We describe a generic strategy for determining the specific composition, changes in the composition, and changes in the abundance of protein complexes. It is based on the use of isotope-coded affinity tag (ICAT) reagents and mass spectrometry to compare the relative abundances of tryptic peptides derived from suitable pairs of purified or partially purified protein complexes. In a first application, the genuine protein components of a large RNA polymerase II (Pol II) preinitiation complex (PIC) were distinguished from a background of co-purifying proteins by comparing the relative abundances of peptides derived from a control sample and the specific complex that was purified from nuclear extracts by a single-step promoter DNA affinity procedure. In a second application, peptides derived from immunopurified STE12 protein complexes isolated from yeast cells in different states were used to detect quantitative changes in the abundance of the complexes, and to detect dynamic changes in the composition of the samples. The use of quantitative mass spectrometry to guide identification of specific complex components in partially purified samples, and to detect quantitative changes in the abundance and composition of protein complexes, provides the researcher with powerful new tools for the comprehensive analysis of macromolecular complexes.
Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.Fisher's method ͉ mixture distribution models S ystems biology (1, 2) aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins, metabolites, and organelles. In systems biology, one typically perturbs a system and, with highthroughput measurements to identify all pertinent elements and their interactions, integrates them into a biological network to understand the system's behavior. As such, systems biology is predicated on the integration of experimental data from an ever increasing number of technologies, such as gene expression arrays, proteomics, and chromatin immunoprecipitation on chip assays (3). Integration achieves one of the most important imperatives of systems biology, namely it reduces the dimensionality of global data to deliver useful information about the system of interest.A major challenge in systems biology is that technologies that globally interrogate biological systems have inherently high falsepositive and false-negative rates (4); thus, each data type alone has a limited utility. The integration of data from different sources provides an effective means to deal with this issue by reinforcing bona fide observations and reducing false negatives. Moreover, because different experimental technologies provide different insights into a system, the integration of multiple data types offers the greatest information about a particular cellular process. For example, gene perturbation experiments (e.g., knockouts or RNA interference) reveal relationships between genes that may imply direct physical interactions or indirect logical interactions. In contrast, chromatin immunoprecipitation chip data can reveal direct protein-DNA interactions or cofacto...
The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.metabolism ͉ yeast ͉ molecular network model ͉ galactose S ystems biology aims to understand the dynamic behavior of molecular networks in the context of the global cell, organ and organism state by exploiting (i) high-throughput interrogation technologies; (ii) increasingly comprehensive databases of biomolecules and their interactions; and (iii) computational predictions of molecular function and interaction ( Fig. 1). Use of each of these sources of information has its own drawbacks (1). For example, many current global assays of mRNA and protein abundance͞state are systematically biased toward more abundant species and measure only the average content of many thousands of cells. Global assays are also inherently noisy and include significant numbers of false positives and false negatives. Databases tend to combine data from different cell types, different strains of an organism, and different experimental conditions. Moreover, well studied molecules and pathways are systematically overrepresented in databases. As a result, integration of database information with a particular set of experimental data can introduce systematic biases into the modelbuilding process. Likewise, computer predictions tend to be more accurate for members of well characterized molecular families. Therefore, there is a pressing need for data integration methodologies that effectively address both random noise and systematic bias in data.In a companion paper (2), we present a data integration methodology and its software implementation to address these challenges. To present the methodology clearly, we used only simulated data in that paper. Here, we present the application of our methodology (named Pointillist) to 18 types of biological data, which we integrate to arrive at a detailed and comprehensive picture of galactose utilization in yeast. The data we integrate include in...
A yeast tRNA three-hybrid interaction approach and an in vivo nuclear tRNA export assay based on amber suppression was used to identify proteins that participate in the nuclear tRNA export process in Saccharomyces cerevisiae. One of the proteins identified by this strategy is Utp8p, an essential 80-kDa nucleolar protein that has been implicated in 18 S ribosomal RNA biogenesis. Our characterization indicated that the major function of Utp8p is in nuclear tRNA export. Like the S. cerevisiae Los1p and the mammalian exportin-t, which are proteins known to facilitate nuclear tRNA export, overexpression of Utp8p restored export of tRNA am Tyr mutants defective in nuclear export. Furthermore, depletion of Utp8p blocked nuclear export of mature tRNAs derived from both intronless and introncontaining pre-tRNAs but did not affect tRNA and rRNA maturation, nuclear export of mRNA and ribosomes, or nuclear tRNA aminoacylation. Overexpression of Utp8p also alleviated nuclear retention of non-aminoacylated tRNA Tyr in a tyrosyl-tRNA synthetase mutant strain. Utp8p binds tRNA directly and saturably, indicating that it has a tRNA-binding site. Utp8p does not appear to function as a tRNA export receptor, because it does not shuttle between the nucleus and the cytoplasm. Taken together, the results suggest that Utp8p is an essential intranuclear component of the nuclear tRNA export machinery, which may channel tRNA to the various tRNA export pathways operating in S. cerevisiae.Translocation of macromolecules between the nucleus and cytoplasm occurs through the nuclear pore complex (NPC)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.