Quantitative analysis of the detergent-insoluble brain proteome in frontotemporal lobar degeneration using SILAC internal standards

A hallmark of neurodegeneration is the aggregation of disease related proteins that are resistant to detergent extraction. In the major pathological subtype of frontotemporal lobar degeneration (FTLD), modified TAR-DNA binding protein 43 (TDP-43), including phosphorylated, ubiquitinated, and proteolytically cleaved forms, is enriched in detergent-insoluble fractions from post-mortem brain tissue. Additional proteins that accumulate in the detergent-insoluble FTLD brain proteome remain largely unknown. In this study, we used proteins from stable isotope-labeled (SILAC) human embryonic kidney 293 cells (HEK293) as internal standards for peptide quantitation across control and FTLD insoluble brain proteomes. Proteins were identified and quantified by liquid-chromatography coupled with tandem mass spectrometry (LC-MS/MS) and 21 proteins were determined to be enriched in FTLD using SILAC internal standards. In parallel, label-free quantification of only the unlabeled brain derived peptides by spectral counts (SC) and G-test analysis identified additional brain-specific proteins significantly enriched in disease. Several proteins determined to be enriched in FTLD using SILAC internal standards were not considered significant by G-test due to their low total number of SC. However, immunoblotting of FTLD and control samples confirmed enrichment of these proteins, highlighting the utility of SILAC internal standard to quantify low-abundance proteins in brain. Of these, the RNA binding protein PTB-associated splicing factor (PSF) was further characterized because of structural and functional similarities to TDP-43. Full-length PSF and shorter molecular weight fragments, likely resulting from proteolytic cleavage, were enriched in FTLD cases. Immunohistochemical analysis of PSF revealed predominately nuclear localization in control and FTLD brain tissue and was not associated with phosphorylated pathologic TDP-43 neuronal inclusions. However, in a subset of FTLD cases, PSF was aberrantly localized to the cytoplasm of oligodendrocytes. These data raise the possibility that PSF directed RNA processes in oligodendrocytes are altered in neurodegenerative disease.     

A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues.     

Comparison of pathway analysis approaches using lung cancer GWAS data sets

Pathway analysis has been proposed as a complement to single SNP analyses in GWAS. This study compared pathway analysis methods using two lung cancer GWAS data sets based on four studies: one a combined data set from Central Europe and Toronto (CETO); the other a combined data set from Germany and MD Anderson (GRMD). We searched the literature for pathway analysis methods that were widely used, representative of other methods, and had available software for performing analysis. We selected the programs EASE, which uses a modified Fishers Exact calculation to test for pathway associations, GenGen (a version of Gene Set Enrichment Analysis (GSEA)), which uses a Kolmogorov-Smirnov-like running sum statistic as the test statistic, and SLAT, which uses a p-value combination approach. We also included a modified version of the SUMSTAT method (mSUMSTAT), which tests for association by averaging χ² statistics from genotype association tests. There were nearly 18000 genes available for analysis, following mapping of more than 300,000 SNPs from each data set. These were mapped to 421 GO level 4 gene sets for pathway analysis. Among the methods designed to be robust to biases related to gene size and pathway SNP correlation (GenGen, mSUMSTAT and SLAT), the mSUMSTAT approach identified the most significant pathways (8 in CETO and 1 in GRMD). This included a highly plausible association for the acetylcholine receptor activity pathway in both CETO (FDR≤0.001) and GRMD (FDR = 0.009), although two strong association signals at a single gene cluster (CHRNA3-CHRNA5-CHRNB4) drive this result, complicating its interpretation. Few other replicated associations were found using any of these methods. Difficulty in replicating associations hindered our comparison, but results suggest mSUMSTAT has advantages over the other approaches, and may be a useful pathway analysis tool to use alongside other methods such as the commonly used GSEA (GenGen) approach.     

Chronobiological analysis of superimposed period data sets using bivariate cosine series models

The non-linear cosine fit (so-called cosinor method) of chronobiological sets is suited to one-dimensional effect versus time data. Extending the method to multiple components allows one to look for subharmonics or superimposed underlying periods. In more complex two-dimensional chronobiological time series, alternative models may be advantageous to visualise the time structure. Bivariate cosine series and the corresponding numerical surface discussion including first and second partial derivatives may be a suited method to characterise individual maxima or minima. Moreover, the method permits one to define a mean over two periods (bivariate MESOR) of a two-dimensional data set.     

Functional and quantitative proteomics using SILAC

Researchers in many biological areas now routinely characterize proteins by mass spectrometry. Among the many formats for quantitative proteomics, stable-isotope labelling by amino acids in cell culture (SILAC) has emerged as a simple and powerful one. SILAC removes false positives in protein-interaction studies, reveals large-scale kinetics of proteomes and - as a quantitative phosphoproteomics technology - directly uncovers important points in the signalling pathways that control cellular decisions.     

Integrating diverse genomic data using gene sets

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.     

Quantitative proteomic analysis of A549 cells infected with human respiratory syncytial virus subgroup B using SILAC coupled to LC-MS/MS

Human respiratory syncytial virus (HRSV) is a leading cause of serious lower respiratory tract infections in infants. The virus has two subgroups A and B, which differ in prevalence and (nucleotide) sequence. The interaction of subgroup A viruses with the host cell is relatively well characterized, whereas for subgroup B viruses it is not. Therefore quantitative proteomics was used to investigate the interaction of subgroup B viruses with A549 cells, a respiratory cell line. Changes in the cellular proteome and potential canonical pathways were determined using SILAC coupled to LC-MS/MS and Ingenuity Pathway Analysis. To reduce sample complexity and investigate potential trafficking both nuclear and cytoplasmic fractions were analyzed. A total of 904 cellular and six viral proteins were identified and quantified, of which 112 cellular proteins showed a twofold or more change in HRSV-infected cells. Data sets were validated using indirect immunofluorescence confocal microscopy on independent samples. Major changes were observed in constituents of mitochondria including components of the electron transport chain complexes and channels, as well as increases in the abundance of the products of interferon-stimulated genes. This is the first quantitative proteomic analysis of cells infected with HRSV-subgroup B.     

Mapping the integrin-linked kinase interactome using SILAC

Protein-protein interactions play an essential role in the regulation of vital biological functions. Through a network of interactions, integrin-linked kinase (ILK) functions downstream of integrin receptors to control cell spreading, migration, growth, survival, and cell cycle progression. Despite many reports on the role of ILK in the regulation of multiple signaling pathways, it is still not understood how ILK integrates and controls complex cellular signals. A more global analysis of ILK-protein complexes will give important insights in the complexity of ILK-dependent signal transduction. Here, we applied a SILAC (stable isotope labeling with amino acids in cell culture)-based proteomics approach to discover novel ILK-interacting proteins. Of 752 proteins identified in ILK immunoprecipitates, 24 proteins had SILAC ratios higher than PINCH, previously identified as direct ILK-binding partner. Some of the newly identified proteins specifically enriched in ILK immunoprecipitates, with potentially interesting roles in ILK biology, include rapamycin-insensitive companion of mTOR (Rictor), alpha- and beta-tubulin, RuvB-like 1 and 2, HS1-associating protein 1 (HAX-1), T-complex protein 1 subunits, and Ras-GTP-ase activating-like protein 1 (IQ-GAP1). Functional interactions between ILK and several of the new binding partners were confirmed by coimmunoprecipitation/Western blot and colocalization experiments. Detailed analysis showed that when ILK is found in a complex with alpha-tubulin and RuvB-like 1, alpha-parvin and PINCH are not present, suggesting that ILK has the ability to form distinct protein complexes throughout the cell. Inhibition of ILK activity with an ILK-kinase inhibitor QLT0267 or downregulation of its expression impaired the ability of RuvB-like 1 to bind to tubulin pointing toward a possible role of ILK in the regulation of RuvB-like 1/tubulin interaction. Using the power of quantitative proteomics to resolve specific from nonspecific protein interactions, we identified several novel ILK-binding proteins, which sheds light on the molecular mechanisms of regulation of ILK-dependent signal transduction.     

Integrated analysis of DNA copy number and gene expression microarray data using gene sets

Background  

Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes.     

Spectrum: spectral analysis of phylogenetic data

MOTIVATION: Spectrum is a new Macintosh and Microsoft Windows program designed to read in phylogenetic four-state or binary data in NEXUS format, and output the corresponding bipartition spectra. It can be used to find the tree whose expected spectrum is closest to the observed spectrum (the closest tree, Hendy, Discr, Math., 96, 51-58,. 1991).      

Automatic analysis of sleep using two parameters based on principal component analysis of electroencephalography spectral data

A computer program for the analysis of a sleep electroencephalogram (EEG) is presented. The method relies on two steps. First, a spectral analysis is performed for signals recorded from one or more electrode locations. Then, two EEG parameters are obtained by storing the spectral activity in a multidimensional space, whose dimension is reduced using principal component analysis (PCA) techniques. The main advantage of these parameters is in describing the process of sleep on a continuous scale as a function of time. Validation of the method was performed with the data collected from 16 subjects (8 young volunteers and 8 elderly insomniacs). Results snowed that the parameters correlate highly with the hypnograms established by conventional visual scoring. This signal parametrisation, however, offers more information regarding the time course of sleep, since small variations within individual sleep stages as well as smooth transitions between stages are assessed. Finally, the concurrent use of both parameters provides an original way of considering sleep as a dynamic process evolving cyclically in a single plane.     

A comparison of detrended correspondence analysis and principal co-ordinates analysis using four sets of Tasmanian vegetation data

Indirect gradient analysis, which entails the elucidation of relationships between trends in community composition and underlying environmental or successional gradients, is a major objective of ordination in plant ecology. Two ordination techniques, detrended correspondence analysis (DCA) and principal co-ordinates analysis (PCOA), were compared using three sets of Tasmanian vegetation data having known gradients and one set where the vegetation was expected to respond to diverse environmental variables. In every case, the results obtained by DCA were considered superior to, or at least as good as, those of PCOA. Hence, DCA appears to be the more suitable of the two methods for indirect gradient analysis.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

Statistical strategies for the analysis of massive data sets

The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.     

Genome-wide linkage analysis of systolic blood pressure slope using the Genetic Analysis Workshop 13 data sets

Systolic blood pressure (SBP) is an age-dependent complex trait for which both environmental and genetic factors may play a role in explaining variability among individuals. We performed a genome-wide scan of the rate of change in SBP over time on the Framingham Heart Study data and one randomly selected replicate of the simulated data from the Genetic Analysis Workshop 13. We used a variance-component model to carry out linkage analysis and a Markov chain Monte Carlo-based multiple imputation approach to recover missing information. Furthermore, we adopted two selection strategies along with the multiple imputation to deal with subjects taking antihypertensive treatment. The simulated data were used to compare these two strategies, to explore the effectiveness of the multiple imputation in recovering varying degrees of missing information, and its impact on linkage analysis results. For the Framingham data, the marker with the highest LOD score for SBP slope was found on chromosome 7. Interestingly, we found that SBP slopes were not heritable in males but were for females; the marker with the highest LOD score was found on chromosome 18. Using the simulated data, we found that handling treated subjects using the multiple imputation improved the linkage results. We conclude that multiple imputation is a promising approach in recovering missing information in longitudinal genetic studies and hence in improving subsequent linkage analyses.