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Optimising methods for the preservation, capture and identification of ubiquitin chains and ubiquitylated proteins by immunoblotting.

ABSTRACT: Immunoblotting is a powerful technique for the semi-quantitative analysis of ubiquitylation events, and remains the most commonly used method to study this process due to its high specificity, speed, sensitivity and relatively low cost. However, the ubiquitylation of proteins is complex and, when the analysis is performed in an inappropriate manner, it can lead to the misinterpretation of results and to erroneous conclusions being reached. Here we discuss the advantages and disadvantages of the methods currently in use to analyse ubiquitin chains and protein ubiquitylation, and describe the procedures that we have found to be most useful for optimising the quality and reliability of the data that we have generated. We also highlight commonly encountered problems and the pitfalls inherent in some of these methods. Finally, we introduce a set of recommendations to help researchers obtain high quality data, especially those new to the field of ubiquitin signalling. The specific topics addressed in this article include sample preparation, the separation, detection and identification of particular ubiquitin chains by immunoblotting, and the analysis of ubiquitin chain topology through the combined use of ubiquitin-binding proteins and ubiquitin linkage-specific deubiquitylases.

SUBMITTER: Emmerich CH

PROVIDER: S-EPMC4709362 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Optimising methods for the preservation, capture and identification of ubiquitin chains and ubiquitylated proteins by immunoblotting.

Emmerich Christoph H CH Cohen Philip P

Biochemical and biophysical research communications 20150829 1

Immunoblotting is a powerful technique for the semi-quantitative analysis of ubiquitylation events, and remains the most commonly used method to study this process due to its high specificity, speed, sensitivity and relatively low cost. However, the ubiquitylation of proteins is complex and, when the analysis is performed in an inappropriate manner, it can lead to the misinterpretation of results and to erroneous conclusions being reached. Here we discuss the advantages and disadvantages of the ...[more]

PMID: 26325464

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Project description:A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.

Dataset Information

Optimising methods for the preservation, capture and identification of ubiquitin chains and ubiquitylated proteins by immunoblotting.

Publications

Optimising methods for the preservation, capture and identification of ubiquitin chains and ubiquitylated proteins by immunoblotting.

Similar Datasets

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