from the stem cells through inhibition by miR396. Inactivation of the GRFs increases the meristem size and induces periclinal formative divisions in transit-amplifying cells. The GRFs repress PLETHORA (PLT) genes, regulating their spatial expression gradient. Conversely, PLT activates MIR396 in the stem cells to repress the GRFs. We identified a pathway regulated by GRF transcription factors that represses stem cell-promoting genes in actively proliferating cells, which is essential for the progression of the cell cycle and the orientation of the cell division plane. If unchecked, the expression of the GRFs in the stem cell niche suppresses formative cell divisions and distorts the organization of the quiescent center. We propose that the interactions identified here between miR396 and GRF and PLT transcription factors are necessary to establish the boundary between the stem cell niche and the transit-amplifying region.Fil: Rodriguez Virasoro, Ramiro Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Ercoli, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Debernardi, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Breakfield, Natalie W.. University of Duke; Estados UnidosFil: Mecchia, Martin Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Sabatini, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Cools, Toon. University of Ghent; BélgicaFil: De Veylder, Lieven. University of Ghent; BélgicaFil: Benfey, Philip N.. University of Duke; Estados UnidosFil: Palatnik, Javier Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentin

G-quadruplex structures trigger RNA phase separation

Liquid–liquid phase separation plays an important role in a variety of cellular processes, including the formation of membrane-less organelles, the cytoskeleton, signalling complexes, and many other biological supramolecular assemblies. Studies on the molecular basis of phase separation in cells have focused on protein-driven phase separation. In contrast, there is limited understanding on how RNA specifically contributes to phase separation. Here, we described a phase-separation-like phenomenon that SHORT ROOT (SHR) RNA undergoes in cells. We found that an RNA G-quadruplex (GQ) forms in SHR mRNA and is capable of triggering RNA phase separation under physiological conditions, suggesting that GQs might be responsible for the formation of the SHR phase-separation-like phenomenon in vivo. We also found the extent of GQ-triggered-phase-separation increases on exposure to conditions which promote GQ. Furthermore, GQs with more G-quartets and longer loops are more likely to form phase separation. Our studies provide the first evidence that RNA can adopt structural motifs to trigger and/or maintain the specificity of RNA-driven phase separation

Whole-Genome Analysis of the SHORT-ROOT Developmental Pathway in Arabidopsis

Stem cell function during organogenesis is a key issue in developmental biology. The transcription factor SHORT-ROOT (SHR) is a critical component in a developmental pathway regulating both the specification of the root stem cell niche and the differentiation potential of a subset of stem cells in the Arabidopsis root. To obtain a comprehensive view of the SHR pathway, we used a statistical method called meta-analysis to combine the results of several microarray experiments measuring the changes in global expression profiles after modulating SHR activity. Meta-analysis was first used to identify the direct targets of SHR by combining results from an inducible form of SHR driven by its endogenous promoter, ectopic expression, followed by cell sorting and comparisons of mutant to wild-type roots. Eight putative direct targets of SHR were identified, all with expression patterns encompassing subsets of the native SHR expression domain. Further evidence for direct regulation by SHR came from binding of SHR in vivo to the promoter regions of four of the eight putative targets. A new role for SHR in the vascular cylinder was predicted from the expression pattern of several direct targets and confirmed with independent markers. The meta-analysis approach was then used to perform a global survey of the SHR indirect targets. Our analysis suggests that the SHR pathway regulates root development not only through a large transcription regulatory network but also through hormonal pathways and signaling pathways using receptor-like kinases. Taken together, our results not only identify the first nodes in the SHR pathway and a new function for SHR in the development of the vascular tissue but also reveal the global architecture of this developmental pathway