Picking the right tool for the job--Phosphoproteomics of egg activation.
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ABSTRACT:
SUBMITTER: Wessel GM
PROVIDER: S-EPMC5093332 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.
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Project description:BackgroundDue to the recent increased use of the McMaster (MM) fecal egg counting method for assessing benzimidazole drug efficacy for treating soil-transmitted helminth (STH) infections, the aim of the current study was to determine the operational value of including the MM method alongside the Kato-Katz (KK) fecal thick smear to increase the diagnostic sensitivity when STHs are co-endemic with trematode helminths (e.g., Schistosoma mansoni).MethodsA cross-sectional study was conducted in school-aged children aged 4-18 years in the northeastern region of the State of Minas Gerais (Brazil), where Necator americanus, Ascaris lumbricoides, Trichuris trichiura, and S. mansoni are co-endemic. One fecal sample from each participant was collected and transported to the field laboratory for analysis. Coprological diagnosis was performed on each fecal sample by three different methods: Formalin-Ether Sedimentation (FES), KK and the MM technique. The diagnostic sensitivity and negative predictive value (NPV) of each technique was calculated using the combination of all three techniques as the composite standard. In order to determine the agreement between the three techniques Fleiss´ kappa was used. Both the Cure Rate (CR) and the Fecal Egg Count Reduction (FECR) were calculated using the two quantification techniques (i.e., the MM and KK).ResultsFecal samples from 1260 children were analyzed. The KK had higher diagnostic sensitivity than the MM for the detection of both A. lumbricoides (KK 97.3%, MM 69.5%) and hookworm (KK 95.1%, MM 80.8%). The CR of a single dose of mebendazole varied significantly between the KK and MM for both A. lumbricoides (p = 0.016) and hookworm (p = 0.000), with lower rates obtained with the KK. On the other hand, the FECR was very similar between both techniques for both A. lumbricoides and hookworm.ConclusionThe MM did not add any diagnostic value over the KK in areas where both STHs and trematodes were co-endemic. The lower sensitivity of the MM would have an important impact on the administration of selective school-based treatment in this area since if only the MM were used, 36 (13.9%) children diagnosed with A. lumbricoides would have gone untreated.
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Project description:BackgroundPicking images of particles in cryo-electron micrographs is an important step in solving the 3D structures of large macromolecular assemblies. However, in order to achieve sub-nanometre resolution it is often necessary to capture and process many thousands or even several millions of 2D particle images. Thus, a computational bottleneck in reaching high resolution is the accurate and automatic picking of particles from raw cryo-electron micrographs.ResultsWe have developed "gEMpicker", a highly parallel correlation-based particle picking tool. To our knowledge, gEMpicker is the first particle picking program to use multiple graphics processor units (GPUs) to accelerate the calculation. When tested on the publicly available keyhole limpet hemocyanin dataset, we find that gEMpicker gives similar results to the FindEM program. However, compared to calculating correlations on one core of a contemporary central processor unit (CPU), running gEMpicker on a modern GPU gives a speed-up of about 27 ×. To achieve even higher processing speeds, the basic correlation calculations are accelerated considerably by using a hierarchy of parallel programming techniques to distribute the calculation over multiple GPUs and CPU cores attached to multiple nodes of a computer cluster. By using a theoretically optimal reduction algorithm to collect and combine the cluster calculation results, the speed of the overall calculation scales almost linearly with the number of cluster nodes available.ConclusionsThe very high picking throughput that is now possible using GPU-powered workstations or computer clusters will help experimentalists to achieve higher resolution 3D reconstructions more rapidly than before.
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Project description:Deubiquitinases (DUBs) have been the subject of intense scrutiny in recent years. Many of their diverse enzymatic mechanisms are well characterized in vitro; however, our understanding of these enzymes at the cellular level lags due to the lack of quality tool reagents. DUBs play a role in seemingly every biological process and are central to many human pathologies, thus rendering them very desirable and challenging therapeutic targets. This review aims to provide researchers entering the field of ubiquitination with knowledge of the pharmacological modulators and tool molecules available to study DUBs. A focus is placed on small molecule inhibitors, ubiquitin variants (UbVs), and activity-based probes (ABPs). Leveraging these tools to uncover DUB biology at the cellular level is of particular importance and may lead to significant breakthroughs. Despite significant drug discovery efforts, only approximately 15 chemical probe-quality small molecule inhibitors have been reported, hitting just 6 of about 100 DUB targets. UbV technology is a promising approach to rapidly expand the library of known DUB inhibitors and may be used as a combinatorial platform for structure-guided drug design.
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Project description:Quantitative proteomics generates large datasets with increasing depth and quantitative information. With the advance of mass spectrometry and increasingly larger data sets, streamlined methodologies and tools for analysis and visualization of phosphoproteomics are needed both at the protein and modified peptide levels. To assist in addressing this need, we developed ProteoViz, which includes a set of R scripts that perform normalization and differential expression analysis of both the proteins and enriched phosphorylated peptides, and identify sequence motifs, kinases, and gene set enrichment pathways. The tool generates interactive visualization plots that allow users to interact with the phosphoproteomics results and quickly identify proteins and phosphorylated peptides of interest for their biological study. The tool also links significant phosphosites with sequence motifs and pathways that will help explain the experimental conditions and guide future experiments. Here, we present the workflow and demonstrate its functionality by analyzing a phosphoproteomic data set from two lymphoma cell lines treated with kinase inhibitors. The scripts and data are freely available at and via the ProteomeXchange with identifier PXD015606.
| S-EPMC7423749 | biostudies-literature
Project description:AimsIn right ventricular cardiomyopathy (RVCM), intramural scar may prevent rapid transmural activation, which may facilitate subepicardial ventricular tachycardia (VT) circuits. A critical transmural activation delay determined during sinus rhythm (SR) may identify VT substrates in RVCM.Methods and resultsConsecutive patients with RVCM who underwent detailed endocardial-epicardial mapping and ablation for scar-related VT were enrolled. The transmural activation interval (TAI, first endocardial to first epicardial activation) and maximal activation interval (MAI, first endocardial to last epicardial activation) were determined in endocardial-epicardial point pairs located <10 mm apart. VT-related sites were determined by conventional substrate mapping and limited activation mapping when possible. Nineteen patients (46 ± 16 years, 84% male, 63% arrhythmogenic right ventricular cardiomyopathy, 37% exercise-induced arrhythmogenic remodelling) were inducible for 44 VT [CL 283 (interquartile range, IQR 240-325)ms]. A total of 2569 endocardial-epicardial coupled point pairs were analysed, including 98 (4%) epicardial VT-related sites.The TAI and MAI were significantly longer at VT-related sites compared with other electroanatomical scar sites [TAI median 31 (IQR 11-50) vs. 2 (-7-11)ms, P < 0.001; MAI median 65 (IQR 45-87) vs. 23 (13-39)ms, P < 0.001]. TAI and MAI allowed highly accurate identification of epicardial VT-related sites (optimal cut-off TAI 17 ms and MAI 45 ms, both AUC 0.81). Both TAI and MAI had a better predictive accuracy for VT-related sites than endocardial and epicardial voltage and electrogram (EGM) duration (AUC 0.51-0.73).ConclusionThe transmural activation delay in SR can be used to identify VT substrates in patients with RVCM and predominantly hemodynamically non-tolerated VT, and may be an important new mapping tool for substrate-based ablation.
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Project description:Embryo mortality rate, which can reach up to 40% in avian species, is a major issue for breeding. It is therefore important to identify new embryo development biomarkers for genetic selection to improve reproductive performances. We have recently shown that chemerin is expressed in the oviductal hen magnum, accumulates in egg white, is correlated with embryo survival and could thus be used as a molecular marker of embryo development. Eggs from seven hen breeds (n = 70) were collected during five successive days at the end of the laying period. After weighing eggs, yolk and albumen, an egg white sample from each egg was collected and a blood sample was taken from each hen. Chemerin concentrations in albumen and blood samples were measured by a specific home made ELISA assay. Hen's plasma and egg's albumen chemerin levels were found to be correlated with reproductive parameters such as fecundity, fertility, embryo mortality, hatchability and laying rates. The inter-hen chemerin level variability in albumen was higher than intra-hen except for one breed (R+). We observed significantly different levels of chemerin in egg white between breeds. However, chemerin concentrations in egg white were not significantly associated to variations of hen plasma chemerin levels. Interestingly, we observed negative correlations between albumen chemerin concentrations and egg weight (r = -0.43, p = 0.001), between albumen weight (r = -0.40, p = 0.002), and between yolk weight (r = -0.28, p = 0.03). We also showed negative correlations between egg white chemerin concentrations and fecundity (r = -0.32, p = 0.011) and fertility (r = -0.27, p = 0.04) whereas no significant correlation was observed with the laying rate. Taken together, these results suggest that egg white chemerin concentration might be a good biomarker for genetic selection for egg weight and fertility in hens, provided these data are confirmed on a larger scale.
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Project description:SummaryComputational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.Availability and implementationthe KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.Contactmark.chance@case.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
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