Rap1-interacting adapter molecule (RIAM) associates with the plasma membrane via a proximity detector

Adaptive immunity depends on lymphocyte adhesion that is mediated by the integrin lymphocyte functional antigen 1 (LFA-1). The small guanosine triphosphatase Rap1 regulates LFA-1 adhesiveness through one of its effectors, Rap1-interacting adapter molecule (RIAM). We show that RIAM was recruited to the lymphocyte plasma membrane (PM) through its Ras association (RA) and pleckstrin homology (PH) domains, both of which were required for lymphocyte adhesion. The N terminus of RIAM inhibited membrane translocation. In vitro, the RA domain bound both Rap1 and H-Ras with equal but relatively low affinity, whereas in vivo only Rap1 was required for PM association. The PH domain bound phosphoinositol 4,5-bisphosphate (PI(4,5)P2) and was responsible for the spatial distribution of RIAM only at the PM of activated T cells. We determined the crystal structure of the RA and PH domains and found that, despite an intervening linker of 50 aa, the two domains were integrated into a single structural unit, which was critical for proper localization to the PM. Thus, the RA-PH domains of RIAM function as a proximity detector for activated Rap1 and PI(4,5)P2

Uniform Bahadur Representation for Nonparametric Censored Quantile Regression: A Redistribution-of-Mass Approach

Censored quantile regressions have received a great deal of attention in the literature. In a linear setup, recent research has found that an estimator based on the idea of “redistribution-of-mass” in Efron (1967, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 4, pp. 831–853, University of California Press) has better numerical performance than other available methods. In this paper, this idea is combined with the local polynomial kernel smoothing for nonparametric quantile regression of censored data. We derive the uniform Bahadur representation for the estimator and, more importantly, give theoretical justification for its improved efficiency over existing estimation methods. We include an example to illustrate the usefulness of such a uniform representation in the context of sufficient dimension reduction in regression analysis. Finally, simulations are used to investigate the finite sample performance of the new estimator

Apical and basolateral localisation of GLUT2 transporters in human lung epithelial cells

Glucose concentrations of normal human airway surface liquid are ~12.5 times lower than blood glucose concentrations indicating that glucose uptake by epithelial cells may play a role in maintaining lung glucose homeostasis. We have therefore investigated potential glucose uptake mechanisms in non-polarised and polarised H441 human airway epithelial cells and bronchial biopsies. We detected mRNA and protein for glucose transporter type 2 (GLUT2) and glucose transporter type 4 (GLUT4) in non-polarised cells but GLUT4 was not detected in the plasma membrane. In polarised cells, GLUT2 protein was detected in both apical and basolateral membranes. Furthermore, GLUT2 protein was localised to epithelial cells of human bronchial mucosa biopsies. In non-polarised H441 cells, uptake of d-glucose and deoxyglucose was similar. Uptake of both was inhibited by phloretin indicating that glucose uptake was via GLUT-mediated transport. Phloretin-sensitive transport remained the predominant route for glucose uptake across apical and basolateral membranes of polarised cells and was maximal at 5–10 mM glucose. We could not conclusively demonstrate sodium/glucose transporter-mediated transport in non-polarised or polarised cells. Our study provides the first evidence that glucose transport in human airway epithelial cells in vitro and in vivo utilises GLUT2 transporters. We speculate that these transporters could contribute to glucose uptake/homeostasis in the human airway

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment

The Use of Decision–Analytic Models in Atopic Eczema: A Systematic Review and Critical Appraisal

Objective: The objective of this systematic review was to identify and assess the quality of published economic decision–analytic models within atopic eczema against best practice guidelines, with the intention of informing future decision–analytic models within this condition. Methods: A systematic search of the following online databases was performed: MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, Cochrane Database of Systematic Reviews, NHS Economic Evaluation Database, EconLit, Scopus, Health Technology Assessment, Cost-Effectiveness Analysis Registry and Web of Science. Papers were eligible for inclusion if they described a decision–analytic model evaluating both the costs and benefits associated with an intervention or prevention for atopic eczema. Data were extracted using a standardised form by two independent reviewers, whilst quality was assessed using the model-specific Philips criteria. Results: Twenty-four models were identified, evaluating either preventions (n = 12) or interventions (n = 12): 14 reported using a Markov modelling approach, four utilised decision trees and one a discrete event simulation, whilst five did not specify the approach. The majority, 22 studies, reported that the intervention was dominant or cost effective, given the assumptions and analytical perspective taken. Notably, the models tended to be short-term (16 used a time horizon of ≤1 year), often providing little justification for the limited time horizon chosen. The methodological and reporting quality of the studies was generally weak, with only seven studies fulfilling more than 50% of their applicable Philips criteria. Conclusions: This is the first systematic review of decision models in eczema. Whilst the majority of models reported favourable outcomes in terms of the cost effectiveness of the new intervention, the usefulness of these findings for decision-making is questionable. In particular, there is considerable scope for increasing the range of interventions evaluated, for improving modelling structures and reporting quality

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field