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.
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. Salmonella enterica serovar Typhimurium causes a host-dependent range of diseases from self-limiting gastroenteritis to life-threatening systemic infections. The complex infection process is initiated by invasion of the intestinal epithelial monolayer (75) by means of a type III secretion system (TTSS), encoded on pathogenicity island 1 (SPI-1), through which effector proteins are translocated into the epithelial cells (17,53). By manipulating host cell functions via these effector proteins, S. enterica serovar Typhimurium alters the epithelial cell's cytoskeletal structure, leading to bacterial internalization (76).The key regulator for the composition and functioning of this invasion-enabling TTSS and associated effector proteins is HilA, an OmpR/ToxR family transcriptional regulator (5), which is also encoded within SPI-1. A complex interaction of environmental and genetic control elements (3,29,43) induces HilA to activate the inv/spa and prg operons, encoding components of the TTSS apparatus (51, 52), and the sic/sip operon, encoding a chaperone and secreted proteins (23). SPI-4, which is required for the enteric phase of pathogenesis (62), also has been related to HilA (2, 23, 61). Furthermore, HilA represses its own expression (23). In addition, HilA indirectly regulates expression of secreted proteins by activating the transcription of the SPI-1 invF gene, encoding an AraC family transcriptional regulator (19).In this work, data on in vivo HilA binding, obtained through genome-wide location analysis (GWLA) or chromatin immunoprecipitation microarray (CHIP-chip) experiments (12), have been combined with transcriptional profiling of an hilA mutant versus a wild-type strain and in silico motif detection to provide a delineation of the direct HilA regulon, i.e., all genes directly bound by HilA, on a genome-wide scale. Retrieval of most of the known direct HilA target genes validated this approach. Moreover, a number of new targets were identified. Based on these findings, an extension of the ...
BackgroundNon-coding RNAs (ncRNAs) play a crucial role in the intricate regulation of bacterial gene expression, allowing bacteria to quickly adapt to changing environments. In the past few years, a growing number of regulatory RNA elements have been predicted by computational methods, mostly in well-studied γ-proteobacteria but lately in several α-proteobacteria as well. Here, we have compared an extensive compilation of these non-coding RNA predictions to intergenic expression data of a whole-genome high-resolution tiling array in the soil-dwelling α-proteobacterium Rhizobium etli.ResultsExpression of 89 candidate ncRNAs was detected, both on the chromosome and on the six megaplasmids encompassing the R. etli genome. Of these, 11 correspond to functionally well characterized ncRNAs, 12 were previously identified in other α-proteobacteria but are as yet uncharacterized and 66 were computationally predicted earlier but had not been experimentally identified and were therefore classified as novel ncRNAs. The latter comprise 17 putative sRNAs and 49 putative cis-regulatory ncRNAs. A selection of these candidate ncRNAs was validated by RT-qPCR, Northern blotting and 5' RACE, confirming the existence of 4 ncRNAs. Interestingly, individual transcript levels of numerous ncRNAs varied during free-living growth and during interaction with the eukaryotic host plant, pointing to possible ncRNA-dependent regulation of these specialized processes.ConclusionsOur data support the practical value of previous ncRNA prediction algorithms and significantly expand the list of candidate ncRNAs encoded in the intergenic regions of R. etli and, by extension, of α-proteobacteria. Moreover, we show high-resolution tiling arrays to be suitable tools for studying intergenic ncRNA transcription profiles across the genome. The differential expression levels of some of these ncRNAs may indicate a role in adaptation to changing environmental conditions.
BackgroundAlthough remote monitoring (RM) has proven its added value in various health care domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive disorders (GHD).ObjectiveThe aim of this study was to evaluate the added value of a remote follow-up program for pregnant women diagnosed with GHD.MethodsA 1-year retrospective study was performed in the outpatient clinic of a 2nd level prenatal center where pregnant women with GHD received RM or conventional care (CC). Primary study endpoints include number of prenatal visits and admissions to the prenatal observation ward. Secondary outcomes include gestational outcome, mode of delivery, neonatal outcome, and admission to neonatal intensive care (NIC). Differences in continuous and categorical variables in maternal demographics and characteristics were tested using Unpaired Student’s two sampled t test or Mann-Whitney U test and the chi-square test. Both a univariate and multivariate analysis were performed for analyzing prenatal follow-up and gestational outcomes. All statistical analyses were done at nominal level, Cronbach alpha=.05.ResultsOf the 166 patients diagnosed with GHD, 53 received RM and 113 CC. After excluding 5 patients in the RM group and 15 in the CC group because of the missing data, 48 patients in RM group and 98 in CC group were taken into final analysis. The RM group had more women diagnosed with gestational hypertension, but less with preeclampsia when compared with CC (81.25% vs 42.86% and 14.58% vs 43.87%). Compared with CC, univariate analysis in RM showed less induction, more spontaneous labors, and less maternal and neonatal hospitalizations (48.98% vs 25.00%; 31.63% vs 60.42%; 74.49% vs 56.25%; and 27.55% vs 10.42%). This was also true in multivariate analysis, except for hospitalizations.ConclusionsAn RM follow-up of women with GHD is a promising tool in the prenatal care. It opens the perspectives to reverse the current evolution of antenatal interventions leading to more interventions and as such to ever increasing medicalized antenatal care.
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.
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