A novel requirement for C. elegans Alix/ALX-1 in RME-1-mediated membrane transport

BACKGROUND: Alix/Bro1p family proteins have recently been identified as important components of multivesicular endosomes (MVEs) and are involved in the sorting of endocytosed integral membrane proteins, interacting with components of the ESCRT complex, the unconventional phospholipid LBPA, and other known endocytosis regulators. During infection, Alix can be co-opted by enveloped retroviruses, including HIV, providing an important function during virus budding from the plasma membrane. In addition, Alix is associated with the actin cytoskeleton and might regulate cytoskeletal dynamics. RESULTS: Here we demonstrate a novel physical interaction between the only apparent Alix/Bro1p family protein in C. elegans, ALX-1, and a key regulator of receptor recycling from endosomes to the plasma membrane, called RME-1. The analysis of alx-1 mutants indicates that ALX-1 is required for the endocytic recycling of specific basolateral cargo in the C. elegans intestine, a pathway previously defined by the analysis of rme-1 mutants. The expression of truncated human Alix in HeLa cells disrupts the recycling of major histocompatibility complex class I, a known Ehd1/RME-1-dependent transport step, suggesting the phylogenetic conservation of this function. We show that the interaction of ALX-1 with RME-1 in C. elegans, mediated by RME-1/YPSL and ALX-1/NPF motifs, is required for this recycling process. In the C. elegans intestine, ALX-1 localizes to both recycling endosomes and MVEs, but the ALX-1/RME-1 interaction appears to be dispensable for ALX-1 function in MVEs and/or late endosomes. CONCLUSIONS: This work provides the first demonstration of a requirement for an Alix/Bro1p family member in the endocytic recycling pathway in association with the recycling regulator RME-1.

Size frequency distribution of prion protein (PrP) aggregates in variant Creutzfeldt-Jakob disease (vCJD)

The frequency distribution of aggregate size of the diffuse and florid-type prion protein (PrP) plaques was studied in various brain regions in cases of variant Creutzfeldt-Jakob disease (vCJD). The size distributions were unimodal and positively skewed and resembled those of β-amyloid (Aβ) deposits in Alzheimer's disease (AD) and Down's syndrome (DS). The frequency distributions of the PrP aggregates were log-normal in shape, but there were deviations from the expected number of plaques in specific size classes. More diffuse plaques were observed in the modal size class and fewer in the larger size classes than expected and more florid plaques were present in the larger size classes compared with the log-normal model. It was concluded that the growth of the PrP aggregates in vCJD does not strictly follow a log-normal model, diffuse plaques growing to within a more restricted size range and florid plaques to larger sizes than predicted. © Springer-Verlag 2005.

MHC class II binding prediction:a little help from a friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

The retromer coat complex coordinates endosomal sorting and dynein-mediated transport, with carrier recognition by the trans-Golgi network

Early endosome-to-trans-Golgi network (TGN) transport is organized by the retromer complex. Consisting of cargo-selective and membrane-bound subcomplexes, retromer coordinates sorting with membrane deformation and carrier formation. Here, we describe four mammalian retromers whose membrane-bound subcomplexes contain specific combinations of the sorting nexins (SNX), SNX1, SNX2, SNX5, and SNX6. We establish that retromer requires a dynamic spatial organization of the endosomal network, which is regulated through association of SNX5/SNX6 with the p150(glued) component of dynactin, an activator of the minus-end directed microtubule motor dynein; an association further defined through genetic studies in C. elegans. Finally, we also establish that the spatial organization of the retromer pathway is mediated through the association of SNX1 with the proposed TGN-localized tether Rab6-interacting protein-1. These interactions describe fundamental steps in retromer-mediated transport and establish that the spatial organization of the retromer network is a critical element required for efficient retromer-mediated sorting.

Novel visualization methods for protein data

Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

EpiTOP—a proteochemometric tool for MHC class II binding prediction

Motivation: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein. Results: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Receptor activity-modifying protein-dependent effects of mutations in the calcitonin receptor-like receptor:implications for adrenomedullin and calcitonin gene-related peptide pharmacology

Background and Purpose Receptor activity-modifying proteins (RAMPs) define the pharmacology of the calcitonin receptor-like receptor (CLR). The interactions of the different RAMPs with this class B GPCR yield high-affinity calcitonin gene-related peptide (CGRP) or adrenomedullin (AM) receptors. However, the mechanism for this is unclear. Experimental Approach Guided by receptor models, we mutated residues in the N-terminal helix of CLR, RAMP2 and RAMP3 hypothesized to be involved in peptide interactions. These were assayed for cAMP production with AM, AM2 and CGRP together with their cell surface expression. Binding studies were also conducted for selected mutants. Key Results An important domain for peptide interactions on CLR from I32 to I52 was defined. Although I41 was universally important for binding and receptor function, the role of other residues depended on both ligand and RAMP. Peptide binding to CLR/RAMP3 involved a more restricted range of residues than that to CLR/RAMP1 or CLR/RAMP2. E101 of RAMP2 had a major role in AM interactions, and F111/W84 of RAMP2/3 was important with each peptide. Conclusions and Implications RAMP-dependent effects of CLR mutations suggest that the different RAMPs control accessibility of peptides to binding residues situated on the CLR N-terminus. RAMP3 appears to alter the role of specific residues at the CLR-RAMP interface compared with RAMP1 and RAMP2. © 2013 The Authors. British Journal of Pharmacology published by John Wiley &. Sons Ltd on behalf of The British Pharmacological Society.

SNX4 coordinates endosomal sorting of TfnR with dynein-mediated transport into the endocytic recycling compartment

SNX-BAR proteins are a sub-family of sorting nexins implicated in endosomal sorting. Here, we establish that through its phox homology (PX) and Bin-Amphiphysin-Rvs (BAR) domains, sorting nexin-4 (SNX4) is associated with tubular and vesicular elements of a compartment that overlaps with peripheral early endosomes and the juxtanuclear endocytic recycling compartment (ERC). Suppression of SNX4 perturbs transport between these compartments and causes lysosomal degradation of the transferrin receptor (TfnR). Through an interaction with KIBRA, a protein previously shown to bind dynein light chain 1, we establish that SNX4 associates with the minus end-directed microtubule motor dynein. Although suppression of KIBRA and dynein perturbs early endosome-to-ERC transport, TfnR sorting is maintained. We propose that by driving membrane tubulation, SNX4 coordinates iterative, geometric-based sorting of the TfnR with the long-range transport of carriers from early endosomes to the ERC. Finally, these data suggest that by associating with molecular motors, SNX-BAR proteins may coordinate sorting with carrier transport between donor and recipient membranes.

A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.

Investigating G protein signalling bias at the glucagon-like peptide-1 receptor in yeast

Background and Purpose The glucagon-like peptide 1 (GLP-1) receptor performs an important role in glycaemic control, stimulating the release of insulin. It is an attractive target for treating type 2 diabetes. Recently, several reports of adverse side effects following prolonged use of GLP-1 receptor therapies have emerged: most likely due to an incomplete understanding of signalling complexities. Experimental Approach We describe the expression of the GLP-1 receptor in a panel of modified yeast strains that couple receptor activation to cell growth via single Gα/yeast chimeras. This assay enables the study of individual ligand-receptor G protein coupling preferences and the quantification of the effect of GLP-1 receptor ligands on G protein selectivity. Key Results The GLP-1 receptor functionally coupled to the chimeras representing the human Gαs, Gαi and Gαq subunits. Calculation of the dissociation constant for a receptor antagonist, exendin-3 revealed no significant difference between the two systems. We obtained previously unobserved differences in G protein signalling bias for clinically relevant therapeutic agents, liraglutide and exenatide; the latter displaying significant bias for the Gαi pathway. We extended the use of the system to investigate small-molecule allosteric compounds and the closely related glucagon receptor. Conclusions and Implications These results provide a better understanding of the molecular events involved in GLP-1 receptor pleiotropic signalling and establish the yeast platform as a robust tool to screen for more selective, efficacious compounds acting at this important class of receptors in the future. © 2014 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of The British Pharmacological Society.

The vacuolar proton-ATPase plays a major role in several membrane-bounded organelles in Paramecium

The vacuolar proton-ATPase (V-ATPase) is a multisubunit enzyme complex that is able to transfer protons over membranes against an electrochemical potential under ATP hydrolysis. The enzyme consists of two subcomplexes: V0, which is membrane embedded; and V1, which is cytosolic. V0 was also reported to be involved in fusion of vacuoles in yeast. We identified six genes encoding c-subunits (proteolipids) of V0 and two genes encoding F-subunits of V1 and studied the role of the V-ATPase in trafficking in Paramecium. Green fluorescent protein (GFP) fusion proteins allowed a clear subcellular localization of c- and F-subunits in the contractile vacuole complex of the osmoregulatory system and in food vacuoles. Several other organelles were also detected, in particular dense core secretory granules (trichocysts). The functional significance of the V-ATPase in Paramecium was investigated by RNA interference (RNAi), using a recently developed feeding method. A novel strategy was used to block the expression of all six c- or both F-subunits simultaneously. The V-ATPase was found to be crucial for osmoregulation, the phagocytotic pathway and the biogenesis of dense core secretory granules. No evidence was found supporting participation of V0 in membrane fusion.

Novel strategies to improve recombinant membrane protein production in yeast

Approximately 60% of pharmaceuticals target membrane proteins; 30% of the human genome codes for membrane proteins yet they represent less than 1% of known unique crystal structures deposited in the Protein Data Bank (PDB), with 50% of structures derived from recombinant membrane proteins having been synthesized in yeasts. G protein-coupled receptors (GPCRs) are an important class of membrane proteins that are not naturally abundant in their native membranes. Unfortunately their recombinant synthesis often suffers from low yields; moreover, function may be lost during extraction and purification from cell membranes, impeding research aimed at structural and functional determination. We therefore devised two novel strategies to improve functional yields of recombinant membrane proteins in the yeast Saccharomyces cerevisiae. We used human adenosine A2A receptor (hA2AR) as a model GPRC since it is functionally and structurally well characterised.In the first strategy, we investigated whether it is possible to provide yeast cells with a selective advantage (SA) in producing the fusion protein hA2AR-Ura3p when grown in medium lacking uracil; Ura3p is a decarboxylase that catalyzes the sixth enzymatic step in the de novo biosynthesis of pyrimidines, generating uridine monophosphate. The first transformant (H1) selected using the SA strategy gave high total yields of hA2AR-Ura3p, but low functional yields as determined by radio-ligand binding, leading to the discovery that the majority of the hA2AR-Ura3p had been internalized to the vacuole. The yeast deletion strain spt3Δ is thought to have slower translation rates and improved folding capabilities compared to wild-type cells and was therefore utilised for the SA strategy to generate a second transformant, SU1, which gave higher functional yields than H1. Subsequently hA2AR-Ura3p from H1 was solubilised with n-dodecyl-β-D-maltoside and cholesteryl hemisuccinate, which yielded functional hA2AR-Ura3p at the highest yield of all approaches used. The second strategy involved using knowledge of translational processes to improve recombinant protein synthesis to increase functional yield. Modification of existing expression vectors with an internal ribosome entry site (IRES) inserted into the 5ˊ untranslated region (UTR) of the gene encoding hA2AR was employed to circumvent regulatory controls on recombinant synthesis in the yeast host cell. The mechanisms involved were investigated through the use of yeast deletion strains and drugs that cause translation inhibition, which is known to improve protein folding and yield. The data highlight the potential to use deletion strains to increase IRES-mediated expression of recombinant hA2AR. Overall, the data presented in this thesis provide mechanistic insights into two novel strategies that can increase functional membrane protein yields in the eukaryotic microbe, S. cerevisiae.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Class I T-cell epitope prediction:improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.