Isabel Riba-Garcia

Dynamics of Protein Turnover, a Missing Dimension in Proteomics

Functional genomic experiments frequently involve a comparison of the levels of gene expression between two or more genetic, developmental, or physiological states. Such comparisons can be carried out at either the RNA (transcriptome) or protein (proteome) level, but there is often a lack of congruence between parallel analyses using these two approaches. To fully interpret protein abundance data from proteomic experiments, it is necessary to understand the contributions made by the opposing processes of synthesis and degradation to the transition between the states compared. Thus, there is a need for reliable methods to determine the rates of turnover of individual proteins at amounts comparable to those obtained in proteomic experiments. Here, we show that stable isotope-labeled amino acids can be used to define the rate of breakdown of individual proteins by inspection of mass shifts in tryptic fragments. The approach has been applied to an analysis of abundant proteins in glucose-limited yeast cells grown in aerobic chemostat culture at steady state. The average rate of degradation of 50 proteins was 2.2%/h, although some proteins were turned over at imperceptible rates, and others had degradation rates of almost 10%/h. This range of values suggests that protein turnover is a significant missing dimension in proteomic experiments and needs to be considered when assessing protein abundance data and comparing it to the relative abundance of cognate mRNA species.

A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

Evidence for Structural Variants of a- and b-Type Peptide Fragment Ions Using Combined Ion Mobility/Mass Spectrometry

Tandem mass spectrometry (MS/MS) of peptides plays a key role in the field of proteomics, and an understanding of the fragmentation mechanisms involved is vital for data interpretation. Not all the fragment ions observed by low-energy collision-induced dissociation of protonated peptides are readily explained by the generally accepted structures for a- and b-ions. The possibility of a macrocyclic structure for b-type ions has been recently proposed. In this study, we have undertaken investigations of linear protonated YAGFL-NH2, N-acetylated-YAGFL-NH2, and cyclo-(YAGFL) peptides and their fragments using a combination of ion mobility (IM) separation and mass spectrometry. The use of IM in this work both gives insight into relative structural forms of the ion species and crucial separation of isobaric species. Our study provides compelling evidence for the formation of a stable macrocyclic structure for the b5 ion generated by fragmentation of protonated linear YAGFL-NH2. Additionally we demonstrate that the a4 ion fragment of protonated YAGFL-NH2 has at least two structures; one of which is attributable to a macrocyclic structure on the basis of its subsequent fragmentation. More generally, this work emphasizes the value of combined IM-MS/MS in probing the detailed fragmentation mechanisms of peptide ions, and illustrates the use of combined ion mobility/collisional activation/mass spectrometry analysis in achieving an effective enhancement of the resolution of the mobility separator.

Stable isotope labelling in vivo as an aid to protein identification in peptide mass fingerprinting.

Peptide mass fingerprinting (PMF) is a powerful technique for identification of proteins derived from in‐gel digests by virtue of their matrix‐assisted laser desorption/ionization‐time of flight mass spectra. However, there are circumstances where the under‐representation of peptides in the mass spectrum and the complexity of the source proteome mean that PMF is inadequate as an identification tool. In this paper, we show that identification is substantially enhanced by inclusion of composition data for a single amino acid. Labelling in vivo with a stable isotope labelled amino acid (in this paper, decadeuterated leucine) identifies the number of such amino acids in each digest fragment, and show a considerable gain in the ability of PMF to identify the parent protein. The method is tolerant to the extent of labelling, and as such, may be applicable to a range of single cell systems.

Genome-wide analysis of the effects of heat shock on a Saccharomyces cerevisiae mutant with a constitutively activated cAMP-dependent pathway

We have used DNA microarray technology and 2-D gel electrophoresis combined with mass spectrometry to investigate the effects of a drastic heat shock from 30℃ to 50℃ on a genome-wide scale. This experimental condition is used to differentiate between wild-type cells and those with a constitutively active cAMP-dependent pathway in Saccharomyces cerevisiae. Whilst more than 50% of the former survive this shock, almost all of the latter lose viability. We compared the transcriptomes of the wildtype and a mutant strain deleted for the gene PDE2, encoding the high-affinity cAMP phosphodiesterase before and after heat shock treatment. We also compared the two heat-shocked samples with one another, allowing us to determine the changes that occur in the pde2Δ mutant which cause such a dramatic loss of viability after heat shock. Several genes involved in ergosterol biosynthesis and carbon source utilization had altered expression levels, suggesting that these processes might be potential factors in heat shock survival. These predictions and also the effect of the different phases of the cell cycle were confirmed by biochemical and phenotypic analyses. 146 genes of previously unknown function were identified amongst the genes with altered expression levels and deletion mutants in 13 of these genes were found to be highly sensitive to heat shock. Differences in response to heat shock were also observed at the level of the proteome, with a higher level of protein degradation in the mutant, as revealed by comparing 2-D gels of wild-type and mutant heat-shocked samples and mass spectrometry analysis of the differentially produced proteins.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high‐throughput LC‐MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data‐dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC‐MS data is modelled as a 2D surface through selection of a sparse set of weighted B‐spline basis functions from an over‐complete dictionary. By ordering and spatially partitioning the weights with an R‐tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open‐source software is available from http://seamass.net/viz/.

Regional protein expression in human Alzheimer's brain correlates with disease severity

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen both the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions show varying and region-specific changes in protein expression. These changes provide novel insights into the progression of disease, novel AD-related pathways, the presence of a ‘gradient’ of protein expression change from less to more affected regions, and the presence of a ‘protective’ protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and opens new avenues of interest to enhance our understanding of molecular pathophysiology of AD, provides new targets for intervention and broadens the conceptual frameworks for future AD research.

A new paradigm for clinical biomarker discovery and screening with Mass Spectrometry through biomedical image analysis principles

Biomarker discovery in amenably sampled body fluids has the potential to empower clinical screening programs for the early detection of disease. Liquid Chromatography interfaced to Mass Spectrometry (LC-MS) has emerged as a central technique for sensitive and automated analysis of proteins and metabolites from these clinical samples. However, the potential of LC-MS as a precise and reliable platform for discovery and screening is dependent on robust, sensitive and specific signal extraction and interpretation. The output of LC-MS is formed as a set of quantifiable images containing thousands of biochemical signals regulated in disease and treatment. We propose to tackle this problem for the first time with a biomedical image analysis paradigm. A novel workflow of image reconstruction, groupwise image registration and Bayesian functional mixed-effects modeling is presented. Poisson counting noise and lognormal biological variation are modeled in the raw image domain, resulting in markedly improved detection limit for differential analysis.