Clinically relevant mutations in the ABCG2 transporter uncovered by genetic analysis linked to erythrocyte membrane protein expression

The ABCG2 membrane protein is a key xeno- and endobiotic transporter, modulating the absorption and metabolism of pharmacological agents and causing multidrug resistance in cancer. ABCG2 is also involved in uric acid elimination and its impaired function is causative in gout. Analysis of ABCG2 expression in the erythrocyte membranes of healthy volunteers and gout patients showed an enrichment of lower expression levels in the patients. By genetic screening based on protein expression, we found a relatively frequent, novel ABCG2 mutation (ABCG2-M71V), which, according to cellular expression studies, causes reduced protein expression, although with preserved transporter capability. Molecular dynamics simulations indicated a stumbled dynamics of the mutant protein, while ABCG2-M71V expression in vitro could be corrected by therapeutically relevant small molecules. These results suggest that personalized medicine should consider this newly discovered ABCG2 mutation, and genetic analysis linked to protein expression provides a new tool to uncover clinically important mutations in membrane proteins. © 2018 The Author(s)

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

Load-driven interactions between energy efficiency and demand response on regional grid scales

Energy efficiency (EE) has long been recognized as a source of value to the electricity grid. Especially with increasing penetration of variable renewable generation, demand response (DR) can also provide system value and support the evolving needs of the grid. Yet there has been little study to date of interactions between EE and DR that may complicate their grid impacts. In this study we perform bottom-up modelling of the interactive effects between EE and DR in buildings for three representative regions of the United States electricity grid. Leveraging new simulation tools that enable detailed modelling of the building stock, we synthesize system-level demand profiles for several scenarios representing different portfolios of EE measures. In each scenario, we couple the underlying building models with a database of DR-enabling technologies to estimate building-level DR capabilities and compute a system-level supply curve for DR. We assess the resulting EE and DR interactive effects based on an existing conceptual framework. The results show a complex relationship between EE and DR, with interactive effects whose size and direction can vary widely depending on the grid system, type of DR, and the framework level being considered. Most often, the overall effect is competition between EE and DR, but significant complementarity can also occur, especially when the EE portfolio includes controls measures. Our results suggest that EE and DR programs developed without considering interactive effects may erode the benefits of both resources, whereas a more integrated approach may yield increased benefits

The predictive value of ABCB1, ABCG2, CYP3A4/5 and CYP2D6 polymorphisms for risperidone and aripiprazole plasma concentrations and the occurrence of adverse drug reactions

We investigated in ninety Caucasian pediatric patients the impact of the main polymorphisms occurring in CYP3A, CYP2D6, ABCB1 and ABCG2 genes on second-generation antipsychotics plasma concentrations, and their association with the occurrence of adverse drug reactions. Patients with the CA/AA ABCG2 genotype had a statistically significant lower risperidone plasma concentration/dose ratio (Ct/ds) (P-value: 0.007) and an higher estimated marginal probability of developing metabolism and nutrition disorders as compared to the ABCG2 c.421 non-CA/AA genotypes (P-value: 0.008). Multivariate analysis revealed that the ABCG2 c.421 CA/AA genotype was found associated to a higher hazard (P-value: 0.004) of developing adverse drug reactions classified as metabolism and nutrition disorders. The ABCB1 2677TT/3435TT genotype had a statistically significant lower aripiprazole Ct/ds if compared with patients with others ABCB1 genotypes (P-value: 0.026). Information obtained on ABCB1 and ABCG2 gene variants may result useful to tailor treatments with these drugs in Caucasian pediatric patients