An automatically produced Thesaurus

A procedure for the computer generation of a thesaurus from a set of descriptors, manually assigned to the documents in a library, is described. Recognises only a quasi-associative relationship among desciptors. the specific advantages of the thesaurus as an open-ended one, the keywords derived from actual documents being the most helpful in retrieving the documents, and as an aid in information search in the collection are pointed out. Computational procedures for generating the thesaurus inclue keyword statistics, matrix inversion, calculation of similarity matrix using Tanimoto coefficient, automatic cluster analysis using minmal-tree procedure, and compilation of groups and main groups of descriptors are given. An algorithm for the graphic display of the main groups of descriptors has been formulated. The main disadvantage of the procedure is that only a limited number of keywords can be processed within a reasonable computer CPU time. Points out that the procedure can be applied to a library of 10,000 to 20,000 documents with a keyword base of 1,000 words using about 3 hours of computing time

Quantitative proteome profiling of lymph node-positive<i>vs</i>. -negative colorectal carcinomas pinpoints MX1 as a marker for lymph node metastasis

We used high-resolution mass spectrometry to measure the abundance of more than 9,000 proteins in 19 individually dissected colorectal tumors representing lymph node metastatic (n = 10) and nonmetastatic (n = 9) phenotypes. Statistical analysis identified MX1 and several other proteins as overexpressed in lymph node-positive tumors. MX1, IGF1-R and IRF2BP1 showed significantly different expression in immunohistochemical validation (Wilcoxon test p = 0.007 for IGF1-R, p = 0.04 for IRF2BP1 and p = 0.02 for MX1 at the invasion front) in the validation cohort. Knockout of MX1 by siRNA in cell cultures and wound healing assays provided additional evidence for the involvement of this protein in tumor invasion. The collection of identified and quantified proteins to our knowledge is the largest tumor proteome dataset available at the present. The identified proteins can give insights into the mechanisms of lymphatic metastasis in colorectal carcinoma and may act as prognostic markers and therapeutic targets after further prospective validation

Whole cell proteome regulation by microRNAs captured in a pulsed SILAC mass spectrometry approach

Since gene expression is controlled on many different levels in a cell, capturing a comprehensive snapshot of all regulatory processes is a difficult task. One possibility to monitor effective changes within a cell is to directly quantify changes in protein synthesis, which reflects the accumulative impact of regulatory mechanisms on gene expression. Pulsed stable isotope labeling by amino acids in cell culture (pSILAC) has been shown to be a viable method to investigate de novo protein synthesis on a proteome-wide scale (Schwanhausser et al., Proteomics 9:205-209, 2009; Selbach et al., Nature 455:58-63, 2008). One application of pSILAC is to study the regulation of protein expression by microRNAs. Here, we describe how pSILAC in conjunction with shotgun mass spectrometry can assess differences in the protein profile between cells transfected with a microRNA and non-transfected cells

Mathematical modelling suggests differential impact of β-TrCP paralogues on Wnt/β-catenin signalling dynamics

The Wnt/β-catenin signalling pathway is involved in the regulation of a multitude of cellular processes by controlling the concentration of the transcriptional regulator β-catenin. Proteasomal degradation of β-catenin is mediated by the two β-transducin repeat-containing protein (β-TrCP) paralogues HOS and FWD1, which are functionally interchangeable and thereby considered to function redundantly in the pathway. HOS and FWD1 are both regulated by Wnt/β-catenin signalling, albeit in opposite directions, thus establishing interlocked negative and positive feedback loops. The functional relevance of the opposite regulation of HOS and FWD1 by Wnt/β-catenin signalling in conjunction with their redundant activities in proteasomal degradation of β-catenin is an unresolved issue. Using a detailed ordinary differential equation (ODE) model, we investigated the specific influence of each individual feedback mechanism and their combination on Wnt/β-catenin signal transduction under wild type and cancerous conditions. We found that under wild type conditions the signalling dynamics are predominantly affected by the HOS feedback due to a higher concentration of HOS than FWD1. Transcriptional up-regulation of FWD1 by other signalling pathways reduced the impact of the HOS feedback. The opposite regulation of HOS and FWD1 expression by Wnt/β-catenin signalling allows employing the FWD1 feedback as a compensation mechanism against aberrant pathway activation due to reduced HOS concentration. In contrast, the FWD1 feedback provides no protection against aberrant activation in APC mutant cancer cells

Micro-proteomics with iterative data analysis:proteome analysis in <i>C.elegans</i> at the single worm level

Proteomics studies typically analyze proteins at a population level, using extracts prepared from tens of thousands to millions of cells. The resulting measurements correspond to average values across the cell population and can mask considerable variation in protein expression and function between individual cells or organisms. Here, we report the development of micro-proteomics for the analysis of Caenorhabditis elegans, a eukaryote composed of 959 somatic cells and approximate to 1500 germ cells, measuring the worm proteome at a single organism level to a depth of approximate to 3000 proteins. This includes detection of proteins across a wide dynamic range of expression levels (&gt;6 orders of magnitude), including many chromatin-associated factors involved in chromosome structure and gene regulation. We apply the micro-proteomics workflow to measure the global proteome response to heat-shock in individual nematodes. This shows variation between individual animals in the magnitude of proteome response following heat-shock, including variable induction of heat-shock proteins. The micro-proteomics pipeline thus facilitates the investigation of stochastic variation in protein expression between individuals within an isogenic population of C. elegans. All data described in this study are available online via the Encyclopedia of Proteome Dynamics (), an open access, searchable database resource

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Newt-omics: a comprehensive repository for omics data from the newt Notophthalmus viridescens

Notophthalmus viridescens, a member of the salamander family is an excellent model organism to study regenerative processes due to its unique ability to replace lost appendages and to repair internal organs. Molecular insights into regenerative events have been severely hampered by the lack of genomic, transcriptomic and proteomic data, as well as an appropriate database to store such novel information. Here, we describe ‘Newt-omics’ (http://newt-omics.mpi-bn.mpg.de), a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centred database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ∼50 000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13 810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise

Proteomics: Challenges, Techniques and Possibilities to Overcome Biological Sample Complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000–16,000 molecules per cell and that differences in mRNA expression between genes explain only 10–40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 9% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 = 0.14). Based on this, our second strategy suggests that mRNA levels explain ∼84% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed