The molecular basis for selective inhibition of unconventional mRNA splicing by an IRE1-binding small molecule.

IRE1 couples endoplasmic reticulum unfolded protein load to RNA cleavage events that culminate in the sequence-specific splicing of the Xbp1 mRNA and in the regulated degradation of diverse membrane-bound mRNAs. We report on the identification of a small molecule inhibitor that attains its selectivity by forming an unusually stable Schiff base with lysine 907 in the IRE1 endonuclease domain, explained by solvent inaccessibility of the imine bond in the enzyme-inhibitor complex. The inhibitor (abbreviated 4µ8C) blocks substrate access to the active site of IRE1 and selectively inactivates both Xbp1 splicing and IRE1-mediated mRNA degradation. Surprisingly, inhibition of IRE1 endonuclease activity does not sensitize cells to the consequences of acute endoplasmic reticulum stress, but rather interferes with the expansion of secretory capacity. Thus, the chemical reactivity and sterics of a unique residue in the endonuclease active site of IRE1 can be exploited by selective inhibitors to interfere with protein secretion in pathological settings.

Addressing accuracy and precision issues in iTRAQ quantitation.

iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variance-stabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variance-stabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions.

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Sub-cellular localization of membrane proteins.

In eukaryotes, numerous complex sub-cellular structures exist. The majority of these are delineated by membranes. Many proteins are trafficked to these in order to be able to carry out their correct physiological function. Assigning the sub-cellular location of a protein is of paramount importance to biologists in the elucidation of its role and in the refinement of knowledge of cellular processes by tracing certain activities to specific organelles. Membrane proteins are a key set of proteins as these form part of the boundary of the organelles and represent many important functions such as transporters, receptors, and trafficking. They are, however, some of the most challenging proteins to work with due to poor solubility, a wide concentration range within the cell and inaccessibility to many of the tools employed in proteomics studies. This review focuses on membrane proteins with particular emphasis on sub-cellular localization in terms of methodologies that can be used to determine the accurate location of membrane proteins to organelles. We also discuss what is known about the membrane protein cohorts of major organelles.

Quantitative proteomic approach to study subcellular localization of membrane proteins.

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.