Genetically modifying Arabidopsis thaliana with a gene from Drought-tolerant Xerophyte Larrea tridentata (Creosote Bush)

L. tridentata, or desert creosote bush, is a xerophytic C3 plant native to the American Southwest, and is known to have evolutionarily developed sophisticated cellular mechanisms to deal with periods of intense abiotic stress. Particularly, complex signaling pathways in L. tridentata allow it to survive in periods of severe water deficiency. Through the findings of Zou et al. [5,6], LtWRKY21 synergistically works with abscisic acid (ABA) to transactivate both ABA-inducible HVA1 and HVA22 promoters. In addition, as ABA and gibberellic acid (GA) pathways are known to act antagonistically. Expectantly, the findings of Zou et al. suggest that LtWRKY21 activates ABA signaling pathways and represses GA signaling pathways [5,6]. More importantly, the LtWRKY21 transcription factor’s synergy with ABA is directly linked to some remarkable molecular adaptations of L. tridentata, some of which include stomatal closure to prevent transpiration, and slowing down gene expression to withstand dehydration [6]. To examine some of these mechanisms, the model plant Arabidopsis thaliana will be transformed with the LtWRKY21 coding region via Agrobacterium-mediated transformation. Successful transformants will be selected and the subsequent generation of transgenic plants will be assayed. Both phenotypic (screening) and genotypic (qRT-PCR and Southern Blot) examination will allow the function and expression patterns of LtWRKY21 to be elucidated under simulated drought. In order for LtWRKY21 to be successfully transformed into Arabidopsis, a tumor-inducing (Ti) plasmid must be engineered to carry LtWRKY21

Decoding the protein interaction network - an approach integrating biology and math

The WRKY super family is known to play a major role during the plant stress response and development. My project focuses on the protein-protein interaction of an Oryzasativa (rice) transcription factor, OsWRKY71 which functions as the repressor of gibberellins signaling pathway. Previous literature revealed that OsWRKY71 can interact with itself or OsWRKY51 to form dimmers by using bimolecular fluorescence complementation (BiFC). To confirm this result, we use yeast two-hybrid system. As our data showed, OsWRKY71 seems to suppress the reporter gene expression of the conventional yeast two-hybrid system, so we use a modified yeast two-hybrid, Mating-based Split Ubiquitin System (MbSUS). The result confirms OsWRKY71 can interact with another OsWRKY71, so this system can be used for future studies of protein-protein interaction of OsWRKY71. Images from Confocal microscopy show OsWRKY71 proteins are anchored on to the membrane through the membrane adaptor, and the Support Vector Machine software confirms the protein-protein interaction of OsWRKY71. The next step of this project is to construct the full length cDNA library of rice to screen suspicious proteins in a larger scale

Collaborative scheduling in dynamic environments using error inference

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments. © 2014 IEEE

Three WRKY transcription factors additively repress abscisic acid and gibberellin signaling in aleurone cells

Members of the WRKY transcription factor superfamily are essential for the regulation of many plant pathways. Functional redundancy due to duplications of WRKY transcription factors, however, complicates genetic analysis by allowing single-mutant plants to maintain wild-type phenotypes. Our analyses indicate that three group I WRKY genes, OsWRKY24, -53, and -70, act in a partially redundant manner. All three showed characteristics of typical WRKY transcription factors: each localized to nuclei and yeast one-hybrid assays indicated that they all bind to W-boxes, including those present in their own promoters. Quantitative real time-PCR (qRT-PCR) analyses indicated that the expression levels of the three WRKY genes varied in the different tissues tested. Particle bombardment-mediated transient expression analyses indicated that all three genes repress the GA and ABA signaling in a dosage-dependent manner. Combination of all three WRKY genes showed additive antagonism of ABA and GA signaling. These results suggest that these WRKY proteins function as negative transcriptional regulators of GA and ABA signaling. However, different combinations of these WRKY genes can lead to varied strengths in suppression of their targets