Regulatory networks of Neisseria meningitidis and their implications for pathogenesis

Neisseria meningitidis, the leading cause of bacterial meningitis, can adapt to different host niches during human infection. Both transcriptional and post-transcriptional regulatory networks have been identified as playing a crucial role for bacterial stress responses and virulence. We investigated the N. meningitidis transcriptional landscape both by microarray and by RNA sequencing (RNAseq). Microarray analysis of N. meningitidis grown in the presence or absence of glucose allowed us to identify genes regulated by carbon source availability. In particular, we identified a glucose-responsive hexR-like transcriptional regulator in N. meningitidis. Deletion analysis showed that the hexR gene is accountable for a subset of the glucose-responsive regulation, and in vitro assays with the purified protein showed that HexR binds to the promoters of the central metabolic operons of meningococcus, by targeting a DNA region overlapping putative regulatory sequences. Our results indicate that HexR coordinates the central metabolism of meningococcus in response to the availability of glucose, and N. meningitidis strains lacking the hexR gene are also deficient in establishing successful bacteremia in a mouse model of infection. In parallel, RNAseq analysis of N. meningitidis cultured under standard or iron-limiting in vitro growth conditions allowed us to identify novel small non-coding RNAs (sRNAs) potentially involved in N. meningitidis regulatory networks. Manual curation of the RNAseq data generated a list of 51 sRNAs, 8 of which were validated by Northern blotting. Deletion of selected sRNAs caused attenuation of N. meningitidis infection in a murine model, leading to the identification of the first sRNAs influencing meningococcal bacteraemia. Furthermore, we describe the identification and initial characterization of a novel sRNA unique to meningococcus, closely associated to genes relevant for the intracellular survival of pathogenic Neisseriae. Taken together, our findings could help unravel the regulation of N. meningitidis adaptation to the host environment and its implications for pathogenesis.

bZIP transcription factors in the regulatory networks controlling seed maturation and germination of Arabidopsis thaliana

Se describe la expresión por RTqPCR de los genes que codifican los factores transcripcionales bZIP44 y bZIP9. Asimismo se establece la interacción entre ambas proteínas en el sistema de 2 híbridos de levadura y in planta por complementación bimolecular fluorescente.

Intrinsic noise in gene regulatory networks

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Evolving genetic regulatory networks using an artificial genome

Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, but collapse to a point attractor when updated asynchronously. Using a hill climbing algorithm the networks are then evolved using a fitness function which rewards patterns of gene expression which revisit as many previously seen states as possible. The final networks exhibit “fuzzy limit cycle” dynamics when updated asynchronously.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

Bayesian Methods for Inferring Gene Regulatory Networks

Thesis (Ph.D.)--University of Washington, 2016-08

Host cellular regulatory networks in dengue virus-human interactions

Dengue fever is one of the most important mosquito-borne diseases worldwide and is caused by infection with dengue virus (DENV). The disease is endemic in tropical and sub-tropical regions and has increased remarkably in the last few decades. At present, there is no antiviral or approved vaccine against the virus. Treatment of dengue patients is usually supportive, through oral or intravenous rehydration, or by blood transfusion for more severe dengue cases. Infection of DENV in humans and mosquitoes involves a complex interplay between the virus and host factors. This results in regulation of numerous intracellular processes, such as signal transduction and gene transcription which leads to progression of disease. To understand the mechanisms underlying the disease, the study of virus and host factors is therefore essential and could lead to the identification of human proteins modulating an essential step in the virus life cycle. Knowledge of these human proteins could lead to the discovery of potential new drug targets and disease control strategies in the future. Recent advances of high throughput screening technologies have provided researchers with molecular tools to carry out investigations on a large scale. Several studies have focused on determination of the host factors during DENV infection in human and mosquito cells. For instance, a genome-wide RNA interference (RNAi) screen has identified host factors that potentially play an important role in both DENV and West Nile virus replication (Krishnan et al. 2008). In the present study, a high-throughput yeast two-hybrid screen has been utilised in order to identify human factors interacting with DENV non-structural proteins. From the screen, 94 potential human interactors were identified. These include proteins involved in immune signalling regulation, potassium voltage-gated channels, transcriptional regulators, protein transporters and endoplasmic reticulum-associated proteins. Validation of fifteen of these human interactions revealed twelve of them strongly interacted with DENV proteins. Two proteins of particular interest were selected for further investigations of functional biological systems at the molecular level. These proteins, including a nuclear-associated protein BANP and a voltage-gated potassium channel Kv1.3, both have been identified through interaction with the DENV NS2A. BANP is known to be involved in NF-kB immune signalling pathway, whereas, Kv1.3 is known to play an important role in regulating passive flow of potassium ions upon changes in the cell transmembrane potential. This study also initiated a construction of an Aedes aegypti cDNA library for use with DENV proteins in Y2H screen. However, several issues were encountered during the study which made the library unsuitable for protein interaction analysis. In parallel, innate immune signalling was also optimised for downstream analysis. Overall, the work presented in this thesis, in particular the Y2H screen provides a number of human factors potentially targeted by DENV during infection. Nonetheless, more work is required to be done in order to validate these proteins and determine their functional properties, as well as testing them with infectious DENV to establish a biological significance. In the long term, data from this study will be useful for investigating potential human factors for development of antiviral strategies against dengue.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

International audience

Evolution of developmental gene regulatory networks in echinoids

Developmental gene regulatory networks (dGRNs) are assemblages of regulatory genes that direct embryonic development of animal body plans and their morpho-logical structures. dGRNs exhibit recursively-wired circuitry that is encoded in the genome and executed during development. Alteration to the regulatory architecture of dGRNs causes variation in developmental programs both during the development of an individual organism and during the evolution of an individual lineage. The ex-planatory power of these networks is best exemplified by the global dGRN directing early development of the euechinoid sea urchin Strongylocentrotus purpuratus. This network consists of numerous regulatory genes engaging in hundreds of genomic regulatory transactions that collectively direct the delineation of early embryonic domains and the specification of cell lineages. Research on closely-related euechi-noid sea urchins, e.g. Lytechinus variegatus and Paracentrotus lividus, has revealed marked conservation of dGRN architecture in echinoid development, suggesting little appreciable alteration has occurred since their divergence in evolution at least 90 million years ago (mya).

We sought to test whether this observation extends to all sea urchins (echinoids) and undertook a systematic analysis of over 50 regulatory genes in the cidaroid sea urchin Eucidaris tribuloides, surveing their regulatory activity and function in a sea urchin that diverged from euechinoid sea urchins at least 268 mya. Our results revealed extensive alterations have occurred to all levels of echinoid dGRN archi-tecture since the cidaroid-euechinoid divergence. Alterations to mesodermal sub-circuits were particularly striking, including functional di˙erences in specification of non-skeletogenic mesenchyme (NSM), skeletogenic mesenchyme (SM), and en-domesodermal segregation. Specification of endomesodermal embryonic domains revealed that, while their underlying network circuitry had clearly diverged, regu-latory states established in pregastrular embryos of these two groups are strikingly similar. Analyses of E. tribuloides specification leading to the estab-lishment of dorsal-ventral (aboral-oral) larval polarity indicated that regulation of regulatory genes expressed in mesodermal embryonic domains had incurred significantly more alterations than those expressed in endodermal and ectodermal domains. Taken together, this study highlights the ability of dGRN architecture to buffer extensive alterations in the evolution and early development of echinoids and adds further support to the notion that alterations can occur at all levels of dGRN architecture and all stages of embryonic development.