Large-scale mapping of human protein–protein interactions by mass spectrometry

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations

Thermal Methods for the Analysis of RNA Folding Pathways

Once a model of the secondary structure of an RNA has been deduced, thermal melting analysis can be used to determine whether the model accounts for all intramolecular interactions of the RNA, or whether noncanonical and tertiary interactions make the structure more stable than predicted, or link parts of the structure in unexpected ways. It is also useful to determine the pH, salt, and temperature ranges under which the RNA adopts a stably folded structure, or to analyze unfolding pathways. This unit discusses sample preparation, instrumentation, and theoretical background. It also provide a sample analysis of tRNA unfolding.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153198/1/cpnc1103.pd

TOGA

Analysis of TOGA gene projection

GENOMIC AND TRANSCRIPTOME PROFILING OF EPITHELIAL OVARIAN CANCER

Background: Epithelial ovarian cancer is the leading cause of death by gynecological malignancy. Due to inadequate screening modalities, a lack of characteristic presenting symptoms, limited treatments and a poor understanding of the molecular underpinnings of the disease, only 25% of ovarian cancers are diagnosed at an early stage. Current 5-year survival rates range from 80%, for disease diagnosed in Stage I to as low as 13% for Stage IV. Current screening for ovarian cancer involves measuring CA-125 levels. However, CA-125 testing has low sensitivity since it can be elevated in a variety of other gynecological diseases. Numerous studies have found molecular heterogeneity between the four histological subtypes of ovarian cancer (serous, endometrioid, clear cell and mucinous). However, treatments remain the same for all subtypes regardless of molecular heterogeneity. Thus, better treatment targets and biomarkers must be found for this disease. Methods:In our study 300 ovarian tumors will be genomically profiled using the Affymetrix Genome-Wide SNP Array 6.0 to identify loci and genes implicated in ovarian cancer. To date, 51 ovarian tumors have been analyzed using the SNP array. Results:Preliminary analysis of copy number variation in these tumors using Partek software has revealed a total of 978 loci. Known amplifications derived from the literature were seen at CCNE1 and ERBB2. Similarly, well known deletions ofp53 and RB1 in ovarian cancer were detected. Novel amplified loci at 18q11.2 and 4q33 were also detected. Novel deletions were detected at 7p13 and 8q22.2. Conclusion: Future work will include running the remaining 249 tumor samples on the SNP array and analyzing the complete dataset using Partek software. Future validation of identified genes in vitro and in vivo may provide insight and possible biomarkers that may be used clinically to benefit the ovarian cancer patient

Differential analysis of membrane proteins in mouse fore- and hindbrain using a label-free approach

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance

Differential analysis of membrane proteins in mouse fore- and hindbrain using a label-free approach

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance

Global Gene Expression Patterns in Clostridium thermocellum as Determined by Microarray Analysis of Chemostat Cultures on Cellulose or Cellobiose▿ †

A microarray study of chemostat growth on insoluble cellulose or soluble cellobiose has provided substantial new information on Clostridium thermocellum gene expression. This is the first comprehensive examination of gene expression in C. thermocellum under defined growth conditions. Expression was detected from 2,846 of 3,189 genes, and regression analysis revealed 348 genes whose changes in expression patterns were growth rate and/or substrate dependent. Successfully modeled genes included those for scaffoldin and cellulosomal enzymes, intracellular metabolic enzymes, transcriptional regulators, sigma factors, signal transducers, transporters, and hypothetical proteins. Unique genes encoding glycolytic pathway and ethanol fermentation enzymes expressed at high levels simultaneously with previously established maximal ethanol production were also identified. Ranking of normalized expression intensities revealed significant changes in transcriptional levels of these genes. The pattern of expression of transcriptional regulators, sigma factors, and signal transducers indicates that response to growth rate is the dominant global mechanism used for control of gene expression in C. thermocellum

Design and analysis of quantitative differential proteomics investigations using LC-MS technology

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/