Michelle S. Scott

Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

A Quantitative Spatial Proteomics Analysis of Proteome Turnover in Human Cells

Measuring the properties of endogenous cell proteins, such as expression level, subcellular localization, and turnover rates, on a whole proteome level remains a major challenge in the postgenome era. Quantitative methods for measuring mRNA expression do not reliably predict corresponding protein levels and provide little or no information on other protein properties. Here we describe a combined pulse-labeling, spatial proteomics and data analysis strategy to characterize the expression, localization, synthesis, degradation, and turnover rates of endogenously expressed, untagged human proteins in different subcellular compartments. Using quantitative mass spectrometry and stable isotope labeling with amino acids in cell culture, a total of 80,098 peptides from 8,041 HeLa proteins were quantified, and their spatial distribution between the cytoplasm, nucleus and nucleolus determined and visualized using specialized software tools developed in PepTracker. Using information from ion intensities and rates of change in isotope ratios, protein abundance levels and protein synthesis, degradation and turnover rates were calculated for the whole cell and for the respective cytoplasmic, nuclear, and nucleolar compartments. Expression levels of endogenous HeLa proteins varied by up to seven orders of magnitude. The average turnover rate for HeLa proteins was ∼20 h. Turnover rate did not correlate with either molecular weight or net charge, but did correlate with abundance, with highly abundant proteins showing longer than average half-lives. Fast turnover proteins had overall a higher frequency of PEST motifs than slow turnover proteins but no general correlation was observed between amino or carboxyl terminal amino acid identities and turnover rates. A subset of proteins was identified that exist in pools with different turnover rates depending on their subcellular localization. This strongly correlated with subunits of large, multiprotein complexes, suggesting a general mechanism whereby their assembly is controlled in a different subcellular location to their main site of function.

PIPs: human protein–protein interaction prediction database

The PIPs database (http://www.compbio.dundee.ac.uk/www-pips) is a resource for studying protein–protein interactions in human. It contains predictions of >37 000 high probability interactions of which >34 000 are not reported in the interaction databases HPRD, BIND, DIP or OPHID. The interactions in PIPs were calculated by a Bayesian method that combines information from expression, orthology, domain co-occurrence, post-translational modifications and sub-cellular location. The predictions also take account of the topology of the predicted interaction network. The web interface to PIPs ranks predictions according to their likelihood of interaction broken down by the contribution from each information source and with easy access to the evidence that supports each prediction. Where data exists in OPHID, HPRD, DIP or BIND for a protein pair this is also reported in the output tables returned by a search. A network browser is included to allow convenient browsing of the interaction network for any protein in the database. The PIPs database provides a new resource on protein–protein interactions in human that is straightforward to browse, or can be exploited completely, for interaction network modelling.

Human miRNA Precursors with Box H/ACA snoRNA Features

MicroRNAs (miRNAs) and small nucleolar RNAs (snoRNAs) are two classes of small non-coding regulatory RNAs, which have been much investigated in recent years. While their respective functions in the cell are distinct, they share interesting genomic similarities, and recent sequencing projects have identified processed forms of snoRNAs that resemble miRNAs. Here, we investigate a possible evolutionary relationship between miRNAs and box H/ACA snoRNAs. A comparison of the genomic locations of reported miRNAs and snoRNAs reveals an overlap of specific members of these classes. To test the hypothesis that some miRNAs might have evolved from snoRNA encoding genomic regions, reported miRNA-encoding regions were scanned for the presence of box H/ACA snoRNA features. Twenty miRNA precursors show significant similarity to H/ACA snoRNAs as predicted by snoGPS. These include molecules predicted to target known ribosomal RNA pseudouridylation sites in vivo for which no guide snoRNA has yet been reported. The predicted folded structures of these twenty H/ACA snoRNA-like miRNA precursors reveal molecules which resemble the structures of known box H/ACA snoRNAs. The genomic regions surrounding these predicted snoRNA-like miRNAs are often similar to regions around snoRNA retroposons, including the presence of transposable elements, target site duplications and poly (A) tails. We further show that the precursors of five H/ACA snoRNA-like miRNAs (miR-151, miR-605, mir-664, miR-215 and miR-140) bind to dyskerin, a specific protein component of functional box H/ACA small nucleolar ribonucleoprotein complexes suggesting that these molecules have retained some H/ACA snoRNA functionality. The detection of small RNA molecules that share features of miRNAs and snoRNAs suggest that these classes of RNA may have an evolutionary relationship.

Protection of p27Kip1 mRNA by quaking RNA binding proteins promotes oligodendrocyte differentiation

The quaking (Qk) locus expresses a family of RNA binding proteins, and the expression of several alternatively spliced isoforms coincides with the development of oligodendrocytes and the onset of myelination. Quaking viable (Qkv) mice harboring an autosomal recessive mutation in this locus have uncompacted myelin in the central nervous system owing to the inability of oligodendrocytes to properly mature. Here we show that the expression of two QKI isoforms, absent from oligodendrocytes of Qkv mice, induces cell cycle arrest of primary rat oligodendrocyte progenitor cells and differentiation into oligodendrocytes. Injection of retroviruses expressing QKI into the telencephalon of mouse embryos induced differentiation and migration of multipotential neural progenitor cells into mature oligodendrocytes localized in the corpus callosum. The mRNA encoding the cyclin-dependent kinase (CDK)-inhibitor p27Kip1 was bound and stabilized by QKI, leading to an increased accumulation of p27Kip1 protein in oligodendrocytes. Our findings demonstrate that QKI is upstream of p27Kip1 during oligodendrocyte differentiation.

From snoRNA to miRNA: Dual function regulatory non-coding RNAs

Small nucleolar RNAs (snoRNAs) are an ancient class of small non-coding RNAs present in all eukaryotes and a subset of archaea that carry out a fundamental role in the modification and processing of ribosomal RNA. In recent years, however, a large proportion of snoRNAs have been found to be further processed into smaller molecules, some of which display different functionality. In parallel, several studies have uncovered extensive similarities between snoRNAs and other types of small non-coding RNAs, and in particular microRNAs. Here, we explore the extent of the relationship between these types of non-coding RNA and the possible underlying evolutionary forces that shaped this subset of the current non-coding RNA landscape.

Human ING1 Proteins Differentially Regulate Histone Acetylation

ING1 proteins are nuclear, growth inhibitory, and regulate apoptosis in different experimental systems. Here we show that similar to their yeast homologs, human ING1 proteins interact with proteins associated with histone acetyltransferase (HAT) activity, such as TRRAP, PCAF, CBP, and p300. Human ING1 immunocomplexes contain HAT activity, and overexpression of p33ING1b, but not of p47ING1a, induces hyperacetylation of histones H3 and H4, in vitro and in vivo at the single cell level. p47ING1a inhibits histone acetylation in vitro and in vivo and binds the histone deacetylase HDAC1. Finally, we present evidence indicating that p33ING1b affects the degree of physical association between proliferating cell nuclear antigen (PCNA) and p300, an association that has been proposed to link DNA repair to chromatin remodeling. Together with the finding that human ING1 proteins bind PCNA in a DNA damage-dependent manner, these data suggest that ING1 proteins provide a direct linkage between DNA repair, apoptosis, and chromatin remodeling via multiple HAT·ING1·PCNA protein complexes.

Identification of human miRNA precursors that resemble box C/D snoRNAs

There are two main classes of small nucleolar RNAs (snoRNAs): the box C/D snoRNAs and the box H/ACA snoRNAs that function as guide RNAs to direct sequence-specific modification of rRNA precursors and other nucleolar RNA targets. A previous computational and biochemical analysis revealed a possible evolutionary relationship between miRNA precursors and some box H/ACA snoRNAs. Here, we investigate a similar evolutionary relationship between a subset of miRNA precursors and box C/D snoRNAs. Computational analyses identified 84 intronic miRNAs that are encoded within either box C/D snoRNAs, or in precursors showing similarity to box C/D snoRNAs. Predictions of the folded structures of these box C/D snoRNA-like miRNA precursors resemble the structures of known box C/D snoRNAs, with the boxes C and D often in close proximity in the folded molecule. All five box C/D snoRNA-like miRNA precursors tested (miR-27b, miR-16-1, mir-28, miR-31 and let-7g) bind to fibrillarin, a specific protein component of functional box C/D snoRNP complexes. The data suggest that a subset of small regulatory RNAs may have evolved from box C/D snoRNAs.

Probabilistic prediction and ranking of human protein-protein interactions

BackgroundAlthough the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.ResultsThe prediction of human protein-protein interactions was investigated by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. Using a conservative threshold, our most accurate predictor identifies 37606 human interactions, 32892 (80%) of which are not present in other publicly available large human interaction datasets, thus substantially increasing the coverage of the human interaction map. A subset of the 32892 novel predicted interactions have been independently validated. Comparison of the prediction dataset to other available human interaction datasets estimates the false positive rate of the new method to be below 80% which is competitive with other methods. Since the new method scores and ranks all human protein pairs, smaller subsets of higher quality can be generated thus leading to even lower false positive prediction rates.ConclusionThe set of interactions predicted in this work increases the coverage of the human interaction map and will help determine the highest confidence human interactions.

Characterization and prediction of protein nucleolar localization sequences

Although the nucleolar localization of proteins is often believed to be mediated primarily by non-specific retention to core nucleolar components, many examples of short nucleolar targeting sequences have been reported in recent years. In this article, 46 human nucleolar localization sequences (NoLSs) were collated from the literature and subjected to statistical analysis. Of the residues in these NoLSs 48% are basic, whereas 99% of the residues are predicted to be solvent-accessible with 42% in α-helix and 57% in coil. The sequence and predicted protein secondary structure of the 46 NoLSs were used to train an artificial neural network to identify NoLSs. At a true positive rate of 54%, the predictor’s overall false positive rate (FPR) is estimated to be 1.52%, which can be broken down to FPRs of 0.26% for randomly chosen cytoplasmic sequences, 0.80% for randomly chosen nucleoplasmic sequences and 12% for nuclear localization signals. The predictor was used to predict NoLSs in the complete human proteome and 10 of the highest scoring previously unknown NoLSs were experimentally confirmed. NoLSs are a prevalent type of targeting motif that is distinct from nuclear localization signals and that can be computationally predicted.