Biology subjects

Dickinson, P.

Publications and source records attributed to Dickinson, P..

4 recordsLinked to original sources

bioRxiv2022

Abscisic Acid modulates neighbor proximity-induced leaf hyponasty in Arabidopsis

Leaves of shade avoiding plants like Arabidopsis thaliana change their growth pattern and position in response to a low red to far-red ratio (LRFR) that is encountered in dense plant communities. In LRFR, transcription factors of the phytochrome interacting family (PIFs) are de-repressed. PIFs induce auxin production, which is required to promote leaf hyponasty thereby favoring access to unfiltered sunlight. Abscisic acid (ABA) has also been implicated in the control of leaf hyponasty. In addition, gene expression patterns suggest that LRFR regulates the ABA response. Here we show that, in leaves, LRFR leads to a rapid increase in ABA levels. Changes in ABA levels depend on PIFs, which regulate the expression of genes encoding isoforms of the enzyme catalyzing a rate-limiting step in ABA biosynthesis. Interestingly, ABA biosynthesis and signaling mutants have more erect leaves in white light but respond less to LRFR. Consistent with this, ABA application decreases the leaf angle in white light but this response is inhibited in LRFR. Tissue-specific interference with ABA signaling indicates that an ABA response is required in different cell types for LRFR-induced hyponasty. Collectively, our data indicate LRFR triggers rapid PIF-mediated ABA production. ABA plays a different role in controlling hyponasty in white light compared to LRFR. Moreover, ABA exerts its activity in multiple cell types to control leaf position. One sentence summaryAbscisic acid biosynthesis and signaling are required to promote leaf hyponasty in response to a drop in the red to far-red ratio.

plant biology↗

bioRxiv2019

Analysis of patient derived xenograft studies in Oncology drug development: impact on design and interpretation of future studies

BackgroundPreclinical Oncology drug development is heavily reliant on xenograft studies to assess the anti-tumour effect of new compounds. Patient derived xenograft (PDX) have become popular as they may better represent the clinical disease, however variability is greater than in cell-line derived xenografts. The typical approach of analysing these studies involves performing an un-paired t-test on the mean tumour volumes between the treated and control group at the end of the study. This approach ignores the time-series and may result in false conclusions, especially when considering the increased variability of PDX studies.\n\nAimTo test the hypothesis that a model-based analysis provides increased power than analysis of final day volumes and to provide insights into more efficient PDX study designs.\n\nMethodsData was extracted from tumour xenograft time-series data from a large publicly available PDX drug treatment database released by Novartis. For all 2-arm studies the percent tumour growth inhibition (TGI) at two time-points, day 10 and day 14 was calculated. For each study, the effect of treatment was calculated using an un-paired t-test and also a model-based analysis using the likelihood ratio-test. In addition a simulation study was also performed to assess the difference in power between the two data-analysis approaches for different levels of TGI for PDX or standard cell-line derived xenografts (CDX).\n\nResultsThe model-based analysis had greater statistical power than the un-paired t-test approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25 percent whereas the un-paired t-test approach required at least 50 percent TGI. These findings were confirmed within the simulation study performed which also highlighted that CDX studies require less animals than PDX studies which show the equivalent level of TGI.\n\nConclusionThe analysis of 59 2-arm PDX studies highlighted that taking a model-based approach gave increased statistical power over simply performing an un-paired t-test on the final study day. Importantly the model-based approach was able to detect smaller size of effect compared to the un-paired t-test approach is which maybe common of such studies. These findings were confirmed within simulated studies which also highlighted the same sample size used for CDX studies would lead to inadequately powered PDX studies. Application of a model-based analysis should allow studies to use less animals and run experiments for a shorter period thus providing effective insight into compound anti-tumour activity

cancer biology↗

bioRxiv2019

An Interscholastic Network to Generate LexA Enhancer Trap Lines in Drosophila

Binary expression systems like the LexA-LexAop system provide a powerful experimental tool kit to study gene and tissue function in developmental biology, neurobiology and physiology. However, the number of well-defined LexA enhancer trap insertions remains limited. In this study, we present the molecular characterization and initial tissue expression analysis of nearly 100 novel StanEx LexA enhancer traps, derived from the StonEx1 index line. This includes 76 insertions into novel, distinct gene loci not previously associated with enhancer traps or targeted LexA constructs. Additionally, our studies revealed evidence for selective transposase-dependent replacement of a previously-undetected KP element on chromosome III within the StanEx1 genetic background during hybrid dysgenesis, suggesting a molecular basis for the over-representation of LexA insertions at the NK7.1 locus in our screen. Production and characterization of novel fly lines were performed by students and teachers in experiment-based genetics classes within a geographically diverse network of public and independent high schools. Thus, unique partnerships between secondary schools and university-based programs have produced and characterized novel genetic and molecular resources in Drosophila for open-source distribution, and provide paradigms for development of science education through experience-based pedagogy.

genetics↗

bioRxiv2017

The G-box transcriptional regulatory code in Arabidopsis

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes--these expansions are linked to adaptation to environmental stressors (1, 2). Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determine that the flanking sequences near G-boxes help determine in vitro specificity, but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we construct a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the gene expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a website that provides interactive visualisations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

plant biology↗