Genome-wide association study meta-analysis for quantitative ultrasound parameters of bone identifies five novel loci for broadband ultrasound attenuation.

Osteoporosis is a common and debilitating bone disease that is characterised by low bone mineral density, typically assessed using dual-energy X-ray absorptiometry. Quantitative ultrasound (QUS), commonly utilising the two parameters velocity of sound (VOS) and broadband ultrasound attenuation (BUA), is an alternative technology used to assess bone properties at peripheral skeletal sites. The genetic influence on the bone qualities assessed by QUS remains an under-studied area. We performed a comprehensive GWAS including low-frequency variants (MAF ≥0.005) for BUA and VOS using a discovery population of individuals with whole-genome sequence (WGS) data from the UK10K project (n=1,268). These results were then meta-analysed with those from two deeply imputed GWAS replication cohorts (n=1,610 and 13,749). In the gender-combined analysis, we identified eight loci associated with BUA and five with VOS at the genome-wide significance level, including three novel loci for BUA at 8p23.1 (PPP1R3B), 11q23.1 (LOC387810) and 22q11.21 (SEPT5) (P = 2.4 × 10-8-1.6 × 10-9). Gene-based association testing in the gender-combined dataset revealed eight loci associated with BUA and seven with VOS at the genome-wide significance level, with one novel locus for BUA at FAM167A (8p23.1) (P = 1.4 × 10-6). An additional novel locus for BUA was seen in the male-specific analysis at DEFB103B (8p23.1) (P = 1.8 × 10-6). Fracture analysis revealed significant associations between variation at the WNT16 and RSPO3 loci and fracture risk (P = 0.004 and 4.0 × 10-4 respectively). In conclusion, by performing a large GWAS meta-analysis for QUS parameters of bone using a combination of WGS and deeply imputed genotype data, we have identified five novel genetic loci associated with BUA

Cell-type-specific profiling of protein-DNA interactions without cell isolation using targeted DamID with next-generation sequencing.

This protocol is an extension to: Nat. Protoc. 2, 1467-1478 (2007); doi:10.1038/nprot.2007.148; published online 7 June 2007The ability to profile transcription and chromatin binding in a cell-type-specific manner is a powerful aid to understanding cell-fate specification and cellular function in multicellular organisms. We recently developed targeted DamID (TaDa) to enable genome-wide, cell-type-specific profiling of DNA- and chromatin-binding proteins in vivo without cell isolation. As a protocol extension, this article describes substantial modifications to an existing protocol, and it offers additional applications. TaDa builds upon DamID, a technique for detecting genome-wide DNA-binding profiles of proteins, by coupling it with the GAL4 system in Drosophila to enable both temporal and spatial resolution. TaDa ensures that Dam-fusion proteins are expressed at very low levels, thus avoiding toxicity and potential artifacts from overexpression. The modifications to the core DamID technique presented here also increase the speed of sample processing and throughput, and adapt the method to next-generation sequencing technology. TaDa is robust, reproducible and highly sensitive. Compared with other methods for cell-type-specific profiling, the technique requires no cell-sorting, cross-linking or antisera, and binding profiles can be generated from as few as 10,000 total induced cells. By profiling the genome-wide binding of RNA polymerase II (Pol II), TaDa can also identify transcribed genes in a cell-type-specific manner. Here we describe a detailed protocol for carrying out TaDa experiments and preparing the material for next-generation sequencing. Although we developed TaDa in Drosophila, it should be easily adapted to other organisms with an inducible expression system. Once transgenic animals are obtained, the entire experimental procedure-from collecting tissue samples to generating sequencing libraries-can be accomplished within 5 d.This work was funded by a Wellcome Trust Senior Investigator Award (103792), Wellcome Trust Programme Grant (092545) and BBSRC Project Grant (BB/L00786X/1) to A.H.B. A.H.B acknowledges core funding to the Gurdon Institute from the Wellcome Trust (092096) and CRUK (C6946/A14492).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nprot.2016.08

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding <it>in silico </it>prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.</p> <p>Results</p> <p>Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species <it>Arabidopsis thaliana</it>. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using <it>Arabidopsis thaliana </it>phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.</p> <p>Conclusions</p> <p>This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos <url>http://csb.cse.yzu.edu.tw/PlantPhos/</url>. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.</p

Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship

Optogenetics allows the manipulation of neural activity in freely moving animals with millisecond precision, but its application in Drosophila melanogaster has been limited. Here we show that a recently described red activatable channelrhodopsin (ReaChR) permits control of complex behavior in freely moving adult flies, at wavelengths that are not thought to interfere with normal visual function. This tool affords the opportunity to control neural activity over a broad dynamic range of stimulation intensities. Using time-resolved activation, we show that the neural control of male courtship song can be separated into (i) probabilistic, persistent and (ii) deterministic, command-like components. The former, but not the latter, neurons are subject to functional modulation by social experience, which supports the idea that they constitute a locus of state-dependent influence. This separation is not evident using thermogenetic tools, a result underscoring the importance of temporally precise control of neuronal activation in the functional dissection of neural circuits in Drosophila

Characterization of Profilin Polymorphism in Pollen with a Focus on Multifunctionality

Profilin, a multigene family involved in actin dynamics, is a multiple partners-interacting protein, as regard of the presence of at least of three binding domains encompassing actin, phosphoinositide lipids, and poly-L-proline interacting patches. In addition, pollen profilins are important allergens in several species like Olea europaea L. (Ole e 2), Betula pendula (Bet v 2), Phleum pratense (Phl p 12), Zea mays (Zea m 12) and Corylus avellana (Cor a 2). In spite of the biological and clinical importance of these molecules, variability in pollen profilin sequences has been poorly pointed out up until now. In this work, a relatively high number of pollen profilin sequences have been cloned, with the aim of carrying out an extensive characterization of their polymorphism among 24 olive cultivars and the above mentioned plant species. Our results indicate a high level of variability in the sequences analyzed. Quantitative intra-specific/varietal polymorphism was higher in comparison to inter-specific/cultivars comparisons. Multi-optional posttranslational modifications, e.g. phosphorylation sites, physicochemical properties, and partners-interacting functional residues have been shown to be affected by profilin polymorphism. As a result of this variability, profilins yielded a clear taxonomic separation between the five plant species. Profilin family multifunctionality might be inferred by natural variation through profilin isovariants generated among olive germplasm, as a result of polymorphism. The high variability might result in both differential profilin properties and differences in the regulation of the interaction with natural partners, affecting the mechanisms underlying the transmission of signals throughout signaling pathways in response to different stress environments. Moreover, elucidating the effect of profilin polymorphism in adaptive responses like actin dynamics, and cellular behavior, represents an exciting research goal for the future

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common-and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.Peer reviewe

Modeling and Analysis of the Molecular Basis of Pain in Sensory Neurons

Intracellular calcium dynamics are critical to cellular functions like pain transmission. Extracellular ATP plays an important role in modulating intracellular calcium levels by interacting with the P2 family of surface receptors. In this study, we developed a mechanistic mathematical model of ATP-induced P2 mediated calcium signaling in archetype sensory neurons. The model architecture, which described 90 species connected by 162 interactions, was formulated by aggregating disparate molecular modules from literature. Unlike previous models, only mass action kinetics were used to describe the rate of molecular interactions. Thus, the majority of the 252 unknown model parameters were either association, dissociation or catalytic rate constants. Model parameters were estimated from nine independent data sets taken from multiple laboratories. The training data consisted of both dynamic and steady-state measurements. However, because of the complexity of the calcium network, we were unable to estimate unique model parameters. Instead, we estimated a family or ensemble of probable parameter sets using a multi-objective thermal ensemble method. Each member of the ensemble met an error criterion and was located along or near the optimal trade-off surface between the individual training data sets. The model quantitatively reproduced experimental measurements from dorsal root ganglion neurons as a function of extracellular ATP forcing. Hypothesized architecture linking phosphoinositide regulation with P2X receptor activity explained the inhibition of P2X-mediated current flow by activated metabotropic P2Y receptors. Sensitivity analysis using individual and the whole system outputs suggested which molecular subsystems were most important following P2 activation. Taken together, modeling and analysis of ATP-induced P2 mediated calcium signaling generated qualitative insight into the critical interactions controlling ATP induced calcium dynamics. Understanding these critical interactions may prove useful for the design of the next generation of molecular pain management strategies