Growth Curve Models for the Analysis of Phenotype Arrays for a Systems Biology Overview of Yersinia pestis

The Phenotype MicroArray technology of Biolog, Inc. (Hayward, CA) measures the respiration of cells as a function of time in thousands of microwells simultaneously, and thus provides a high-throughput means of studying cellular phenotypes. The microwells contain compounds involved in a number of biochemical pathways, as well as chemicals that test the sensitivity of cells against antibiotics and stress. While the PM experimental workflow is completely automated, statistical methods to analyze and interpret the data are lagging behind. To take full advantage of the technology, it is essential to develop efficient analytical methods to quantify the information in the complex datasets resulting from PM experiments. We propose the use of statistical growth-curve models to rigorously quantify observed differences in PM experiments, in the context of the growth and metabolism of Yersinia pestis cells grown under different physiological conditions. The information from PM experiments complement genomic and proteomic results and can be used to identify gene function and in drug development. Successful coupling of phenomics results with genomics and proteomics will lead to an unprecedented ability to characterize bacterial function at a systems biology level

Statistical Analysis of Variation in the Human Plasma Proteome

Quantifying the variation in the human plasma proteome is an essential prerequisite for disease-specific biomarker detection. We report here on the longitudinal and individual variation in human plasma characterized by two-dimensional difference gel electrophoresis (2-D DIGE) using plasma samples from eleven healthy subjects collected three times over a two week period. Fixed-effects modeling was used to remove dye and gel variability. Mixed-effects modeling was then used to quantitate the sources of proteomic variation. The subject-to-subject variation represented the largest variance component, while the time-within-subject variation was comparable to the experimental variation found in a previous technical variability study where one human plasma sample was processed eight times in parallel and each was then analyzed by 2-D DIGE in triplicate. Here, 21 protein spots had larger than 50% CV, suggesting that these proteins may not be appropriate as biomarkers and should be carefully scrutinized in future studies. Seventy-eight protein spots showing differential protein levels between different individuals or individual collections were identified by mass spectrometry and further characterized using hierarchical clustering. The results present a first step toward understanding the complexity of longitudinal and individual variation in the human plasma proteome, and provide a baseline for improved biomarker discovery

Proteomic characterization of Yersinia pestis virulence

ABSTRACT Yersinia pestis, the etiological agent of plague, functions via the Type III secretion mechanism whereby virulence factors are induced upon interactions with a mammalian host. Here, the Y. pestis proteome was studied by two-dimensional differential gel electrophoresis (2-D DIGE) under physiologically relevant growth conditions mimicking the calcium concentrations and temperatures that the pathogen would encounter in the flea vector and upon interaction with the mammalian host. Over 4100 individual protein spots were detected of which hundreds were differentially expressed in the entire comparative experiment. A total of 43 proteins that were differentially expressed between the vector and host growth conditions were identified by mass spectrometry. Expected differences in expression were observed for several known virulence factors including catalase-peroxidase (KatY), murine toxin (Ymt), plasminogen activator (Pla), and F1 capsule antigen (Caf1), as well as putative virulence factors. Chaperone proteins and signaling molecules hypothesized to be involved in virulence due to their role in Type III secretion were also identified. Other differentially expressed proteins not previously reported to contribute to virulence are candidates for more detailed mechanistic studies, representing potential new virulence determinants. For example, several sugar metabolism proteins were differentially regulated in response to lower calcium and higher temperature, suggesting these proteins, while not directly connected to virulence, either represent a metabolic switch for survival in the host environment or may facilitate production of virulence factors. Results presented here contribute to a more thorough understanding of the virulence mechanism of Y. pestis through proteomic characterization of the pathogen under induced virulence

Cluster analysis of host cytokine responses to biodefense pathogens in a whole blood ex vivo exposure model (WEEM)

AbstractBackgroundRapid detection and therapeutic intervention for infectious and emerging diseases is a major scientific goal in biodefense and public health. Toward this end, cytokine profiles in human blood were investigated using a human whole blood ex vivo exposure model, called WEEM.ResultsSamples of whole blood from healthy volunteers were incubated with seven pathogens including Yersinia pseudotuberculosis, Yersinia enterocolitica, Bacillus anthracis, and multiple strains of Yersinia pestis, and multiplexed protein expression profiling was conducted on supernatants of these cultures with an antibody array to detect 30 cytokines simultaneously. Levels of 8 cytokines, IL-1α, IL-1β, IL-6, IL-8, IL-10, IP-10, MCP-1 and TNFα, were significantly up-regulated in plasma after bacterial exposures of 4 hours. Statistical clustering was applied to group the pathogens based on the host response protein expression profiles. The nearest phylogenetic neighbors clustered more closely than the more distant pathogens, and all seven pathogens were clearly differentiated from the unexposed control. In addition, the Y. pestis and Yersinia near neighbors were differentiated from the B. anthracis strains.ConclusionsCluster analysis, based on host response cytokine profiles, indicates that distinct patterns of immunomodulatory proteins are induced by the different pathogen exposures and these patterns may enable further development into biomarkers for diagnosing pathogen exposure

Subcellular Proteomic Analysis of Host-Pathogen Interactions Using Human Monocytes Exposed to Yersinia Pestis and Yersinia Pseudotuberculosis

Yersinia pestis, the etiological agent of plague, is of concern to human health both from an infectious disease and a civilian biodefense perspective. While Y. pestis and Y. pseudotuberculosis share more than 90% DNA homology, they have significantly different clinical manifestations. Plague is often fatal if untreated, yet Y. pseudotuberculosis causes severe intestinal distress and is rarely fatal. A better understanding of host response to these closely related pathogens may help explain the different mechanisms of virulence and pathogenesis that result in such different clinical outcomes. The aim of this study was to characterize host protein expression changes in human monocyte-like U937 cells after exposure to Y. pestis and Y. pseudotuberculosis. In order to gain global proteomic coverage of host response, proteins from cytoplasmic, nuclear and membrane fractions of host cells were studied by 2-dimensional differential gel electrophoresis (2-D DIGE) and relative protein expression differences were quantitated. Differentially expressed proteins, with at least 1.5 fold expression changes and p values of 0.01 or less, were identified by MALDI-MS or LC/MS/MS. With these criteria, differential expression was detected in 16 human proteins after Y. pestis exposure and 13 human proteins after Y. pseudotuberculosis exposure, of which only two of the differentially expressed proteins identified were shared between the two exposures. Proteins identified in this study are reported to be involved in a wide spectrum of cellular functions and host defense mechanisms including apoptosis, cytoskeletal rearrangement, protein synthesis and degradation, DNA replication and transcription, metabolism, protein folding, and cell signaling. Notably, the differential expression patterns observed can distinguish the two pathogen exposures from each other and from unexposed host cells. The functions of the differentially expressed proteins identified provide insight on the different virulence and pathogenic mechanisms of Y. pestis and Y. pseudotuberculosis

Polynucleotide phosphorylase independently controls virulence factor expression levels and export in Yersinia spp

Abstract Previously, it was shown that optimal functioning of the Yersinia type III secretion system (T3SS) in cell culture infection assays requires the exoribonuclease polynucleotide phosphorylase (PNPase) and that normal T3SS activity could be restored in the Δpnp strains by expressing just the ~70-aa S1 RNA-binding domain of PNPase. Here, it is shown that the YersiniaΔpnp strain is less virulent in the mouse compared with the isogenic wild-type strain. To begin to understand what could be limiting T3SS activity in the absence of PNPase, T3SS-encoding transcripts and proteins in the YersiniaΔpnp strains were analyzed. Surprisingly, it was found that the Δpnp Yersinia strains possessed enhanced levels of T3SS-encoding transcripts and proteins compared with the wild-type strains. We then found that an S1 variant containing a disruption in its RNA-binding subdomain was inactive in terms of restoring normal T3SS activity. However, T3SS expression levels did not differ between Δpnp strains expressing active and inactive S1 proteins, further showing that T3SS activity and expression levels, at least as related to PNPase and its S1 domain, are not linked. The results suggest that PNPase affects the expression and activity of the T3SS by distinct mechanisms and that the S1-dependent effect on T3SS activity involves an RNA intermediate

Proteomic Characterization of Yersinia pestis Virulence

The Yersinia pestis proteome was studied as a function of temperature and calcium by two-dimensional differential gel electrophoresis. Over 4,100 individual protein spots were detected, of which hundreds were differentially expressed. A total of 43 differentially expressed protein spots, representing 24 unique proteins, were identified by mass spectrometry. Differences in expression were observed for several virulence-associated factors, including catalase-peroxidase (KatY), murine toxin (Ymt), plasminogen activator (Pla), and F1 capsule antigen (Caf1), as well as several putative virulence factors and membrane-bound and metabolic proteins. Differentially expressed proteins not previously reported to contribute to virulence are candidates for more detailed mechanistic studies, representing potential new virulence determinants