William H. Turkett

A Kinetic Analysis of the Auxin Transcriptome Reveals Cell Wall Remodeling Proteins That Modulate Lateral Root Development in Arabidopsis

We identified genes involved in auxin-dependent lateral root formation using high temporal resolution and genome-wide transcript abundance analysis of auxin-treated Arabidopsis roots. Cell wall modification mutants, revealed by a reverse-genetic screen, had root phenotypes, supporting the hypothesis that auxin-mediated cell wall remodeling is an essential feature of lateral root development. To identify gene products that participate in auxin-dependent lateral root formation, a high temporal resolution, genome-wide transcript abundance analysis was performed with auxin-treated Arabidopsis thaliana roots. Data analysis identified 1246 transcripts that were consistently regulated by indole-3-acetic acid (IAA), partitioning into 60 clusters with distinct response kinetics. We identified rapidly induced clusters containing auxin-response functional annotations and clusters exhibiting delayed induction linked to cell division temporally correlated with lateral root induction. Several clusters were enriched with genes encoding proteins involved in cell wall modification, opening the possibility for understanding mechanistic details of cell structural changes that result in root formation following auxin treatment. Mutants with insertions in 72 genes annotated with a cell wall remodeling function were examined for alterations in IAA-regulated root growth and development. This reverse-genetic screen yielded eight mutants with root phenotypes. Detailed characterization of seedlings with mutations in CELLULASE3/GLYCOSYLHYDROLASE9B3 and LEUCINE RICH EXTENSIN2, genes not normally linked to auxin response, revealed defects in the early and late stages of lateral root development, respectively. The genes identified here using kinetic insight into expression changes lay the foundation for mechanistic understanding of auxin-mediated cell wall remodeling as an essential feature of lateral root development.

Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression dataset with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

Engaging Non-Traditional Students in Computer Science through Socially-Inspired Learning and Sustained Mentoring

This paper describes a new program for attracting non-traditional students into computer science and retaining them through sustained peer and faculty mentoring. The program is centered on socially-inspired learning, - learning in and for a community. It consists of a STEM Incubator course, hands-on projects with real-world applications, a sandbox lab, and a mentoring system that begins in the STEM Incubator course and continues with students who choose to remain involved in projects and courses. Our program is in its second year. Data collected on enrollment and retention and results of student questionnaires show promise for the success and sustainability of the program.

An Evaluation of Philosophical Agent Architectures for Mission Robustness

This paper reports on initial investigations of an agent architecture that embodies philosophical and social layers. A key feature of the architecture is that agent behavior is constrained by sets of agent societal laws similar to Asimov’s laws of robotics. In accordance with embedded philosophical principles, agents use decision theory in their negotiations to evaluate the expected utility of proposed actions and use of resources. This enables more robust decision-making and task execution. To evaluate the robustness, our investigations have included the effect of misinformation among cooperative agents in worth-oriented domains, and active countermeasures for dealing with the misinformation. We demonstrate that propagating misinformation is against the principles of ethical agents. Moreover, such agents are obligated to report on misbehavior, which minimizes its effects and furthers the progress of the agents and their society towards their goals. We also show how dedicating some agents to specialized tasks can improve the performance of a society.

K-mer based classifiers extract functionally relevant features to support accurate Peroxiredoxin subgroup distinction

Background The Peroxiredoxins (Prx) are a family of proteins that play a major role in antioxidant defense and peroxide-regulated signaling. Six distinct Prx subgroups have been defined based on analysis of structure and sequence regions in proximity to the Prx active site. Analysis of other sequence regions of these annotated proteins may improve the ability to distinguish subgroups and uncover additional representative sequence regions beyond the active site. Results The space of Prx subgroup classifiers is surveyed to highlight similarities and differences in the available approaches. Exploiting the recent growth in annotated Prx proteins, a whole sequence-based classifier is presented that employs support vector machines and a k-mer (k=3) sequence representation. Distinguishing k-mers are extracted and located relative to published active site regions. Conclusions This work demonstrates that the 3-mer based classifier can attain high accuracy in subgroup annotation, at rates similar to the current state-of-the-art. Analysis of the classifier’s automatically derived models show that the classification decision is based on a combination of conserved features, including a significant number of residue regions that have not been previously suggested as informative by other classifiers but for which there is evidence of functional relevance.