CYP1A1, GSTM1 and GSTT1 polymorphisms and lung cancer: a pooled analysis of gene–gene interactions

Gene–environment interactions have been extensively studied in lung cancer. It is likely that several genetic polymorphisms cooperate in increasing the individual risk. Therefore, the study of gene–gene interactions might be important to identify high-susceptibility subgroups. GSEC is an initiative aimed at collecting available data sets on metabolic polymorphisms and the risks of cancer at several sites and performing pooled analyses of the original data. Authors of published papers have provided original data sets. The present paper refers to gene–gene interactions in lung cancer and considers three polymorphisms in three metabolic genes: CYP1A1, GSTM1 and GSTT1. The present analyses compare the gene–gene interactions of the CYP1A1*2A, GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted in Europe and the USA between 1991 and 2000. Only Caucasians have been included. The data set includes 1466 cases and 1488 controls. The only clear-cut association was found with CYP1A1*2A. This association remained unchanged after stratification by polymorphisms in other genes (with an odds ratio [OR] of approximately 2.5), except when interaction with GSTM1 was considered. When the OR for CYP1A1*2A was stratified according to the GSTM1 genotype, the OR was increased only among the subjects who had the null (homozygous deletion) GSTM1 genotype (OR=2.8, 95% CI=0.9–8.4). The odds ratio for the interactive term (CYP1A1*2A by GSTM1) in logistic regression was 2.7 (95% CI=0.5–15.3). An association between lung cancer and the homozygous CYP1A1*2A genotype is confirmed. An apparent and biologically plausible interaction is suggested between this genotype and GSTM1.     

Mixing of nanofluids: molecular dynamics simulations and modelling

In this paper we study a mixing scheme, which has recently been proposed for microfluids, on the nanoscale. We do this by performing a series of nonequilibrium molecular dynamics simulations. On the nanoscale the chaotic mixing regime is captured. We discover a new phenomenon where the two mixing fluids exchange positions after leaving the mixing intersection. The results from the molecular dynamics simulations also reveal complex spatio-temporal stream velocity profiles generated by the mixing device. We find that these profiles can be modelled through an approximate analytical solution to the Navier–Stokes equation.     

Social jetlag,academic achievement and cognitive performance: Understanding gender/sex differences

Adolescents in high school suffer from circadian misalignment, undersleeping on weekdays and oversleeping on weekends. Since high schools usually impose early schedules, adolescents suffer from permanent social jetlag (SJL) and thus are a suitable population to study the effects of SJL on both academic and cognitive performance. In this study, 796 adolescents aged 12–16 years reported information about their sleep habits, morningness–eveningness (M–E), cognitive abilities and grade point average (GPA). Time in bed on both weekdays and weekends was not related to cognitive abilities, and only time in bed on weekdays was related to academic achievement. SJL was negatively related to academic achievement, cognitive abilities (except for vocabulary and verbal fluency abilities) and general cognitive ability (g), whereas M–E was slightly positively related to academic achievement and marginally negatively related to inductive reasoning. Results separated by sex/gender indicated that SJL may be more detrimental to girls’ performance, as it was negatively related to a greater number of cognitive abilities and GPA.     

Protein dynamics and motions in relation to their functions: several case studies and the underlying mechanisms

Proteins are dynamic entities in cellular solution with functions governed essentially by their dynamic personalities. We review several dynamics studies on serine protease proteinase K and HIV-1 gp120 envelope glycoprotein to demonstrate the importance of investigating the dynamic behaviors and molecular motions for a complete understanding of their structure–function relationships. Using computer simulations and essential dynamic (ED) analysis approaches, the dynamics data obtained revealed that: (i) proteinase K has highly flexible substrate-binding site, thus supporting the induced-fit or conformational selection mechanism of substrate binding; (ii) Ca²⁺ removal from proteinase K increases the global conformational flexibility, decreases the local flexibility of substrate-binding region, and does not influence the thermal motion of catalytic triad, thus explaining the experimentally determined decreased thermal stability, reduced substrate affinity, and almost unchanged catalytic activity upon Ca²⁺ removal; (iii) substrate binding affects the large concerted motions of proteinase K, and the resulting dynamic pocket can be connected to substrate binding, orientation, and product release; (iv) amino acid mutations 375 S/W and 423 I/P of HIV-1 gp120 have distinct effects on molecular motions of gp120, facilitating 375 S/W mutant to assume the CD4-bound conformation, while 423 I/P mutant to prefer for CD4-unliganded state. The mechanisms underlying protein dynamics and protein–ligand binding, including the concept of the free energy landscape (FEL) of the protein–solvent system, how the ruggedness and variability of FEL determine protein’s dynamics, and how the three ligand-binding models, the lock-and-key, induced-fit, and conformational selection are rationalized based on the FEL theory are discussed in depth.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Deciphering structural aspects of reverse cholesterol transport: mapping the knowns and unknowns

Atherosclerosis is a major contributor to the onset and progression of cardiovascular disease (CVD). Cholesterol-loaded foam cells play a pivotal role in forming atherosclerotic plaques. Induction of cholesterol efflux from these cells may be a promising approach in treating CVD. The reverse cholesterol transport (RCT) pathway delivers cholesteryl ester (CE) packaged in high-density lipoproteins (HDL) from non-hepatic cells to the liver, thereby minimising cholesterol load of peripheral cells. RCT takes place via a well-organised interplay amongst apolipoprotein A1 (ApoA1), lecithin cholesterol acyltransferase (LCAT), ATP binding cassette transporter A1 (ABCA1), scavenger receptor-B1 (SR-B1), and the amount of free cholesterol. Unfortunately, modulation of RCT for treating atherosclerosis has failed in clinical trials owing to our lack of understanding of the relationship between HDL function and RCT. The fate of non-hepatic CEs in HDL is dependent on their access to proteins involved in remodelling and can be regulated at the structural level. An inadequate understanding of this inhibits the design of rational strategies for therapeutic interventions. Herein we extensively review the structure–function relationships that are essential for RCT. We also focus on genetic mutations that disturb the structural stability of proteins involved in RCT, rendering them partially or completely non-functional. Further studies are necessary for understanding the structural aspects of RCT pathway completely, and this review highlights alternative theories and unanswered questions.     

Human appropriation of net primary production as driver of change in landscape-scale vertebrate richness

Aim

Land use is the most pervasive driver of biodiversity loss. Predicting its impact on species richness (SR) is often based on indicators of habitat loss. However, the degradation of habitats, especially through land-use intensification, also affects species. Here, we evaluate whether an integrative metric of land-use intensity, the human appropriation of net primary production, is correlated with the decline of SR in used landscapes across the globe.

Location

Global.

Time period

Present.

Major taxa studied

Birds, mammals and amphibians.

Methods

Based on species range maps (spatial resolution: 20 km × 20 km) and an area-of-habitat approach, we calibrated a “species–energy model” by correlating the SR of three groups of vertebrates with net primary production and biogeographical covariables in “wilderness” areas (i.e., those where available energy is assumed to be still at pristine levels). We used this model to project the difference between pristine SR and the SR corresponding to the energy remaining in used landscapes (i.e., SR loss expected owing to human energy extraction outside wilderness areas). We validated the projected species loss by comparison with the realized and impending loss reconstructed from habitat conversion and documented by national Red Lists.

Results

Species–energy models largely explained landscape-scale variation of mapped SR in wilderness areas (adjusted R²-values: 0.79–0.93). Model-based projections of SR loss were lower, on average, than reconstructed and documented ones, but the spatial patterns were correlated significantly, with stronger correlation in mammals (Pearson's r = 0.68) than in amphibians (r = 0.60) and birds (r = 0.57).

Main conclusions

Our results suggest that the human appropriation of net primary production is a useful indicator of heterotrophic species loss in used landscapes, hence we recommend its inclusion in models based on species–area relationships to improve predictions of land-use-driven biodiversity loss.