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1.  The Proteomic Response to Mutants of the Escherichia coli RNA Degradosome 
Molecular bioSystems  2013;9(4):750-757.
The Escherichia coli RNA degradosome recognizes and degrades RNA through the coordination of four main protein components, the endonuclease RNase E, the exonuclease PNPase, the RhlB helicase and the metabolic enzyme enolase. To help our understanding of the functions of the RNA degradosome, we quantified expression changes of >2,300 proteins by mass spectrometry based shotgun proteomics in E. coli strains deficient in rhlB, eno, pnp (which displays temperature sensitive growth), or rne(1-602) which encodes a C-terminal truncation mutant of RNaseE and is deficient in degradosome assembly. Global protein expression changes are most similar between the pnp and rhlB mutants, confirming the functional relationship between the genes. We observe down-regulation of protein chaperones including GroEL and DnaK (which associate with the degradosome), a decrease in translation related proteins in Δpnp, ΔrhlB and rne(1-602) cells, and a significant increase in the abundance of aminoacyl-tRNA synthetases. Analysis of the observed proteomic changes point to a shared motif, CGCTGG, that may be associated with RNA degradosome targets. Further, our data provide information on the expression modulation of known degradosome-associated proteins, such as DeaD and RNase G, as well as other RNA helicases and RNases – suggesting or confirming functional complementarity in some cases. Taken together, our results emphasize the role of the RNA degradosome in the modulation of the bacterial proteome and provide the first large-scale proteomic description of the response to perturbation of this major pathway of RNA degradation.
PMCID: PMC3709862  PMID: 23403814
2.  Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry 
Proteomic analysis of T cells emerging from quiescence identifies dynamic network-level changes in key cellular processes. Disruption of two such processes, ribosome biogenesis and RNA splicing, reveals that the programs controlling cell growth and cell-cycle entry are separable.
The authors conduct a proteomic and protein interaction network analysis of human T lymphocytes during entry into the first cell cycle.Inhibiting the induction of eIF6 (60S ribosome biogenesis) causes T cells to enter the cell cycle without growing in size.Inhibiting the induction of SF3B2/SF3B4 (U2/U12-dependent RNA splicing) allows an increase in cell size without entering the cell cycle.These results provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells.
Regulating the transition of cells such as T lymphocytes from quiescence (G0) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G0. We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G0 into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle.
PMCID: PMC3321526  PMID: 22415777
cell cycle; cell size; mass spectrometry; proteomics; T cells
3.  Integrating shotgun proteomics and mRNA expression data to improve protein identification 
Bioinformatics  2009;25(11):1397-1403.
Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.
Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19–63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.
Availability and Implementation: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from
Supplementary Information: Supplementary data website:
PMCID: PMC2682515  PMID: 19318424
4.  The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results 
BMC Bioinformatics  2008;9:529.
Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called Oi value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (Oi). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances.
The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis.
The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.
PMCID: PMC2639435  PMID: 19068132

Results 1-4 (4)