Inge Thijs

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

BackgroundMetabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.ResultsHere, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.ConclusionTaken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

Delineation of the Salmonella enterica Serovar Typhimurium HilA Regulon through Genome-Wide Location and Transcript Analysis

The Salmonella enterica serovar Typhimurium HilA protein is the key regulator for the invasion of epithelial cells. By a combination of genome-wide location and transcript analysis, the HilA-dependent regulon has been delineated. Under invasion-inducing conditions, HilA binds to most of the known target genes and a number of new target genes. The sopB, sopE, and sopA genes, encoding effector proteins secreted by the type III secretion system on Salmonella pathogenicity island 1 (SPI-1), were identified as being both bound by HilA and differentially regulated in an HilA mutant. This suggests a cooperative role for HilA and InvF in the regulation of SPI-1-secreted effectors. Also, siiA, the first gene of SPI-4, is both bound by HilA and differentially regulated in an HilA mutant, thus linking this pathogenicity island to the invasion key regulator. Finally, the interactions of HilA with the SPI-2 secretion system gene ssaH and the flagellar gene flhD imply a repressor function for HilA under invasion-inducing conditions.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Integration of omics data: how well does it work for bacteria?

In the current omics era, innovative high‐throughput technologies allow measuring temporal and conditional changes at various cellular levels. Although individual analysis of each of these omics data undoubtedly results into interesting findings, it is only by integrating them that gaining a global insight into cellular behaviour can be aimed at. A systems approach thus is predicated on data integration. However, because of the complexity of biological systems and the specificities of the data‐generating technologies (noisiness, heterogeneity, etc.), integrating omics data in an attempt to reconstruct signalling networks is not trivial. Developing its methodologies constitutes a major research challenge. Besides for their intrinsic value towards health care, environment and industry, prokaryotes are ideal model systems to further develop these methods because of their lower regulatory complexity compared with eukaryotes, and the ease with which they can be manipulated. Several successful examples outlined in this review already show the potential of the systems approach for both fundamental and industrial applications, which would be time‐consuming or impossible to develop solely through traditional reductionist approaches.

FabR regulates Salmonella biofilm formation via its direct target FabB

BackgroundBiofilm formation is an important survival strategy of Salmonella in all environments. By mutant screening, we showed a knock-out mutant of fabR, encoding a repressor of unsaturated fatty acid biosynthesis (UFA), to have impaired biofilm formation. In order to unravel how this regulator impinges on Salmonella biofilm formation, we aimed at elucidating the S. Typhimurium FabR regulon. Hereto, we applied a combinatorial high-throughput approach, combining ChIP-chip with transcriptomics.ResultsAll the previously identified E. coli FabR transcriptional target genes (fabA, fabB and yqfA) were shown to be direct S. Typhimurium FabR targets as well. As we found a fabB overexpressing strain to partly mimic the biofilm defect of the fabR mutant, the effect of FabR on biofilms can be attributed at least partly to FabB, which plays a key role in UFA biosynthesis. Additionally, ChIP-chip identified a number of novel direct FabR targets (the intergenic regions between hpaR/hpaG and ddg/ydfZ) and yet putative direct targets (i.a. genes involved in tRNA metabolism, ribosome synthesis and translation). Next to UFA biosynthesis, a number of these direct targets and other indirect targets identified by transcriptomics (e.g. ribosomal genes, ompA, ompC, ompX, osmB, osmC, sseI), could possibly contribute to the effect of FabR on biofilm formation.ConclusionOverall, our results point at the importance of FabR and UFA biosynthesis in Salmonella biofilm formation and their role as potential targets for biofilm inhibitory strategies.