Mass spectrometry based approaches are commonly used to identify proteins from multiprotein complexes, typically with the goal of identifying new complex members or identifying post translational modifications. However, with the recent demonstration that spectral counting is a powerful quantitative proteomic approach, the analysis of multiprotein complexes by mass spectrometry can be reconsidered in certain cases. Using the chromatography based approach named multidimensional protein identification technology, multiprotein complexes may be analyzed quantitatively using the normalized spectral abundance factor that allows comparison of multiple independent analyses of samples. This study describes an approach to visualize multiprotein complex datasets that provides structure function information that is superior to tabular lists of data. In this method review, we describe a reanalysis of the Rpd3/Sin3 small and large histone deacetylase complexes previously described in a tabular form to demonstrate the normalized spectral abundance factor approach.

If the large collection of microarray-specific statistical tools was applicable to the analysis of quantitative shotgun proteomics datasets, it would certainly foster an important advancement of proteomics research. Here we analyze two large multidimensional protein identification technology datasets, one containing eight replicates of the soluble fraction of a yeast whole-cell lysate and one containing nine replicates of a human immunoprecipitate, to test whether normalized spectral abundance factor (NSAF) values share substantially similar statistical properties with transcript abundance values from Affymetrix GeneChip data. First we show similar dynamic range and distribution properties of these two types of numeric values. Next we show that the standard deviation (S.D.) of a protein's NSAF values was dependent on the average NSAF value of the protein itself, following a power law. This relationship can be modeled by a power law global error model (PLGEM), initially developed to describe the variance-versus-mean dependence that exists in GeneChip data. PLGEM parameters obtained from NSAF datasets proved to be surprisingly similar to the typical parameters observed in GeneChip datasets. The most important common feature identified by this approach was that, although in absolute terms the S.D. of replicated abundance values increases as a function of increasing average abundance, the coefficient of variation, a relative measure of variability, becomes progressively smaller under the same conditions. We next show that PLGEM parameters were reasonably stable to decreasing numbers of replicates. We finally illustrate one possible application of PLGEM in the identification of differentially abundant proteins that might potentially outperform standard statistical tests. In summary, we believe that this body of work lays the foundation for the application of microarray-specific tools in the analysis of NSAF datasets.

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SummaryIn eukaryotes, mRNA export involves many evolutionarily conserved factors that carry the nascent transcript to the nuclear pore complex (NPC). The THO/TREX complex couples transcription to mRNA export and recruits the mRNA export receptor NXF1 for the transport of messenger ribonucleoprotein particles (mRNP) to the NPC. The transcription and export complex 2 (TREX-2) was suggested to interact with NXF1 and to shuttle between transcription sites and the NPC. Here, we characterize the dynamics of human TREX-2 and show that it stably associates with the NPC basket. Moreover, the association of TREX-2 with the NPC requires the basket nucleoporins NUP153 and TPR, but is independent of transcription. Differential profiles of mRNA nuclear accumulation reveal that TREX-2 functions similarly to basket nucleoporins, but differently from NXF1. Thus, our results show that TREX-2 is an NPCassociated complex in mammalian cells and suggest that it is involved in putative NPC basket-related functions.