Expression of gibberellin 20-oxidase1 (AtGA20ox1) in Arabidopsis seedlings with altered auxin status is regulated at multiple levels

Bioactive gibberellins (GAs) affect many biological processes including germination, stem growth, transition to flowering, and fruit development. The location, timing, and level of bioactive GA are finely tuned to ensure that optimal growth and development occur. The balance between GA biosynthesis and deactivation is controlled by external factors such as light and by internal factors that include auxin. The role of auxin transport inhibitors (ATIs) and auxins on GA homeostasis in intact light-grown Arabidopsis thaliana (L.) Heynh. seedlings was investigated. Two ATIs, 1-N-naphthylthalamic acid (NPA) and 1-naphthoxyacetic acid (NOA) caused elevated expression of the GA biosynthetic enzyme AtGA20-oxidase1 (AtGA20ox1) in shoot but not in root tissues, and only at certain developmental stages. It was investigated whether enhanced AtGA20ox1 gene expression was a consequence of altered flow through the GA biosynthetic pathway, or was due to impaired GA signalling that can lead to enhanced AtGA20ox1 expression and accumulation of a DELLA protein, Repressor of ga1-3 (RGA). Both ATIs promoted accumulation of GFP-fused RGA in shoots and roots, and this increase was counteracted by the application of GA4. These results suggest that in ATI-treated seedlings the impediment to DELLA protein degradation may be a deficiency of bioactive GA at sites of GA response. It is proposed that the four different levels of AtGA20ox1 regulation observed here are imposed in a strict hierarchy: spatial (organ-, tissue-, cell-specific) > developmental > metabolic > auxin regulation. Thus results show that, in intact auxin- and auxin transport inhibitor-treated light-grown Arabidopsis seedlings, three other levels of regulation supersede the effects of auxin on AtGA20ox1

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (<it>Arabidopsis thaliana</it>)<it>.</it> However, these networks are usually constructed and analyzed in an <it>ad hoc</it> manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.</p> <p>Results</p> <p>Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, <it>p</it> < 1E-300, photosynthetic membrane, <it>p</it> < 1.3E-137, proteasome complex, <it>p</it> < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.</p> <p>Conclusions</p> <p>The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.</p