Summary FMRP loss-of-function causes Fragile X Syndrome (FXS) and autistic features. FMRP is a polyribosome-associated neuronal RNA-binding protein, suggesting that it plays a key role in regulating neuronal translation, but there has been little consensus regarding either its RNA targets or mechanism of action. Here we use high throughput sequencing of RNAs isolated by crosslinking immunoprecipitation (HITS-CLIP) to identify FMRP interactions with mouse brain polyribosomal mRNAs. FMRP interacts with the coding region of transcripts encoding pre- and postsynaptic proteins, and transcripts implicated in autism spectrum disorders (ASD). We developed a brain polyribosome-programmed translation system, revealing that FMRP reversibly stalls ribosomes specifically on its target mRNAs. Our results indicate that loss of a translational brake on the synthesis of a subset of synaptic proteins may contribute to FXS. In addition, they provide insight into the molecular basis of the cognitive and allied defects in FXS and ASD, and suggest multiple targets for clinical intervention.

We have determined the solution structure of the complex between an arginine-glycine-rich RGG peptide from the fragile X mental retardation protein (FMRP) and an in vitro-selected guanine-rich sc1 RNA. The bound RNA forms a novel G-quadruplex separated from the flanking duplex stem by a mixed junctional tetrad. The RGG peptide is positioned along the major groove of the RNA duplex, with the G-quadruplex forcing a sharp turn of R10GGGGR15 at the duplex-quadruplex junction. Arginines R10 and R15 form cross-strand specificity-determining intermolecular hydrogen-bonds with the major-groove edges of guanines of adjacent Watson-Crick G•C pairs. Filter binding assays on RNA and peptide mutations identify and validate contributions of peptide-RNA intermolecular contacts and shape complementarity to molecular recognition. These findings on FMRP RGG domain recognition by a combination of G-quadruplex and surrounding RNA sequences have implications for recognition of other genomic G-rich RNAs.

The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called “travel behavior analysis” or “human mobility analysis”). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.

BackgroundExcess adiposity is associated with cardiovascular disease (CVD) risk factors such as hypertension, diabetes mellitus and dyslipidemia. Amongst the various measures of adiposity, the best one to help predict these risk factors remains contentious. A novel index of adiposity, the Body Adiposity Index (BAI) was proposed in 2011, and has not been extensively studied in all populations. Therefore, the purpose of this study is to compare the relationship between Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), Waist-to-Height Ratio (WHtR), Body Adiposity Index (BAI) and CVD risk factors in the local adult population.Methods and FindingsThis is a cross sectional study involving 1,891 subjects (Chinese 59.1% Malay 22.2%, Indian 18.7%), aged 21–74 years, based on an employee health screening (2012) undertaken at a hospital in Singapore. Anthropometric indices and CVD risk factor variables were measured, and Spearman correlation, Receiver Operating Characteristic (ROC) curves and multiple logistic regressions were used. BAI consistently had the lower correlation, area under ROC and odd ratio values when compared with BMI, WC and WHtR, although differences were often small with overlapping 95% confidence intervals. After adjusting for BMI, BAI did not further increase the odds of CVD risk factors, unlike WC and WHtR (for all except hypertension and low high density lipoprotein cholesterol). When subjects with the various CVD risk factors were grouped according to established cut-offs, a BMI of ≥23.0 kg/m2 and/or WHtR ≥0.5 identified the highest proportion for all the CVD risk factors in both genders, even higher than a combination of BMI and WC.ConclusionsBAI may function as a measure of overall adiposity but it is unlikely to be better than BMI. A combination of BMI and WHtR could have the best clinical utility in identifying patients with CVD risk factors in an adult population in Singapore.