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Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes.

ABSTRACT: Understanding the function of the thousands of cellular proteins is a central question in molecular cell biology. As proteins are typically part of multiple dynamic and often overlapping macromolecular complexes exerting distinct functions, the identification of protein-protein interactions (PPI) and their assignment to specific complexes is a crucial but challenging task. We present a protein fragments complementation assay integrated with the proximity-dependent biotinylation technique BioID. Activated on the interaction of two proteins, split-BioID is a conditional proteomics approach that allows in a single and simple assay to both experimentally validate binary PPI and to unbiasedly identify additional interacting factors. Applying our method to the miRNA-mediated silencing pathway, we can probe the proteomes of two distinct functional complexes containing the Ago2 protein and uncover the protein GIGYF2 as a regulator of miRNA-mediated translation repression. Hence, we provide a novel tool to study dynamic spatiotemporally defined protein complexes in their native cellular environment.

SUBMITTER: Schopp IM

PROVIDER: S-EPMC5467174 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes.

Schopp Isabel Myriam IM Amaya Ramirez Cinthia Claudia CC Debeljak Jerneja J Kreibich Elisa E Skribbe Merle M Wild Klemens K Béthune Julien J

Nature communications 20170606

Understanding the function of the thousands of cellular proteins is a central question in molecular cell biology. As proteins are typically part of multiple dynamic and often overlapping macromolecular complexes exerting distinct functions, the identification of protein-protein interactions (PPI) and their assignment to specific complexes is a crucial but challenging task. We present a protein fragments complementation assay integrated with the proximity-dependent biotinylation technique BioID. ...[more]

PMID: 28585547

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Project description:To complement existing affinity purification (AP) approaches for the identification of protein-protein interactions (PPI), enzymes have been introduced that allow the proximity-dependent labeling of proteins in living cells. One such enzyme, BirA* (used in the BioID approach), mediates the biotinylation of proteins within a range of approximately 10 nm. Hence, when fused to a protein of interest and expressed in cells, it allows the labeling of proximal proteins in their native environment. As opposed to AP that relies on the purification of assembled protein complexes, BioID detects proteins that have been marked within cells no matter whether they are still interacting with the protein of interest when they are isolated. Since it biotinylates proximal proteins, one can moreover capitalize on the exceptional affinity of streptavidin for biotin to very efficiently isolate them. While BioID performs better than AP for identifying transient or weak interactions, both AP- and BioID-mass spectrometry approaches provide an overview of all possible interactions a given protein may have. However, they do not provide information on the context of each identified PPI. Indeed, most proteins are typically part of several complexes, corresponding to distinct maturation steps or different functional units. To address this common limitation of both methods, we have engineered a protein-fragments complementation assay based on the BirA* enzyme. In this assay, two inactive fragments of BirA* can reassemble into an active enzyme when brought in close proximity by two interacting proteins to which they are fused. The resulting split-BioID assay thus allows the labeling of proteins that assemble around a pair of interacting proteins. Provided these two only interact in a given context, split-BioID then allows the analysis of specific context-dependent functional units in their native cellular environment. Here, we provide a step-by-step protocol to test and apply split-BioID to a pair of interacting proteins.

Project description:Bacillus thuringiensis (Bt) is the most widely used active ingredient for biological insecticides. The composition of ?-endotoxins (Cry and Cyt proteins) in the parasporal crystal determines the toxicity profile of each Bt strain. However, a reliable method for their identification and quantification has not been available, due to the high sequence identity of the genes that encode the ?-endotoxins and the toxins themselves. Here, we have developed an accurate and reproducible mass spectrometry-based method (liquid chromatography-tandem mass spectrometry-multiple reaction monitoring [LC-MS/MS-MRM]) using isotopically labeled proteotypic peptides for each protein in a particular mixture to determine the relative proportion of each ?-endotoxin within the crystal. To validate the method, artificial mixtures containing Cry1Aa, Cry2Aa, and Cry6Aa were analyzed. Determination of the relative abundance of proteins (in molarity) with our method was in good agreement with the expected values. This method was then applied to the most common commercial Bt-based products, DiPel DF, XenTari GD, VectoBac 12S, and Novodor, in which between three and six ?-endotoxins were identified and quantified in each product. This novel approach is of great value for the characterization of Bt-based products, not only providing information on host range, but also for monitoring industrial crystal production and quality control and product registration for Bt-based insecticides.IMPORTANCE Bacillus thuringiensis (Bt)-based biological insecticides are used extensively to control insect pests and vectors of human diseases. Bt-based products provide greater specificity and biosafety than broad-spectrum synthetic insecticides. The biological activity of this bacterium resides in spores and crystals comprising complex mixtures of toxic proteins. We developed and validated a fast, accurate, and reproducible method for quantitative determination of the crystal components of Bt-based products. This method will find clear applications in the improvement of various aspects of the industrial production process of Bt. An important aspect of the production of Bt-based insecticides is its quality control. By specifically quantifying the relative proportion of each of the toxins that make up the crystal, our method represents the most consistent and repeatable evaluation procedure in the quality control of different batches produced in successive fermentations. This method can also contribute to the design of specific culture media and fermentation conditions that optimize Bt crystal composition across a range of Bt strains that target different pestiferous insects. Quantitative information on crystal composition should also prove valuable to phytosanitary product registration authorities that oversee the safety and efficacy of crop protection products.

Project description:To provide data that can guide community-targeted practices, policies, and interventions in urban metropolitan areas, we used geospatial analysis to examine the community-level opioid overdose death determinants and their spatial variation across a study area. We obtained spatial datasets containing multiple, high-quality measures of socioeconomic conditions, public health status, and demographics for analysis and visualization in geographic information systems. We employed a multiscale modeling approach (multiscale geographically weighted regression; MGWR) to provide a comprehensive and robust analysis of opioid overdose death determinants, explain how geospatial patterns vary across scales across Milwaukee County in 2019, and examine the differential influence of factors locally, regionally, and globally. We subsequently examined how associations varied with the racial/ethnic composition of communities by dividing Milwaukee County into White-majority, Black-majority, and Hispanic-majority regions according to census data and conducting separate, independent modeling processes. Overall, the multiscale model explained 83% of opioid overdose death variability across neighborhoods in Milwaukee County using 12 selected variables. Statistical analysis and geovisualization of patterns, trends, and clusters using MGWR unveiled dramatic racialized health disparities in Milwaukee, showing how factors that influenced opioid overdose deaths varied across diverse communities in Milwaukee. The observed geographic variation in relationships included the impact of naloxone availability and incarceration rates on overdose deaths with pronounced differences between White communities and communities of color. Understanding, community-level factors that contribute to overdose risk should guide targeted community-level solutions. Overall, our findings demonstrate the value of precision epidemiology using MGWR analysis for defining and guiding responses to public health challenges.

Dataset Information

Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes.

Publications

Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes.

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