Tobacco bZIP transcription factor TGA2.2 and related factor TGA2.1 have distinct roles in plant defense responses and plant development

Salicylic acid (SA) is a crucial internal signaling molecule needed for the induction of plant defense responses upon attack of a variety of pathogens. Basic leucine zipper transcription factors of the TGA family bind to activating sequence-1 (as-1)-like elements which are SA-responsive cis elements found in promoters of 'immediate early' and 'late' SA-inducible genes. TGA2.2 constitutes the main component of tobacco as-1-binding factor-1 (ASF-1). TGA2.1, which differs from TGA2.2 by being able to activate transcription in yeast, constitutes a minor fraction of the complex. Both proteins interact with NPR1, a protein essential for SA inducibility of 'late' genes. Here we demonstrate using dsRNAi mediated gene silencing that reducing the amount of TGA2.2 and TGA2.1 correlates with a significant decrease in ASF-1 activity and with a decreased inducibility of both 'immediate early' and 'late' genes. In contrast, reducing the amount of TGA2.1 alone had no effect on the expression of these target genes suggesting that TGA2.1 is dispensable for SA-inducible gene expression from the as-1 element. Expression of a TGA2.2 mutant unable to form heterodimers with the endogenous pool of TGA factors led to reduced SA-inducibility of 'immediate early' gene Nt103, indicating that the native leucine zipper is important for the protein to act positively on transcription. Plants with reduced amounts of TGA2.1 developed petal like stamens indicating a regulatory role of TGA2.1 in defining organ identity in tobacco flowers. A model is suggested that unifies conflicting results on the function of tobacco TGA factors with respect to activation of the 'late'PR-1a promoter

Estimation of Patient Preference-Based Utility Weights from the Functional Assessment of Cancer Therapy—General

AbstractObjectivesThe goal of this study was to estimate an algorithm to convert responses to the Functional Assessment of Cancer Therapy—General (FACT-G) to time trade-off (TTO) utilities based on utilities for current health elicited from cancer patients.MethodsData for 1433 cancer patients were randomly separated into construction and validation samples. Four FACT-G questions were selected for inclusion based on correlation with Eastern Clinical Oncology Group—Performance Status (ECOG-PS) scores and TTO utilities. Item response theory was used to collapse response categories. Ordinary least squares regression with the constant constrained to one was used to estimate the algorithm.ResultsThe algorithm estimated mean utility for the full validation sample within three points of observed mean utility (0.805 vs. 0.832, P < 0.01). Mean utilities were wellpredicted (mean absolute difference < 0.03, P > 0.05) for most subgroups defined by ECOG-PS and Short Form-36 physical functioning scores, and responses to the FACT-G overall quality of life item. Nevertheless, the algorithm systematically overpredicted utilities for poorer health states.ConclusionA FACT-G-based algorithm of cancer patient utilities was developed that estimates group mean utility scores with accuracy comparable to other indirect preference-based measures of health-related quality of life. Patient-based preferences for health outcomes of cancer treatment may be useful in multiple situations, such as managing resources within cancer centers and in understanding health states preferences among cancer experienced patients before and after treatment