A semi-quantitative model of Quorum-Sensing in Staphylococcus aureus, approved by microarray meta-analyses and tested by mutation studies

Modeling of stringent-response reflects nutrient stress induced growth impairment and essential amino acids in different Staphylococcus aureus mutants

Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model (195 nodes, 320 edges) the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92% and quantitatively in 81%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus.

A Novel Aza-Derivative Inhibits agr Quorum Sensing Signaling and Synergizes Methicillin-Resistant Staphylococcus aureus to Clindamycin

Increasing antibiotic resistance and diminishing pharmaceutical industry investments have increased the need for molecules that can treat infections caused by dangerous pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). Quorum Sensing (QS) is a signaling mechanism that regulates bacterial virulence in pathogens. A report demonstrating that the anti-inflammatory drug Diflunisal reduces MRSA virulence factors' expression prompted us to design, synthesize and test 16 aza-analogs as inhibitors of S. aureus virulence factors controlled by the accessory gene regulator (agr) QS system. At first, we evaluated by qRT-PCR the activity of compounds on rnaIII expression, a QS related gene. Azan-7 was the most active molecule tested and it did not show cytotoxic activity in human cell lines. Moreover, we demonstrated that it did not affect bacterial proliferation. Regulation of MRSA virulence genes by Azan-7 was investigated using qRT-PCR and RNAseq. Azan-7 significantly reduced hla, psmα, hysA, agrA, cap1A, and cap1C gene expression. In silico docking demonstrated that Azan-7 binds the response regulator AgrA. This data was confirmed by electrophoretic mobility shift assay (EMSA) reporting that Azan-7 binding to AgrA protein strongly reduced the AgrA-DNA complex formation at the P3 promoter region involved in the regulation of rnaIII transcription. Azan-7 inhibited MRSA-mediated haemolysis, reduced survival of the pathogen at low pH levels, and increased macrophage killing. In addition, Azan-7 enhanced MRSA susceptibility to clindamycin both in planktonic growth and biofilm. Azan-7 did not induce resistance over 10 days in culture. It was equally active against all the AgrA MRSA subtypes encountered among clinical isolates, but it was not active against Staphylococcus epidermidis, although the AgrA proteins show an approximate 80% homology. These results demonstrate that Azan-7 inhibits the expression of MRSA virulence factors by interfering in the QS and synergizes MRSA biofilm with clindamycin, indicating the compound as a promising candidate for the treatment of MRSA infections.

Noncoding RNA

Regulatory RNAs, present in many bacterial genomes and particularly in pathogenic bacteria such as Staphylococcus aureus, control the expression of genes encoding virulence factors or metabolic proteins. They are extremely diverse and include noncoding RNAs (sRNA), antisense RNAs, and some 5' or 3' untranslated regions of messenger RNAs that act as sensors for metabolites, tRNAs, or environmental conditions (e.g., temperature, pH). In this review we focus on specific examples of sRNAs of S. aureus that illustrate how numerous sRNAs and associated proteins are embedded in complex networks of regulation. In addition, we discuss the CRISPR-Cas systems defined as an RNA-interference-like mechanism, which also exist in staphylococcal strains.

From Staphylococcus aureus gene regulation to its pattern formation

The focus of this paper is to develop a new partial differential equation model for the pattern formation of the human pathogen Staphylococcus aureus, starting from a newly developed model of selected gene regulation mechanisms. In our model, we do not only account for the bacteria densities and nutrient concentrations, but also for the quorum sensing and biofilm components, since they enable bacteria to coordinate their behavior and provide the environment in which the colony grows. To this end, we model the relevant gene regulation systems using ordinary differential equations and therefrom derive our evolution equations for quorum sensing and biofilm environment by time-scale arguments. Furthermore, we compare and validate our model and the corresponding simulation results with biological real data observations of Staphylococcus aureus mutant colony growth in the laboratory. We show that we are able to adequately display the qualitative biological features of pattern formation in selected mutants, using the parameter changes indicated by the gene regulation mechanisms.

A combined tissue-engineered/in silico signature tool patient stratification in lung cancer

Patient‐tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix‐based three‐dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR‐activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho‐arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho‐kinase array, proliferation, and apoptosis), we generated cell line‐specific in silico topologies and condition‐specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS‐mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus

A central question to biology is how pathogenic bacteria initiate acute or chronic infections. Here we describe a genetic program for cell-fate decision in the opportunistic human pathogen Staphylococcus aureus, which generates the phenotypic bifurcation of the cells into two genetically identical but different cell types during the course of an infection. Whereas one cell type promotes the formation of biofilms that contribute to chronic infections, the second type is planktonic and produces the toxins that contribute to acute bacteremia. We identified a bimodal switch in the agr quorum sensing system that antagonistically regulates the differentiation of these two physiologically distinct cell types. We found that extracellular signals affect the behavior of the agr bimodal switch and modify the size of the specialized subpopulations in specific colonization niches. For instance, magnesium-enriched colonization niches causes magnesium binding to S. aureusteichoic acids and increases bacterial cell wall rigidity. This signal triggers a genetic program that ultimately downregulates the agr bimodal switch. Colonization niches with different magnesium concentrations influence the bimodal system activity, which defines a distinct ratio between these subpopulations; this in turn leads to distinct infection outcomes in vitro and in an in vivo murine infection model. Cell differentiation generates physiological heterogeneity in clonal bacterial infections and helps to determine the distinct infection types.

While in hospital, patients can be unwittingly exposed to bacteria that can cause disease. These hospital-associated bacteria can lead to potentially life-threatening infections that may also complicate the treatment of the patients’ existing medical conditions. Staphylococcus aureus is one such bacterium, and it can cause several types of infection including pneumonia, blood infections and long-term infections of prosthetic devices.

It is thought that S. aureus is able to cause so many different types of infection because it is capable of colonizing distinct tissues and organs in various parts of the body. Understanding the biological processes that drive the different infections is crucial to improving how these infections are treated.

S. aureus lives either as an independent, free-swimming cell or as part of a community known as a biofilm. These different lifestyles dictate the type of infection the bacterium can cause, with free-swimming cells producing toxins that contribute to intense, usually short-lived, infections and biofilms promoting longer-term infections that are difficult to eradicate. However, it is not clear how a population of S. aureus cells chooses to adopt a particular lifestyle and whether there are any environmental signals that influence this decision.

Here, Garcia-Betancur et al. found that S. aureus populations contain small groups of cells that have already specialized into a particular lifestyle. These groups of cells collectively influence the choice made by other cells in the population. While both lifestyles will be represented in the population, environmental factors influence the numbers of cells that initially adopt each type of lifestyle, which ultimately affects the choice made by the rest of the population. For example, if the bacteria colonize a tissue or organ that contains high levels of magnesium ions, the population is more likely to form biofilms.

In the future, the findings of Garcia-Betancur et al. may help us to predict how an infection may develop in a particular patient, which may help to diagnose the infection more quickly and allow it to be treated more effectively.

Quorum-Sensing Systems in the Plant Growth-Promoting Bacterium Paraburkholderia phytofirmans PsJN Exhibit Cross-Regulation and Are Involved in Biofilm Formation

Quorum-sensing systems play important roles in host colonization and host establishment of Burkholderiales species. Beneficial Paraburkholderia species share a conserved quorum-sensing (QS) system, designated BraI/R, that controls different phenotypes. In this context, the plant growth-promoting bacterium Paraburkholderia phytofirmans PsJN possesses two different homoserine lactone QS systems BpI.1/R.1 and BpI.2/R.2 (BraI/R-like QS system). The BpI.1/R.1 QS system was previously reported to be important to colonize and produce beneficial effects in Arabidopsis thaliana plants. Here, we analyzed the temporal variations of the QS gene transcript levels in the wild-type strain colonizing plant roots. The gene expression patterns showed relevant differences in both QS systems compared with the wild-type strain in the unplanted control treatment. The gene expression data were used to reconstruct a regulatory network model of QS systems in P. phytofirmans PsJN, using a Boolean network model. Also, we examined the phenotypic traits and transcript levels of genes involved in QS systems, using P. phytofirmans mutants in homoserine lactone synthases genes. We observed that the BpI.1/R.1 QS system regulates biofilm formation production in strain PsJN and this phenotype was associated with the lower expression of a specific extracytoplasmic function sigma factor ecf26.1 gene (implicated in biofilm formation) in the bpI.1 mutant strain.

Staphylococcus aureus RNAIII and Its Regulon Link Quorum Sensing, Stress Responses, Metabolic Adaptation, and Regulation of Virulence Gene Expression

Staphylococcus aureus RNAIII is one of the main intracellular effectors of the quorum-sensing system. It is a multifunctional RNA that encodes a small peptide, and its noncoding parts act as antisense RNAs to regulate the translation and/or the stability of mRNAs encoding transcriptional regulators, major virulence factors, and cell wall metabolism enzymes. In this review, we explain how regulatory proteins and RNAIII are embedded in complex regulatory circuits to express virulence factors in a dynamic and timely manner in response to stress and environmental and metabolic changes.

Host-pathogen interactions between the human innate immune system and Candida albicans-understanding and modeling defense and evasion strategies

The diploid, polymorphic yeast Candida albicans is one of the most important human pathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within the human host for a long time. However, alterations in the host environment can render C. albicans virulent. In this review, we describe the immunological cross-talk between C. albicans and the human innate immune system. We give an overview in form of pairs of human defense strategies including immunological mechanisms as well as general stressors such as nutrient limitation, pH, fever etc. and the corresponding fungal response and evasion mechanisms. Furthermore, Computational Systems Biology approaches to model and investigate these complex interactions are highlighted with a special focus on game-theoretical methods and agent-based models. An outlook on interesting questions to be tackled by Systems Biology regarding entangled defense and evasion mechanisms is given.

A defense-offense multi-layered regulatory switch in a pathogenic bacterium

Cells adapt to environmental changes by efficiently adjusting gene expression programs. Staphylococcus aureus, an opportunistic pathogenic bacterium, switches between defensive and offensive modes in response to quorum sensing signal. We identified and studied the structural characteristics and dynamic properties of the core regulatory circuit governing this switch by deterministic and stochastic computational methods, as well as experimentally. This module, termed here Double Selector Switch (DSS), comprises the RNA regulator RNAIII and the transcription factor Rot, defining a double-layered switch involving both transcriptional and post-transcriptional regulations. It coordinates the inverse expression of two sets of target genes, immuno-modulators and exotoxins, expressed during the defensive and offensive modes, respectively. Our computational and experimental analyses show that the DSS guarantees fine-tuned coordination of the inverse expression of its two gene sets, tight regulation, and filtering of noisy signals. We also identified variants of this circuit in other bacterial systems, suggesting it is used as a molecular switch in various cellular contexts and offering its use as a template for an effective switching device in synthetic biology studies.

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model

For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre‐requisites are tumor models with multiple read‐out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three‐dimensional (3D) lung carcinoma models on a decellularized tissue matrix, providing a complex microenvironment for cell growth. For model generation, we used two cell lines with (HCC827) or without (A549) an activating mutation of the epidermal growth factor receptor (EGFR), exhibiting different sensitivities to the EGFR inhibitor gefitinib. EGFR activation in HCC827 was inhibited by gefitinib, resulting in a significant reduction of proliferation (Ki‐67 proliferation index) and in the induction of apoptosis (TUNEL staining, M30‐ELISA). No significant effect was observed in conventional cell culture. Results from the 3D model correlated with the results of an in silico model that integrates the EGFR signaling network according to clinical data. The application of TGFβ1 induced tumor cell invasion, accompanied by epithelial–mesenchymal transition (EMT) both in vitro and in silico. This was confirmed in the 3D model by acquisition of mesenchymal cell morphology and modified expression of fibronectin, E‐cadherin, β‐catenin and mucin‐1. Quantitative read‐outs for proliferation, apoptosis and invasion were established in the complex 3D tumor model. The combined in vitro and in silico model represents a powerful tool for systems analysis.

Combination of a human 3D lung tumor tissue model with a Boolean in silico Model.

Establishment of in silico signaling network topology for personalized medicine.

Significant decrease of tumor proliferation and induction of apoptosis upon in vitro treatment with tyrosine kinase inhibitors.

Decreased proliferation of tumor cells in the 3D model compared to 2D conditions.

Induction of invasion with EMT by TGFβ stimulation.