Networking genetic regulation and neural computation: directed network topology and its effect on the dynamics

Two Component Regulatory Systems and Antibiotic Resistance in Gram-Negative Pathogens

Gram-negative pathogens such as Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa are the leading cause of nosocomial infections throughout the world. One commonality shared among these pathogens is their ubiquitous presence, robust host-colonization and most importantly, resistance to antibiotics. A significant number of two-component systems (TCSs) exist in these pathogens, which are involved in regulation of gene expression in response to environmental signals such as antibiotic exposure. While the development of antimicrobial resistance is a complex phenomenon, it has been shown that TCSs are involved in sensing antibiotics and regulating genes associated with antibiotic resistance. In this review, we aim to interpret current knowledge about the signaling mechanisms of TCSs in these three pathogenic bacteria. We further attempt to answer questions about the role of TCSs in antimicrobial resistance. We will also briefly discuss how specific two-component systems present in K. pneumoniae, A. baumannii, and P. aeruginosa may serve as potential therapeutic targets.

Characterization of the Direct Interaction between Hybrid Sensor Kinases PA1611 and RetS That Controls Biofilm Formation and the Type III Secretion System in Pseudomonas aeruginosa

One of the leading causes of morbidity and mortality in cystic fibrosis (CF) patients is pulmonary infection with Pseudomonas aeruginosa, and the pathophysiology of pulmonary infection in CF is affected by the lifestyle of this micro-organism. RetS-GacS/A-RsmA is a key regulatory pathway in P. aeruginosa that determines the bacterium's lifestyle choice. Previously, we identified PA1611, a hybrid sensor kinase, as a new player in this pathway that interacts with RetS and influences biofilm formation and type III secretion system. In this study, we explored the structural and mechanistic basis of the interaction between PA1611 and RetS. We identified the amino acid residues critical for PA1611-RetS interactions by molecular modeling. These residues were then targeted for site-directed mutagenesis. Amino acid substitutions were carried out at seven key positions in PA1611 and at six corresponding key positions in RetS. The influence of such substitutions in PA1611 on the interaction was analyzed by bacterial two-hybrid assays. We carried out functional analysis of these mutants in P. aeruginosa for their effect on specific phenotypes. Two residues, F269 and E276, located within the histidine kinase A and histidine kinase-like ATPase domains of PA1611 were found to play crucial roles in the PA1611-RetS interaction and had profound effects on phenotypes. Corresponding mutations in RetS demonstrated similar results. We further confirmed that these mutations in PA1611 function through the GacS/GacA-RsmY/Z signaling pathway. Collectively, our findings provide a noncognate sensor kinase direct interaction model for a signaling pathway, key for lifestyle selection in P. aeruginosa, and targeting such interaction may serve as a novel way of controlling infections with P. aeruginosa.

Effect of database drift on network topology and enrichment analyses: a case study for RegulonDB

RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information. Database URL: www.regulondb.ccg.unam.mx.

Irreversible bimolecular chemical reactions on directed scale-free networks

Kinetics of irreversible bimolecular chemical reactions A+A→0 and A+B→0 on directed scale-free networks with the in-degree distribution P(in)(k)∼k(-γ)(in) and the out-degree distribution P(out)(ℓ)∼ℓ(-γ)(out) are investigated. Since the correlation between k and ℓ of each node generally exists in directed networks, we control the correlation (kℓ) with the probability r∈[0,1] by two different algorithms for the construction of the directed networks, i.e., the so-called k and ℓ algorithms. For r=1, the k algorithm gives (kℓ)=(k(2)), whereas the ℓ algorithm gives (kℓ)=(ℓ(2). For r=0, (kℓ)=(k)(ℓ) for both algorithms. The kinetics of both reactions are analyzed using heterogeneous mean-field (HMF) theory and Monte Carlo simulations. The density of particles (ρ) algebraically decays in time t as ρ(t)∼t(-α). The kinetics of both reactions are determined by the same rate equation, dρ/dt=aρ(2)+bρ(θ-1), apart from coefficients. The exponent θ is determined by the algorithm: θ=γ(in) for the k algorithm (r≥0) and θ=γ(min) for the ℓ algorithm (r>0), where γ(min) is the smaller exponent between γ(in) and γ(out). For θ>3, one observes the ordinary mean-field kinetics, ρ∼1/t (α=1). In contrast, for θ<3, ρ(t) anomalously decays with α=1/(θ-2). The HMF predictions are confirmed by the simulations on quenched directed networks.

Epidemic spreading in annealed directed networks: susceptible-infected-susceptible model and contact process

We investigate epidemic spreading in annealed directed scale-free networks with the in-degree (k) distribution P(in)(k)~k(-γ(in)) and the out-degree (ℓ) distribution, P(out)(ℓ)~ℓ(-γ(out)). The correlation <kl> of each node on the networks is controlled by the probability r(0≤r≤1) in two different algorithms, the so-called k and ℓ algorithms. For r=1, the k algorithm gives <kl>=<k(2)>, whereas the ℓ algorithm gives <kl>=<ℓ(2)>. For r=0, <kl>=<k><ℓ> for both algorithms. As the prototype of epidemic spreading, the susceptible-infected-susceptible model and contact process on the networks are analyzed using the heterogeneous mean-field theory and Monte Carlo simulations. The directedness of links and the correlation of the network are found to play important roles in the spreading, so that critical behaviors of both models are distinct from those on undirected scale-free networks.

An evolving model of undirected networks based on microscopic biological interaction systems

With protein or gene interaction systems as the background, this paper proposes an evolving model of biological undirected networks, which are consistent with some plausible mechanisms in biology. Through introducing a rule of preferential duplication of a node inversely proportional to the degree of existing nodes and an attribute of the age of the node (the older, the more influence), by which the probability of a node receiving re-wiring links is chosen, the model networks generated in certain parameter conditions could reproduce series of statistic topological characteristics of real biological graphs, including the scale-free feature, small world effect, hierarchical modularity, limited structural robustness, and disassortativity of degree-degree correlation.

Models and average properties of scale-free directed networks

We extend the merging model for undirected networks by Kim [Eur. Phys. J. B 43, 369 (2004)] to directed networks and investigate the emerging scale-free networks. Two versions of the directed merging model, friendly and hostile merging, give rise to two distinct network types. We uncover that some nontrivial features of these two network types resemble two levels of a certain randomization/nonspecificity in the link reshuffling during network evolution. Furthermore, the same features show up, respectively, in metabolic networks and transcriptional networks. We introduce measures that single out the distinguishing features between the two prototype networks, as well as point out features that are beyond the prototypes.

Dynamic behaviors in directed networks

Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks.