The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

To cause a systemic infection, Salmonella must respond to many environmental cues during mouse infection and express specific subsets of genes in a temporal and spatial manner, but the regulatory pathways are poorly established. To unravel how micro-environmental signals are processed and integrated into coordinated action, we constructed in-frame non-polar deletions of 83 regulators inferred to play a role in Salmonella enteriditis Typhimurium (STM) virulence and tested them in three virulence assays (intraperitoneal [i.p.], and intragastric [i.g.] infection in BALB/c mice, and persistence in 129X1/SvJ mice). Overall, 35 regulators were identified whose absence attenuated virulence in at least one assay, and of those, 14 regulators were required for systemic mouse infection, the most stringent virulence assay. As a first step towards understanding the interplay between a pathogen and its host from a systems biology standpoint, we focused on these 14 genes. Transcriptional profiles were obtained for deletions of each of these 14 regulators grown under four different environmental conditions. These results, as well as publicly available transcriptional profiles, were analyzed using both network inference and cluster analysis algorithms. The analysis predicts a regulatory network in which all 14 regulators control the same set of genes necessary for Salmonella to cause systemic infection. We tested the regulatory model by expressing a subset of the regulators in trans and monitoring transcription of 7 known virulence factors located within Salmonella pathogenicity island 2 (SPI-2). These experiments validated the regulatory model and showed that the response regulator SsrB and the MarR type regulator, SlyA, are the terminal regulators in a cascade that integrates multiple signals. Furthermore, experiments to demonstrate epistatic relationships showed that SsrB can replace SlyA and, in some cases, SlyA can replace SsrB for expression of SPI-2 encoded virulence factors.

We have examined expression of the genes on Salmonella pathogenicity island 1 (SPI1) during growth under the physiologically well defined standard growth condition of Luria-Bertani medium with aeration. We found that the central regulator hilA and the genes under its control are expressed at the onset of stationary phase. Interestingly, the two-component regulatory genes hilC/ hilD, sirA/barA, and ompR, which are known to modulate expression from the hilA promoter (hilAp) under so-called "inducing conditions" (Luria-Bertani medium containing 0.3 M NaCl without aeration), acted under standard conditions at the stationary phase induction level. The induction of hilAp depended not on RpoS, the stationary phase sigma factor, but on the stringent signal molecule ppGpp. In the ppGpp null mutant background, hilAp showed absolutely no activity. The stationary phase induction of hilAp required spoT but not relA. Consistent with this requirement, hilAp was also induced by carbon source deprivation, which is known to transiently elevate ppGpp mediated by spoT function. The observation that amino acid starvation elicited by the addition of serine hydroxamate did not induce hilAp in a RelA ؉ SpoT ؉ strain suggested that, in addition to ppGpp, some other alteration accompanying entry into the stationary phase might be necessary for induction. It is speculated that during the course of infection Salmonella encounters various stressful environments that are sensed and translated to the intracellular signal, ppGpp, which allows expression of Salmonella virulence genes, including SPI1 genes.

Using sample-matched transcriptomics and proteomics measurements it is now possible to begin to understand the impact of post-transcriptional regulatory programs in Enterobacteria. In bacteria post-transcriptional regulation is mediated by relatively few identified RNA-binding protein factors including CsrA, Hfq and SmpB. A mutation in any one of these three genes, csrA, hfq, and smpB, in Salmonella is attenuated for mouse virulence and unable to survive in macrophages. CsrA has a clearly defined specificity based on binding to a specific mRNA sequence to inhibit translation. However, the proteins regulated by Hfq and SmpB are not as clearly defined. Previous work identified proteins regulated by hfq using purification of the RNA-protein complex with direct sequencing of the bound RNAs and found binding to a surprisingly large number of transcripts. In this report we have used global proteomics to directly identify proteins regulated by Hfq or SmpB by comparing protein abundance in the parent and isogenic hfq or smpB mutant. From these same samples we also prepared RNA for microarray analysis to determine if alteration of protein expression was mediated post-transcriptionally. Samples were analyzed from bacteria grown under four different conditions; two laboratory conditions and two that are thought to mimic the intracellular environment. We show that mutants of hfq and smpB directly or indirectly modulate at least 20% and 4% of all possible Salmonella proteins, respectively, with limited correlation between transcription and protein expression. These proteins represent a broad spectrum of Salmonella proteins required for many biological processes including host cell invasion, motility, central metabolism, LPS biosynthesis, two-component regulatory systems, and fatty acid metabolism. Our results represent one of the first global analyses of post-transcriptional regulons in any organism and suggest that regulation at the translational level is widespread and plays an important role in virulence regulation and environmental adaptation for Salmonella.

Abstract-Feature subset selection (FSS) is a known technique to preprocess the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated the data. We propose a family of novel unsupervised methods for feature subset selection from Multivariate Time Series (MTS) based on Common Principal Component Analysis, termed C CLeV V er. Traditional FSS techniques, such as Recursive Feature Elimination (RFE) and Fisher Criterion (FC), have been applied to MTS data sets, e.g., Brain Computer Interface (BCI) data sets. However, these techniques may lose the correlation information among features, while our proposed techniques utilize the properties of the principal component analysis to retain that information. In order to evaluate the effectiveness of our selected subset of features, we employ classification as the target data mining task. Our exhaustive experiments show that C CLeV V er outperforms RFE, FC, and random selection by up to a factor of two in terms of the classification accuracy, while taking up to 2 orders of magnitude less processing time than RFE and FC.

It has been estimated that less than 1% of the microorganisms in nature can be cultivated by conventional techniques. Thus, the classical approach of isolating enzymes from pure cultures allows the analysis of only a subset of the total naturally occurring microbiota in environmental samples enriched in microorganisms. To isolate useful microbial enzymes from uncultured soil microorganisms, a metagenome was isolated from soil samples, and a metagenomic library was constructed by using the pUC19 vector. The library was screened for amylase activity, and one clone from among approximately 30,000 recombinant Escherichia coli clones showed amylase activity. Sequencing of the clone revealed a novel amylolytic enzyme expressed from a novel gene. The putative amylase gene (amyM) was overexpressed and purified for characterization. Optimal conditions for the enzyme activity of the AmyM protein were 42°C and pH 9.0; Ca 2؉ stabilized the activity. The amylase hydrolyzed soluble starch and cyclodextrins to produce high levels of maltose and hydrolyzed pullulan to panose. The enzyme showed a high transglycosylation activity, making ␣-(1, 4) linkages exclusively. The hydrolysis and transglycosylation properties of AmyM suggest that it has novel characteristics and can be regarded as an intermediate type of maltogenic amylase, ␣-amylase, and 4-␣-glucanotransferase.

Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.

SsrA/SsrB is a primary two-component system that mediates the survival and replication of Salmonella within host cells. When activated, the SsrB response regulator directly promotes the transcription of multiple genes within Salmonella pathogenicity island 2 (SPI-2). As expression of the SsrB protein is promoted by several transcription factors, including SsrB itself, the expression of SPI-2 genes can increase to undesirable levels under activating conditions. Here, we report that Salmonella can avoid the hyperactivation of SPI-2 genes by using ptsN-encoded EIIA Ntr , a component of the nitrogen-metabolic phosphotransferase system. Under SPI-2-inducing conditions, the levels of SsrB-regulated gene transcription increased abnormally in a ptsN deletion mutant, whereas they decreased in a strain overexpressing EIIA Ntr . We found that EIIA Ntr controls SPI-2 genes by acting on the SsrB protein at the posttranscriptional level. EIIA Ntr interacted directly with SsrB, which prevented the SsrB protein from binding to its target promoter. Finally, the Salmonella strain, either lacking the ptsN gene or overexpressing EIIA Ntr , was unable to replicate within macrophages, and the ptsN deletion mutant was attenuated for virulence in mice. These results indicated that normal SPI-2 gene expression maintained by an EIIA Ntr -SsrB interaction is another determinant of Salmonella virulence.virulence gene regulation | nitrogen-metabolic phosphotransferase system (PTS) | protein-protein interaction