Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation

Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis

The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).

Three genetic-environmental networks for human personality

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.

Uncovering Tumour Heterogeneity through PKR and nc886 Analysis in Metastatic Colon Cancer Patients Treated with 5-FU-Based Chemotherapy

Colorectal cancer treatment has advanced over the past decade. The drug 5-fluorouracil is still used with a wide percentage of patients who do not respond. Therefore, a challenge is the identification of predictive biomarkers. The protein kinase R (PKR also called EIF2AK2) and its regulator, the non-coding pre-mir-nc886, have multiple effects on cells in response to numerous types of stress, including chemotherapy. In this work, we performed an ambispective study with 197 metastatic colon cancer patients with unresectable metastases to determine the relative expression levels of both nc886 and PKR by qPCR, as well as the location of PKR by immunohistochemistry in tumour samples and healthy tissues (plasma and colon epithelium). As primary end point, the expression levels were related to the objective response to first-line chemotherapy following the response evaluation criteria in solid tumours (RECIST) and, as the second end point, with survival at 18 and 36 months. Hierarchical agglomerative clustering was performed to accommodate the heterogeneity and complexity of oncological patients' data. High expression levels of nc886 were related to the response to treatment and allowed to identify clusters of patients. Although the PKR mRNA expression was not associated with chemotherapy response, the absence of PKR location in the nucleolus was correlated with first-line chemotherapy response. Moreover, a relationship between survival and the expression of both PKR and nc886 in healthy tissues was found. Therefore, this work evaluated the best way to analyse the potential biomarkers PKR and nc886 in order to establish clusters of patients depending on the cancer outcomes using algorithms for complex and heterogeneous data.

Uncovering the complex genetics of human character

Human personality is 30-60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic-phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Uncovering the complex genetics of human temperament

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

The complex genetics and biology of human temperament: a review of traditional concepts in relation to new molecular findings

Recent genome-wide association studies (GWAS) have shown that temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term learning and memory. The results were replicated in three independent samples despite variable cultures and environments. The identified genes were enriched in pathways activated by behavioral conditioning in animals, including the two major molecular pathways for response to extracellular stimuli, the Ras-MEK-ERK and the PI3K-AKT-mTOR cascades. These pathways are activated by a wide variety of physiological and psychosocial stimuli that vary in positive and negative valence and in consequences for health and survival. Changes in these pathways are orchestrated to maintain cellular homeostasis despite changing conditions by modulating temperament and its circadian and seasonal rhythms. In this review we first consider traditional concepts of temperament in relation to the new genetic findings by examining the partial overlap of alternative measures of temperament. Then we propose a definition of temperament as the disposition of a person to learn how to behave, react emotionally, and form attachments automatically by associative conditioning. This definition provides necessary and sufficient criteria to distinguish temperament from other aspects of personality that become integrated with it across the life span. We describe the effects of specific stimuli on the molecular processes underlying temperament from functional, developmental, and evolutionary perspectives. Our new knowledge can improve communication among investigators, increase the power and efficacy of clinical trials, and improve the effectiveness of treatment of personality and its disorders.

PhoP-Mediated Repression of the SPI1 Type 3 Secretion System in Salmonella enterica Serovar Typhimurium

Salmonella must rapidly adapt to various niches in the host during infection. Relevant virulence factors must be appropriately induced, and systems that are detrimental in a particular environment must be turned off. Salmonella infects intestinal epithelial cells using a type 3 secretion system (T3SS) encoded on Salmonella pathogenicity island 1 (SPI1). The system is controlled by three AraC-like regulators, HilD, HilC, and RtsA, which form a complex feed-forward loop to activate expression of hilA, encoding the main transcriptional regulator of T3SS structural genes. This system is tightly regulated, with many of the activating signals acting at the level of hilD translation or HilD protein activity. Once inside the phagosomes of epithelial cells, or in macrophages during systemic stages of disease, the SPI1 T3SS is no longer required or expressed. Here, we show that the PhoPQ two-component system, critical for intracellular survival, appears to be the primary mechanism by which Salmonella shuts down the SPI1 T3SS. PhoP negatively regulates hilA through multiple distinct mechanisms: direct transcriptional repression of the hilA promoter, indirect transcriptional repression of both the hilD and rtsA promoters, and activation of the small RNA (sRNA) PinT. Genetic analyses and electrophoretic mobility shift assays suggest that PhoP specifically binds the hilA promoter to block binding of activators HilD, HilC, and RtsA as a mechanism of repression.IMPORTANCE Salmonella is one of the most common foodborne pathogens, causing an estimated 1.2 million illnesses per year in the United States. A key step in infection is the activation of the bacterial invasion machinery, which induces uptake of the bacterium into epithelial cells and leads to induction of inflammatory diarrhea. Upon entering the vacuolar compartments of host cells, Salmonella senses an environmental transition and represses the invasion machinery with a two-component system relevant for survival within the vacuole. This adaptation to specific host niches is an important example of how signals are integrated for survival of the pathogen.

The influence of temperament and character profiles on specialty choice and well-being in medical residents

BACKGROUND

Multiple factors influence the decision to enter a career in medicine and choose a specialty. Previous studies have looked at personality differences in medicine but often were unable to describe the heterogeneity that exists within each specialty. Our study used a person-centered approach to characterize the complex relations between the personality profiles of resident physicians and their choice of specialty.

METHODS

169 resident physicians at a large Midwestern US training hospital completed the Temperament and Character Inventory (TCI) and the Satisfaction with Life Scale (SWLS). Clusters of personality profiles were identified without regard to medical specialty, and then the personality clusters were tested for association with their choice of specialty by co-clustering analysis. Life satisfaction was tested for association with personality traits and medical specialty by linear regression and analysis of variance.

RESULTS

We identified five clusters of people with distinct personality profiles, and found that these were associated with particular medical specialties Physicians with an "investigative" personality profile often chose pathology or internal medicine, those with a "commanding" personality often chose general surgery, "rescuers" often chose emergency medicine, the "dependable" often chose pediatrics, and the "compassionate" often chose psychiatry. Life satisfaction scores were not enhanced by personality-specialty congruence, but were related strongly to self-directedness regardless of specialty.

CONCLUSIONS

The personality profiles of physicians were strongly associated with their medical specialty choices. Nevertheless, the relationships were complex: physicians with each personality profile went into a variety of medical specialties, and physicians in each medical specialty had variable personality profiles. The plasticity and resilience of physicians were more important for their life satisfaction than was matching personality to the prototype of a particular specialty.

Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy

Fractional anisotropy (FA) analysis of diffusion tensor-images (DTI) has yielded inconsistent abnormalities in schizophrenia (SZ). Inconsistencies may arise from averaging heterogeneous groups of patients. Here we investigate whether SZ is a heterogeneous group of disorders distinguished by distinct patterns of FA reductions. We developed a Generalized Factorization Method (GFM) to identify biclusters (i.e., subsets of subjects associated with a subset of particular characteristics, such as low FA in specific regions). GFM appropriately assembles a collection of unsupervised techniques with Non-negative Matrix Factorization to generate biclusters, rather than averaging across all subjects and all their characteristics. DTI tract-based spatial statistics images, which output is the locally maximal FA projected onto the group white matter skeleton, were analyzed in 47 SZ and 36 healthy subjects, identifying 8 biclusters. The mean FA of the voxels of each bicluster was significantly different from those of other SZ subjects or 36 healthy controls. The eight biclusters were organized into four more general patterns of low FA in specific regions: 1) genu of corpus callosum (GCC), 2) fornix (FX)+external capsule (EC), 3) splenium of CC (SCC)+retrolenticular limb (RLIC)+posterior limb (PLIC) of the internal capsule, and 4) anterior limb of the internal capsule. These patterns were significantly associated with particular clinical features: Pattern 1 (GCC) with bizarre behavior, pattern 2 (FX+EC) with prominent delusions, and pattern 3 (SCC+RLIC+PLIC) with negative symptoms including disorganized speech. The uncovered patterns suggest that SZ is a heterogeneous group of disorders that can be distinguished by different patterns of FA reductions associated with distinct clinical features.

A multiobjective method for robust identification of bacterial small non-coding RNAs

MOTIVATION

Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species.

RESULTS

We present a novel methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits. Many non-redundant optimal aggregations uncovered by using multiobjective optimization techniques are then combined into a multiclassifier, which ensures robustness during detection and prediction even in genomes with distinct nucleotide composition. By training with sRNAs in Salmonella enterica Typhimurium, we were able to successfully predict sRNAs in Sinorhizobium meliloti, as well as in multiple and poorly annotated species. The proposed methodology, like a meta-analysis approach, may begin to lay a possible foundation for developing robust predictive methods across a wide spectrum of genomic variability.

AVAILABILITY AND IMPLEMENTATION

Scripts created for the experimentation are available at http://m4m.ugr.es/SupInfo/sRNAOS/sRNAOSscripts.zip.

CONTACT

delval@decsai.ugr.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS

It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual GWAS nor meta-analytic combinations thereof are helpful for disclosing which genetic variants contribute to explain a particular phenotype. Here, we propose that most of the missing heritability is latent in the GWAS data, which conceals intermediate phenotypes. To uncover such latent information, we propose the PGMRA server that introduces phenomics--the full set of phenotype features of an individual--to identify SNP-set structures in a broader sense, i.e. causally cohesive genotype-phenotype relations. These relations are agnostically identified (without considering disease status of the subjects) and organized in an interpretable fashion. Then, by incorporating a posteriori the subject status within each relation, we can establish the risk surface of a disease in an unbiased mode. This approach complements-instead of replaces-current analysis methods. The server is publically available at http://phop.ugr.es/fenogeno.

An integrative approach to inferring gene regulatory module networks

Background

Gene regulatory networks (GRNs) provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of gene regulatory modules and GRNs require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming.

Results

In this work, the BiologicalNetworks application–the data integration and network based research environment–was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the gene regulation analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided.

Conclusion

The generated modular networks, regulatory modules and binding sites can be visualized and further analyzed within this same application. The developed tools were applied to the mouse model of asthma and the OCT4 regulatory network in embryonic stem cells. Developed methods and data are available through the Java application from BiologicalNetworks program at http://www.biologicalnetworks.org.

Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor

Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli, transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the challenge of inferring activities of unknown regulators by generating de novo (binding) motifs and integrating with expression data. We identify a number of key regulators active in the metabolic switch, including PhoP with its associated directed repeat PHO box, candidate motifs for two SARPs, a CRP family regulator, an iron response regulator and that for LexA. Experimental validation for some of our predictions was obtained using gel-shift assays. Our analysis is applicable to any organism for which there is a reasonable amount of complementary expression data and for which motifs (either over represented or evolutionary conserved) can be identified in the genome.

Advantages and limitations of current network inference methods

Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.

Defining the plasticity of transcription factor binding sites by Deconstructing DNA consensus sequences: the PhoP-binding sites among gamma/enterobacteria

Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg(2+) homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the "Divide & Conquer" strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase.

Identifying promoter features of co-regulated genes with similar network motifs

Background

A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.

Results

We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif.

Conclusion

Global transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or network motif, but also the cis-acting elements participating in each of these connexions.

Evolution of a bacterial regulon controlling virulence and Mg(2+) homeostasis

Related organisms typically rely on orthologous regulatory proteins to respond to a given signal. However, the extent to which (or even if) the targets of shared regulatory proteins are maintained across species has remained largely unknown. This question is of particular significance in bacteria due to the widespread effects of horizontal gene transfer. Here, we address this question by investigating the regulons controlled by the DNA-binding PhoP protein, which governs virulence and Mg(2+) homeostasis in several bacterial species. We establish that the ancestral PhoP protein directs largely different gene sets in ten analyzed species of the family Enterobacteriaceae, reflecting both regulation of species-specific targets and transcriptional rewiring of shared genes. The two targets directly activated by PhoP in all ten species (the most distant of which diverged >200 million years ago), and coding for the most conserved proteins are the phoPQ operon itself and the lipoprotein-encoding slyB gene, which decreases PhoP protein activity. The Mg(2+)-responsive PhoP protein dictates expression of Mg(2+) transporters and of enzymes that modify Mg(2+)-binding sites in the cell envelope in most analyzed species. In contrast to the core PhoP regulon, which determines the amount of active PhoP and copes with the low Mg(2+) stress, the variable members of the regulon contribute species-specific traits, a property shared with regulons controlled by dissimilar regulatory proteins and responding to different signals.

DSM axis II: personality disorders or adaptation disorders?

PURPOSE OF REVIEW

After the initial enthusiasm, the study of personality disorder seems to be at a crossroad, without clear direction. This is mainly due to overlapping categorical diagnostic criteria of personality disorders. Study samples based on these criteria are inadequate and their results questionable.

RECENT FINDINGS

The literature is unanimously advocating a dimensional concept of personality disorders. Four dimensions are consistently reported to underlie personality disorder symptoms. We put forward an argument that personality disorders are disorders of adaptation, not of personality per se, as extreme personality traits are not ipso facto dysfunctional. Available methods to assess maladaptation are reviewed.

SUMMARY

The diagnosis 'personality disorder' should be replaced by the diagnosis 'adaptation disorders'. This reflects the real nature of the disorder more accurately, and is likely to reduce the stigmatizing component of the personality disorder diagnosis as it places emphasis on positive efforts to improve adaptation. The suggested revisions of the personality disorder diagnosis and dimensional approach to these disorders are likely to advance treatment and research - we discuss these aspects in some detail.

RstA-promoted expression of the ferrous iron transporter FeoB under iron-replete conditions enhances Fur activity in Salmonella enterica

The Fur protein is a primary regulator that monitors and controls cytoplasmic iron levels. We now report the identification of a regulatory pathway mediated by the Salmonella response regulator RstA that promotes Fur activity. Genome-wide expression experiments revealed that under iron-replete conditions, expression of the RstA protein from a plasmid lowered transcription levels of various genes involved in iron acquisition. The RstA protein controlled iron-responsive genes through the Fur-Fe(II) protein because deletion of the fur gene or iron depletion abrogated RstA-mediated repression of these genes. The RstA protein maintained wild-type levels of the Fur protein but exceptionally activated transcription of the feoAB operon encoding the ferrous iron transporter FeoB by binding directly to the feoA promoter. This FeoB induction resulted in increased ferrous iron uptake, which associates with the Fur protein because lack of RstA-dependent transcriptional activation of the feoA promoter and feoB-deletion abolished repression of the Fur target genes by the RstA protein. Under iron-replete conditions, RstA expression retarded Salmonella growth but enabled the Fur protein to repress the target genes beyond the levels which were simply accomplished by iron.

Onto-CC: a web server for identifying Gene Ontology conceptual clusters

The Gene Ontology (GO) vocabulary has been extensively explored to analyze the functions of coexpressed genes. However, despite its extended use in Biology and Medical Sciences, there are still high levels of uncertainty about which ontology (i.e. Molecular Process, Cellular Component or Molecular Function) should be used, and at which level of specificity. Moreover, the GO database can contain incomplete information resulting from human annotations, or highly influenced by the available knowledge about a specific branch in an ontology. In spite of these drawbacks, there is a trend to ignore these problems and even use GO terms to conduct searches of gene expression profiles (i.e. expression + GO) instead of more cautious approaches that just consider them as an independent source of validation (i.e. expression versus GO). Consequently, propagating the uncertainty and producing biased analysis of the required gene grouping hypotheses. We proposed a web tool, Onto-CC, as an automatic method specially suited for independent explanation/validation of gene grouping hypotheses (e.g. coexpressed genes) based on GO clusters (i.e. expression versus GO). Onto-CC approach reduces the uncertainty of the queries by identifying optimal conceptual clusters that combine terms from different ontologies simultaneously, as well as terms defined at different levels of specificity in the GO hierarchy. To do so, we implemented the EMO-CC methodology to find clusters in structural databases [GO Directed acyclic Graph (DAG) tree], inspired on Conceptual Clustering algorithms. This approach allows the management of optimal cluster sets as potential parallel hypotheses, guided by multiobjective/multimodal optimization techniques. Therefore, we can generate alternative and, still, optimal explanations of queries that can provide new insights for a given problem. Onto-CC has been successfully used to test different medical and biological hypotheses including the explanation and prediction of gene expression profiles resulting from the host response to injuries in the inflammatory problem. Onto-CC provides two versions: Ready2GO, a precalculated EMO-CC for several genomes and an Advanced Onto-CC for custom annotation files (http://gps-tools2.wustl.edu/onto-cc/index.html).

Overcoming H-NS-mediated transcriptional silencing of horizontally acquired genes by the PhoP and SlyA proteins in Salmonella enterica

The acquisition of new traits through horizontal gene transfer depends on the ability of the recipient organism to express the incorporated genes. However, foreign DNA appears to be silenced by the histone-like nucleoid-structuring protein (H-NS) in several enteric pathogens, raising the question of how this silencing is overcome and the acquired genes are expressed at the right time and place. To address this question, we investigated transcription of the horizontally acquired ugtL and pagC genes from Salmonella enterica, which is dependent on the regulatory DNA-binding proteins PhoP and SlyA. We reconstituted transcription of the ugtL and pagC genes in vitro and determined occupancy of their respective promoters by PhoP, H-NS, and RNA polymerase in vivo. The SlyA protein counteracted H-NS-promoted repression in vitro but could not promote gene transcription by itself. PhoP-promoted transcription required SlyA when H-NS was present but not in its absence. In vivo, H-NS remained bound to the ugtL and pagC promoters under inducing conditions that promoted RNA polymerase recruitment and transcription of the ugtL and pagC genes. Our results indicate that relief of H-NS repression and recruitment of RNA polymerase are controlled by different regulatory proteins that act in concert to express horizontally acquired genes.

Regulation of multidrug efflux systems involved in multidrug and metal resistance of Salmonella enterica serovar Typhimurium

Multidrug-resistant strains of Salmonella are now encountered frequently, and the rates of multidrug resistance have increased considerably in recent years. Here, we report that the two-component regulatory system BaeSR increases multidrug and metal resistance in Salmonella through the induction of drug efflux systems. Screening of random fragments of genomic DNA for the ability to increase beta-lactam resistance in Salmonella enterica led to the isolation of a plasmid containing baeR, which codes for the response regulator of BaeSR. When overexpressed, baeR significantly increased the resistance of the delta acrB strain to oxacillin, cloxacillin, and nafcillin. baeR overexpression conferred resistance to novobiocin and deoxycholate, as well as to beta-lactams in Salmonella. The increase in drug resistance caused by baeR overexpression was completely suppressed by deletion of the multifunctional outer membrane channel gene tolC. TolC interacts with different drug efflux systems. Among the nine drug efflux systems in Salmonella, quantitative real-time PCR analysis showed that BaeR induced the expression of acrD and mdtABC. Double deletion of these two genes completely suppressed BaeR-mediated multidrug resistance, whereas single deletion of either gene did not. The promoter regions of acrD and mdtABC harbor binding sites for the response regulator BaeR, which activates acrD and mdtABC transcription in response to indole, copper, and zinc. In addition to their role in multidrug resistance, we found that BaeSR, AcrD, and MdtABC contribute to copper and zinc resistance in Salmonella. Our results indicate that the BaeSR system increases multidrug and metal resistance in Salmonella by inducing the AcrD and MdtABC drug efflux systems. We found a previously uncharacterized physiological role for the AcrD and MdtABC multidrug efflux systems in metal resistance.

The RcsCDB signaling system and swarming motility in Salmonella enterica serovar typhimurium: dual regulation of flagellar and SPI-2 virulence genes

The Rcs phosphorelay is a multicomponent signaling system that positively regulates colanic acid synthesis and negatively regulates motility and virulence. We have exploited a spontaneously isolated mutant, IgaA(T191P), that is nearly maximally activated for the Rcs system to identify a vast set of genes that respond to the stimulation, and we report new regulatory properties of this signaling system in Salmonella enterica serovar Typhimurium. Microarray data show that the Rcs system normally functions as a positive regulator of SPI-2 and other genes important for the growth of Salmonella in macrophages, although when highly activated the system completely represses the SPI-1/SPI-2 virulence, flagellar, and fimbrial biogenesis pathways. The auxiliary protein RcsA, which works with RcsB to positively regulate colanic acid and other target genes, not only stimulates but also antagonizes the positive regulation of many genes in the igaA mutant. We show that RcsB represses motility through the RcsB box in the promoter region of the master operon flhDC and that RcsA is not required for this regulation. Curiously, RcsB selectively stimulates expression of the flagellar type 3 secretion genes fliPQR; an RcsAB box located downstream of fliR influences this regulation. We show that excess colanic acid impairs swimming and inhibits swarming motility, consistent with the inverse regulation of the two pathways by the Rcs system.

Induction of RpoS degradation by the two-component system regulator RstA in Salmonella enterica

Bacterial survival in diverse and changing environments relies on the accurate interplay between different regulatory pathways, which determine the design of an adequate adaptive response. The proper outcome depends on a precise gene expression profile generated from the finely tuned and concerted action of transcriptional factors of distinct regulatory hierarchies. Salmonella enterica serovar Typhimurium harbors multiple regulatory systems that are crucial for the bacterium to cope with harsh extra- and intracellular environments. In this work, we found that the expression of Salmonella RstA, a response regulator from the two-component system family, was able to downregulate the expression of three RpoS-controlled genes (narZ, spvA, and bapA). Furthermore, this downregulation was achieved by a reduction in RpoS cellular levels. The alternative sigma factor RpoS is critical for bacterial endurance under the most-stressful conditions, including stationary-phase entrance and host adaptation. Accordingly, RpoS cellular levels are tightly controlled by complex transcriptional, translational, and posttranslational mechanisms. The analysis of each regulatory step revealed that in Salmonella, RstA expression was able to promote RpoS degradation independently of the MviA-ClpXP proteolytic pathway. Additionally, we show that RstA is involved in modulating Salmonella biofilm formation. The fact that the RpoS-modulated genes affected by RstA expression have previously been demonstrated to contribute to Salmonella pathogenic traits, which include biofilm-forming capacity, suggests that under yet unknown conditions, RstA may function as a control point of RpoS-dependent pathways that govern Salmonella virulence.

Gene promoter scan methodology for identifying and classifying coregulated promoters

A critical challenge of the postgenomic era is to understand how genes are differentially regulated. Genetic and genomic approaches have been used successfully to assign genes to distinct regulatory networks in both prokaryotes and eukaryotes. However, little is known about what determines the differential expression of genes within a particular network, even when it involves a single transcription factor. The fact that coregulated genes may be differentially expressed suggests that subtle differences in the shared cis-acting regulatory elements are likely to be significant. This chapter describes a method, termed gene promoter scan (GPS), that discriminates among coregulated promoters by simultaneously considering a variety of cis-acting regulatory features. Application of this method to the PhoP/PhoQ two-component regulatory system of Escherichia coli and Salmonella enterica uncovered novel members of the PhoP regulon, as well as regulatory interactions that had not been discovered using previous approaches. The predictions made by GPS were validated experimentally to establish that the PhoP protein uses multiple mechanisms to control gene transcription and is a central element in a highly connected network.

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.