Biology subjects

Scott, N. E.

Publications and source records attributed to Scott, N. E..

3 recordsLinked to original sources

bioRxiv2018

A dual motif mediates outer-membrane translocation and packing of glycosidases into Bacteroides Outer Membrane Vesicles.

Outer membrane vesicles (OMV) are spherical structures derived from the outer membrane (OM) of Gram-negative bacteria. Bacteroides spp. are prominent components of the human gut microbiota, and OMV produced with these species are proposed to play key roles in gut homeostasis. OMV biogenesis in Bacteroides is a poorly understood process. Here, we revisited the protein composition of B. theta OMVs by mass spectrometry. We confirmed that OMVs produced by this organism contain large quantities of glycosidases and proteases, with most of them being lipoproteins. We found that most of these OMV-enriched lipoproteins are encoded by polysaccharide utilization loci (PULs), such as the sus operon. We examined the subcellular localization of the components of the Sus system, and found that the alpha-amylase SusG is highly enriched in OMVs while the oligosaccharide importer SusC remains mostly in the OM. We show that all OMV-enriched lipoproteins possess a lipoprotein export sequence (LES) that mediates translocation of SusG from the periplasmic face of the OM towards the extracellular milieu and is required for SusG to localize preferentially to OMVs. We also show that surface-exposed SusG in OMVs is active and can rescue growth of bacterial cells incapable of growing on starch as only carbon source. Our results support the role of OMVs as \"public goods\" that can be utilized by other organisms with different metabolic capabilities.\n\nIMPORTANCESpecies from the Bacteroides genus are predominant members of the human gut microbiota. OMVs in Bacteroides have been shown to be important for the homeostasis of complex host-commensal relationships, mainly involving immune tolerance and protection from disease. OMVs carry many enzymatic activities involved in the cleavage of complex polysaccharides and have been proposed as public goods that can provide growth to other bacterial species by release of polysaccharide breakdown products into the gut lumen. Nevertheless, the mechanistic nature of OMV biogenesis is unclear for Bacteroides spp. This works shows the presence of a negatively-charged rich amino acid dual motif that is required for efficient packing of the surface-exposed alpha-amylase SusG into OMVs. Discovery of this motif (LES) is the first step in the generation of tailor made probiotic interventions that can exploit LES-related sequences to generate Bacteroides strains displaying proteins of interest in OMVs.

microbiology↗

bioRxiv2018

An atlas of protein-protein interactions across mammalian tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

systems biology↗

bioRxiv2017

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

BackgroundAn organisms protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.\n\nResultsHere we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, better performance, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2015b). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.\n\nConclusionsPrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics proficiency to analyze high-throughput co-elution datasets.

bioinformatics↗