Ruihua Fang

Modification of Host Lipid Raft Proteome upon Hepatitis C Virus Replication

Hepatitis C virus (HCV) replication complex resides in detergent-insoluble subcellular domains or lipid rafts. We used two proteomics approaches to characterize the protein content of lipid rafts isolated from Huh7 cells and its modification upon HCV replication. Using two-dimensional electrophoresis and mass spectrometry, we identified ∼100 protein spots in the isolated lipid rafts; among them, 39 were reproducibly modified in HCV replicon cell lines as compared with control cell lines. We also used stable isotope labeling by amino acids in cell culture (SILAC) combined with one-dimensional electrophoresis separation and mass spectrometry. Using this approach, we identified 1036 individual proteins based on peptides selected with at least 95% confidence; among them, 413 proteins were identified with at least two peptides. Quantification analysis identified 150 proteins modified by at least 2.5-fold (110 up-regulated and 40 down-regulated) in HCV-replicating cells compared with controls. Protein identifications and quantifications obtained by both proteomics approaches were largely concordant. Modulated proteins included a majority of proteins involved in vesicular and protein trafficking and in cell signaling. Remarkably for a large number of proteins, their up-regulation in lipid rafts of HCV replicon cells was due to their relocalization. By using small interfering RNAs directed to the modulated small GTPases Cdc42 and RhoA, we observed an increase in HCV replication, whereas reduction of syntaxin 7 expression resulted in decreased replication of HCV. Our findings indicate that protein subcellular relocalization occurs in HCV-containing cells that can directly affect HCV replication.

Differential Label-free Quantitative Proteomic Analysis of Shewanella oneidensis Cultured under Aerobic and Suboxic Conditions by Accurate Mass and Time Tag Approach

We describe the application of LC-MS without the use of stable isotope labeling for differential quantitative proteomic analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and suboxic conditions. LC-MS/MS was used to initially identify peptide sequences, and LC-FTICR was used to confirm these identifications as well as measure relative peptide abundances. 2343 peptides covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as statistical analysis of microarrays, whereas another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis was transitioned from aerobic to suboxic conditions.

Mixed-effects statistical model for comparative LC-MS proteomics studies.

Comparing a proteins concentrations across two or more treatments is the focus of many proteomics studies. A frequent source of measurements for these comparisons is a mass spectrometry (MS) analysis of a proteins peptide ions separated by liquid chromatography (LC) following its enzymatic digestion. Alas, LC-MS identification and quantification of equimolar peptides can vary significantly due to their unequal digestion, separation, and ionization. This unequal measurability of peptides, the largest source of LC-MS nuisance variation, stymies confident comparison of a proteins concentration across treatments. Our objective is to introduce a mixed-effects statistical model for comparative LC-MS proteomics studies. We describe LC-MS peptide abundance with a linear model featuring pivotal terms that account for unequal peptide LC-MS measurability. We advance fitting this model to an often incomplete LC-MS data set with REstricted Maximum Likelihood (REML) estimation, producing estimates of model goodness-of-fit, treatment effects, standard errors, confidence intervals, and protein relative concentrations. We illustrate the model with an experiment featuring a known dilution series of a filamentous ascomycete fungus Trichoderma reesei protein mixture. For 781 of the 1546 T. reesei proteins with sufficient data coverage, the fitted mixed-effects models capably described the LC-MS measurements. The LC-MS measurability terms effectively accounted for this major source of uncertainty. Ninety percent of the relative concentration estimates were within 0.5-fold of the true relative concentrations. Akin to the common ratio method, this model also produced biased estimates, albeit less biased. Bias decreased significantly, both absolutely and relative to the ratio method, as the number of observed peptides per protein increased. Mixed-effects statistical modeling offers a flexible, well-established methodology for comparative proteomics studies integrating common experimental designs with LC-MS sample processing plans. It favorably accounts for the unequal LC-MS measurability of peptides and produces informative quantitative comparisons of a proteins concentration across treatments with objective measures of uncertainties.