Tyrosine phosphorylation of DEPTOR functions as a molecular switch to activate mTOR signaling

Metabolic dysfunction is a major driver of tumorigenesis. The serine/threonine kinase mechanistic target of rapamycin (mTOR) constitutes a key central regulator of metabolic pathways promoting cancer cell proliferation and survival. mTOR activity is regulated by metabolic sensors as well as by numerous factors comprising the phosphatase and tensin homolog/PI3K/AKT canonical pathway, which are often mutated in cancer. However, some cancers displaying constitutively active mTOR do not carry alterations within this canonical pathway, suggesting alternative modes of mTOR regulation. Since DEPTOR, an endogenous inhibitor of mTOR, was previously found to modulate both mTOR complexes 1 and 2, we investigated the different post-translational modification that could affect its inhibitory function. We found that tyrosine (Tyr) 289 phosphorylation of DEPTOR impairs its interaction with mTOR, leading to increased mTOR activation. Using proximity biotinylation assays, we identified SYK (spleen tyrosine kinase) as a kinase involved in DEPTOR Tyr 289 phosphorylation in an ephrin (erythropoietin-producing hepatocellular carcinoma) receptor–dependent manner. Altogether, our work reveals that phosphorylation of Tyr 289 of DEPTOR represents a novel molecular switch involved in the regulation of both mTOR complex 1 and mTOR complex 2.     

Mast Cells Present Protrusions into Blood Vessels upon Tracheal Allergen Challenge in Mice

Mast cells (MC) and myeloid dendritic cells (DC) act proximally in detecting and processing antigens and immune insults. We sought to understand their comparative dynamic behavior with respect to the airway epithelium in the steady state and in response to an allergic stimulus in mouse trachea. We devised methods to label MC in living trachea and to demonstrate that MC and DC occupy distinct layers of the tracheal mucosa, with DC being closer to the lumen. DC numbers doubled after allergen challenge, but MC numbers remained stable. MC and DC migrated minimally in either steady state or allergen-challenge conditions, and their interactions with one another appeared to be stochastic and relatively infrequent. While DC, unlike MC, exhibited probing behaviors involving dendrites, these projections did not cross the epithelium into the airway lumen. MC typically were located too far from the epithelial surface to contact the tracheal lumen. However, MC had protrusions toward and into blood vessels, likely to load with IgE. Thus, DC and MC occupy distinct niches and engage in sessile surveillance in the mouse trachea. Little or no access of these cell types to the airway lumen suggests that trans-epithelial transport of proteins in the steady state would be required for them to access luminal antigens.     

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.     

What Was the Set of Ubiquitin and Ubiquitin-Like Conjugating Enzymes in the Eukaryote Common Ancestor?

Ubiquitin (Ub)-conjugating enzymes (E2) are key enzymes in ubiquitination or Ub-like modifications of proteins. We searched for all proteins belonging to the E2 enzyme super-family in seven species (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Schizosaccharomyces pombe, Saccharomyces cerevisiae, and Arabidopsis thaliana) to identify families and to reconstruct each family’s phylogeny. Our phylogenetic analysis of 207 genes led us to define 17 E2 families, with 37 E2 genes, in the human genome. The subdivision of E2 into four classes did not correspond to the phylogenetic tree. The sequence signature HPN (histidine–proline–asparagine), followed by a tryptophan residue at 16 (up to 29) amino acids, was highly conserved. When present, the active cysteine was found 7 to 8 amino acids from the C-terminal end of HPN. The secondary structures were characterized by a canonical alpha/beta fold. Only family 10 deviated from the common organization because the proteins were devoid of enzymatic activity. Family 7 had an insertion between beta strands 1 and 2; families 3, 5 and 14 had an insertion between the active cysteine and the conserved tryptophan. The three-dimensional data of these proteins highlight a strong structural conservation of the core domain. Our analysis shows that the primitive eukaryote ancestor possessed a diversified set of E2 enzymes, thus emphasizing the importance of the Ub pathway. This comprehensive overview of E2 enzymes emphasizes the diversity and evolution of this superfamily and helps clarify the nomenclature and true orthologies. A better understanding of the functions of these enzymes is necessary to decipher several human diseases. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.