Plant responses to ethylene are mediated by regulation of EBF1/2-dependent degradation of the ETHYLENE INSENSITIVE3 (EIN3) transcription factor. Here, we report that the level of EIL1 protein is upregulated by ethylene through an EBF1/2-dependent pathway. Genetic analysis revealed that EIL1 and EIN3 cooperatively but differentially regulate a wide array of ethylene responses, with EIL1 mainly inhibiting leaf expansion and stem elongation in adult plants and EIN3 largely regulating a multitude of ethylene responses in seedlings. When EBF1 and EBF2 are disrupted, EIL1 and EIN3 constitutively accumulate in the nucleus and remain unresponsive to exogenous ethylene application. Further study revealed that the levels of EBF1 and EBF2 proteins are downregulated by ethylene and upregulated by silver ion and MG132, suggesting that ethylene stabilizes EIN3/EIL1 by promoting EBF1 and EBF2 proteasomal degradation. Also, we found that EIN2 is indispensable for mediating ethylene-induced EIN3/EIL1 accumulation and EBF1/2 degradation, whereas MKK9 is not required for ethylene signal transduction, contrary to a previous report. Together, our studies demonstrate that ethylene similarly regulates EIN3 and EIL1, the two master transcription factors coordinating myriad ethylene responses, and clarify that EIN2 but not MKK9 is required for ethylene-induced EIN3/EIL1 stabilization. Our results also reveal that EBF1 and EBF2 act as essential ethylene signal transducers that by themselves are subject to proteasomal degradation.

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n ¼ 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. Ó2017 AACR.

The activation domain of the yeast Gal4 protein binds specifically to the Gal80 repressor and is also thought to associate with one or more coactivators in the RNA polymerase II holoenzyme and chromatin remodeling machines. This is a specific example of a common situation in biochemistry where a single protein domain can interact with multiple partners. Are these different interactions related chemically? To probe this point, phage display was employed to isolate peptides from a library based solely on their ability to bind Gal80 protein in vitro. Peptide-Gal80 protein association is shown to be highly specific and of moderate affinity. The Gal80 protein-binding peptides compete with the native activation domain for the repressor, suggesting that they bind to the same site. It was then asked if these peptides could function as activation domains in yeast when tethered to a DNA binding domain. Indeed, this is the case.

There is currently great interest in the fabrication of protein-detecting arrays comprised of large numbers of immobilized protein capture agents. While most efforts in this arena have focused on the use of biomolecules such as antibodies and nucleic acid aptamers as capture agents, synthetic species have many potential advantages. However, synthetic molecules isolated from combinatorial libraries generally do not bind target proteins with the high affinity necessary for array applications. Here, we demonstrate that simple linear peptides bind dimeric proteins tenaciously when immobilized, although they exhibit only modest affinity in solution. These data show that high-affinity bidentate capture agents for dimeric proteins can be created by simply immobilizing modest-affinity ligands on a surface at high density, bypassing the requirement for careful optimization of linker length and geometry that is normally required to create a high-affinity solution bidentate ligand.

Motivation Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type. Results We developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets (even from different species) and applied our framework on simulated, pancreas and brain scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent cell subtype labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy simulated 1 datasets: 90%; simulated 2 datasets: 94%; pancreas datasets: 88% and brain datasets: 66%) using LAmbDA Feedforward 1 Layer Neural Network with bagging. This method achieved higher weighted accuracy in labeling cellular subtypes than two other state-of-the-art methods, scmap and CaSTLe in brain (66% versus 60% and 32%). Furthermore, it achieved better performance in correctly predicting ambiguous cellular subtype labels across datasets in 88% of test cases compared with CaSTLe (63%), scmap (50%) and MetaNeighbor (50%). LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. Availability and implementation github.com/tsteelejohnson91/LAmbDA Supplementary information Supplementary data are available at Bioinformatics online.