A local resampling trick for focused molecular dynamics

We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can lead to incorrect sampling if the selection depends on the configuration in a way that is not accounted for. We avoid this pitfall by introducing appropriate auxiliary variables. This results in an implementation that is closely related to configurational freezing and elastic barrier dynamical freezing. We implement the method and validate that it can be used to supplement conventional molecular dynamics in free energy calculations (absolute hydration and relative binding)

A systematic review and meta-analysis of e-Mental Health interventions to treat symptoms of post-traumatic stress

Quality control of RNA-seq data: Box Plot of transcript quantification levels. The distributions of FPKM scores across samples are visualized. (PDF 130 kb

Ribosome-associated vesicles: A dynamic subcompartment of the endoplasmic reticulum in secretory cells

The endoplasmic reticulum (ER) is a highly dynamic network of membranes. Here, we combine live-cell microscopy with in situ cryo–electron tomography to directly visualize ER dynamics in several secretory cell types including pancreatic β-cells and neurons under near-native conditions. Using these imaging approaches, we identify a novel, mobile form of ER, ribosome-associated vesicles (RAVs), found primarily in the cell periphery, which is conserved across different cell types and species. We show that RAVs exist as distinct, highly dynamic structures separate from the intact ER reticular architecture that interact with mitochondria via direct intermembrane contacts. These findings describe a new ER subcompartment within cells

RNA-seq analyses of changes in the Anopheles gambiae transcriptome associated with resistance to pyrethroids in Kenya: identification of candidate-resistance genes and candidate-resistance SNPs.

BackgroundThe extensive use of pyrethroids for control of malaria vectors, driven by their cost, efficacy and safety, has led to widespread resistance. To favor their sustainable use, the World Health Organization (WHO) formulated an insecticide resistance management plan, which includes the identification of the mechanisms of resistance and resistance surveillance. Recognized physiological mechanisms of resistance include target site mutations in the para voltage-gated sodium channel, metabolic detoxification and penetration resistance. Such understanding of resistance mechanisms has allowed the development of resistance monitoring tools, including genotyping of the kdr mutation L1014F/S in the para gene.MethodsThe sequence-based technique RNA-seq was applied to study changes in the transcriptome of deltamethrin-resistant and -susceptible Anopheles gambiae mosquitoes from the Western Province of Kenya. The resulting gene expression profiles were compared to data in the most recent literature to derive a list of candidate resistance genes. RNA-seq data were analyzed also to identify sequence polymorphisms linked to resistance.ResultsA total of five candidate-resistance genes (AGAP04177, AGAP004572, AGAP008840, AGAP007530 and AGAP013036) were identified with altered expression between resistant and susceptible mosquitoes from West and East Africa. A change from G to C at position 36043997 of chromosome 3R resulting in A101G of the sulfotransferase gene AGAP009551 was significantly associated with the resistance phenotype (odds ratio: 5.10). The kdr L1014S mutation was detected at similar frequencies in both phenotypically resistant and susceptible mosquitoes, suggesting it is no longer fully predictive of the resistant phenotype.ConclusionsOverall, these results support the conclusion that resistance to pyrethroids is a complex and evolving phenotype, dependent on multiple gene functions including, but not limited to, metabolic detoxification. Functional convergence among metabolic detoxification genes may exist, with the role of each gene being modulated by the life history and selection pressure on mosquito populations. As a consequence, biochemical assays that quantify overall enzyme activity may be a more suitable method for predicting metabolic resistance than gene-based assays

Additional file 4: of RNA-seq analyses of changes in the Anopheles gambiae transcriptome associated with resistance to pyrethroids in Kenya: identification of candidate-resistance genes and candidate-resistance SNPs

Lists of DE genes. List of the 2457 genes significantly DE between field-caught resistant and susceptible mosquitoes (Sheet 2457 RvS). List of the 182 genes significantly DE between field-caught resistant and susceptible mosquitoes of the Kisumu strain, but not field-caught susceptible mosquitoes and Kisumu mosquitoes (sheet 182 constitutive DE gene). List of the 55 DE genes in mosquitoes from Western Kenya in 2010 and 2012 (Sheet 55 candidate resistance genes). (XLSX 394 kb

Ribosome-associated Vesicles: A Dynamic Subcompartment of the Endoplasmic Reticulum in Secretory Cells

The endoplasmic reticulum (ER) is a highly dynamic network of membranes. Here, we combine live-cell microscopy with in situ cryo-electron tomography to directly visualize ER dynamics in several secretory cell types including pancreatic β-cells and neurons under near-native conditions. Using these imaging approaches, we identify a novel, mobile form of ER, ribosome-associated vesicles (RAVs), found primarily in the cell periphery, which is conserved across different cell types and species. We show that RAVs exist as distinct, highly dynamic structures separate from the intact ER reticular architecture that interact with mitochondria via direct intermembrane contacts. These findings describe a new ER subcompartment within cells.Support for this study was provided by the L. V. Gerstner Jr., Scholars Program (to Z.F.), the Leon Levy Foundation (to Z.F.), the John F. and Nancy A. Emmerling Fund of the Pittsburgh Foundation (to Z.F.), the Department of Defense PR141292 (to Z.F.), NIH K08DA031241 (to Z.F.), NSF MCB-1408986 (to S.A.M.), the National Science Foundation Graduate Research Fellowship (to N.H.T.), NIH K01AG045335 (to E.A.G.), NIH 1S10RR019003 (to S.C.W.), NIH 1S10RR025488 (to S.C.W.), NIH 1S10RR016236 (to S.C.W.), NIH F30NS093798 (to S.E.S.), NIH R56AG058593 (to Z.P.W.), the Howard Hughes Medical Institute (to P.W., N.H.T., J.F., and G.J.J.), NIH GM29169 (to J.F.), NIH GM122588 (to G.J.J.), NIH AI150464 (to G.J.J.), the Israel Science Foundation Grant 1285/14 (to S.G.W.), the European Research Council under the European Union’s Seventh Framework Programme (grant number 310649) (to D.F.), MINECO AIC-A-2011-0638 (to J.M.C.), the Spanish Ministry of Economy and Competitiveness grant BIO2016-76400-R AEI/FEDER, UE (to J.M.C.), and Comunidad Autónoma de Madrid grant S2017/BMD-3817 (to J.M.C.). Some of the cryo-ET was performed in the Beckman Institute Resource Center for Transmission EM at Caltech. Additional work was also performed at the Simons Electron Microscopy Center and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, supported by grants from the Simons Foundation (349247), NYSTAR, and the NIH National Institute of General Medical Sciences (GM103310) with added support from NIH S10 RR029300-01. CSTET data acquisition was partially supported by the Irving and Cherna Moskowitz Center for Nano and Bio-Nano Imaging at the Weizmann Institute of Science. Some of the live confocal images were collected and processed in the Confocal and Specialized Microscopy Shared Resource of the Herbert Irving Comprehensive Cancer Center at Columbia University and supported by NIH P30 CA013696. Part of the cryo-EM image processing was conducted as an Instruct-ERIC collaboration project PD1222 at the Instruct Image Processing CenterPeer reviewe