Genetic regulatory networks in avian B cells

Biology is turning into an information science. The science of systems biology seeks to understand the genetic networks that govern organism development and functions. In this study the chicken was used as a model organism in the study of B cell regulatory factors. These studies open new avenues for plasma cell research by connecting the down regulation of the B cell gene expression program directly to the initiation of plasma cell differentiation. The unique advantages of the DT40 avian B cell model system, specifically its high homologous recombination rate, were utilized to study gene regulation in Pax5 knock out cell lines and to gain new insights into the B cell to plasma cell transitions that underlie the secretion of antibodies as part of the adaptive immune response. The Pax5 transcription factor is central to the commitment, development and maintenance of the B cell phenotype. Mice lacking the Pax5 gene have an arrest in development at the pro-B lymphocyte stage while DT40 cells have been derived from cells at a more mature stage of development. The DT40 Pax5-/- cells exhibited gene expression similarities with primary chicken plasma cells. The expression of the plasma cell transcription factors Blimp-1 and XBP-1 were significantly upregulated while the expression of the germinal centre factor BCL6 was diminished in Pax5-/- cells, and this alteration was normalized by Pax5 re-introduction. The Pax5-deficient cells further manifested substantially elevated secretion of IgM into the supernatant, another characteristic of plasma cells. These results for the first time indicated that the downregulation of the Pax5 gene in B cells promotes plasma cell differentiation. Cross-species meta-analysis of chicken and mouse Pax5 gene knockout studies uncovers genes and pathways whose regulatory relationship to Pax5 has remained unchanged for over 300 million years. Restriction of the hematopoietic stem cell fate to produce T, B and NK cell lineages is dependent on the Ikaros and its molecular partners, the closely related Helios and Aiolos. Ikaros family members are zinc finger proteins which act as transcriptional repressors while helping to activate lymphoid genes. Helios in mice is expressed from the hematopoietic stem cell level onwards, although later in development its expression seems to predominate in the T cell lineage. This study establishes the emergence and sequence of the chicken Ikaros family members. Helios expression in the bursa of Fabricius, germinal centres and B cell lines suggested a role for Helios in the avian B-cell lineage, too. Phylogenetic studies of the Ikaros family connect the expansion of the Ikaros family, and thus possibly the emergence of the adaptive immune system, with the second round of genome duplications originally proposed by Ohno. Paralogs that have arisen as a result of genome-wide duplications are sometimes termed ohnologs – Ikaros family proteins appear to fit that definition. This study highlighted the opportunities afforded by the genome sequencing efforts and somatic cell reverse genetics approaches using the DT40 cell line. The DT40 cell line and the avian model system promise to remain a fruitful model for mechanistic insight in the post-genomic era as well.

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.

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.

Bistability and switching in the lysis/lysogeny genetic regulatory network of bacteriophage lambda

Bistability and switching are two important aspects of the genetic regulatory network of phage. Positive and negative feedbacks are key regulatory mechanisms in this network. By the introduction of threshold values, the developmental pathway of A phage is divided into different stages. If the protein level reaches a threshold value, positive or negative feedback will be effective and regulate the process of development. Using this regulatory mechanism, we present a quantitative model to realize bistability and switching of phage based on experimental data. This model gives descriptions of decisive mechanisms for different pathways in induction. A stochastic model is also introduced for describing statistical properties of switching in induction. A stochastic degradation rate is used to represent intrinsic noise in induction for switching the system from the lysogenic pathway to the lysis pathway. The approach in this paper represents an attempt to describe the regulatory mechanism in genetic regulatory network under the influence of intrinsic noise in the framework of continuous models. (C) 2003 Elsevier Ltd. All rights reserved.

Genetic Susceptibility Loci in Rheumatoid Arthritis Establish Transcriptional Regulatory Networks with Other Genes

Linkage studies have identified the human leukocyte antigen (HLA)-DRB1 as a putative rheumatoid arthritis (RA) susceptibility locus (SL). Nevertheless, it was estimated that its contribution was partial, suggesting that other non-HLA genes may play a role in RA susceptibility. To test this hypothesis, we conducted microarray transcription profiling of peripheral blood mononuclear cells in 15 RA patients and analyzed the data, using bioinformatics programs (significance analysis of microarrays method and GeneNetwork), which allowed us to determine the differentially expressed genes and to reconstruct transcriptional networks. The patients were grouped according to disease features or treatment with tumor necrosis factor blocker. Transcriptional networks that were reconstructed allowed us to identify the interactions occurring between RA SL and other genes, for example, HLA-DRB1 interacting with FNDC3A (fibronectin type III domain containing 3A). Given that fibronectin fragments can stimulate mediators of matrix and cartilage destruction in RA, this interaction is of special interest and may contribute to a clearer understanding of the functional role of HLA-DRB1 in RA pathogenesis.

Genes that code for T cell signaling proteins establish transcriptional regulatory networks during thymus ontogeny

Gene expression profiling by cDNA microarrays during murine thymus ontogeny has contributed to dissecting the large-scale molecular genetics of T cell maturation. Gene profiling, although useful for characterizing the thymus developmental phases and identifying the differentially expressed genes, does not permit the determination of possible interactions between genes. In order to reconstruct genetic interactions, on RNA level, within thymocyte differentiation, a pair of microarrays containing a total of 1,576 cDNA sequences derived from the IMAGE MTB library was applied on samples of developing thymuses (14-17 days of gestation). The data were analyzed using the GeneNetwork program. Genes that were previously identified as differentially expressed during thymus ontogeny showed their relationships with several other genes. The present method provided the detection of gene nodes coding for proteins implicated in the calcium signaling pathway, such as Prrg2 and Stxbp3, and in protein transport toward the cell membrane, such as Gosr2. The results demonstrate the feasibility of reconstructing networks based on cDNA microarray gene expression determinations, contributing to a clearer understanding of the complex interactions between genes involved in thymus/thymocyte development.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Cognate microRNA-Gene Regulatory Networks Mediating Glioblastoma Oncogenic Properties

Malignant gliomas, including the most common and fatal form glioblastoma (GBM, WHO grade IV astrocytoma), remain a challenge to treat. In the United States and Europe, more than 30,000 patients per year are newly diagnosed with GBM. Despite ongoing trials, the best currently available multimodal treatment approaches include surgical resection followed by concomitant and adjuvant radiation (RT) and temozolomide (TMZ) therapy, resulting in a low median overall survival (OS) rate ranging from 12.2 - 15.9 months. The important role of genetic and epigenetic changes in DNA, RNA, and protein alteration as well as epigenetic changes secondary to the tumor microenvironment and outside selection pressure (therapeutic interventions), are increasingly being recognized. In GBM treatment, the focus is shifting toward a more patient-centered (personalized) therapy. In this regard, in particular, microRNAs are being increasingly studied. MicroRNAs are non¬protein coding small RNAs that serve as negative gene regulators by binding to a specific sequence in the promoter region of a target gene, thus regulating gene expression. A single microRNA potentially targets hundreds of genes; thus, microRNAs and their cognate target genes have important roles as tumor suppressors and oncogenes as well as regulators of various cancer- specific cellular features, such as proliferation, apoptosis, invasion, and metastasis. The identification of distinct microRNA-gene regulatory networks in GBM patients can be expected to provide novel therapeutic insights by identifying candidate patients for targeted therapies. To this end, in this work we identified and validated clinically relevant and meaningful novel gene- microRNA regulatory networks that correlated with MR tumor phenotypes, histopathology, and patient survival and response rates to therapy. - Le traitement des gliomes malins, y compris sous leur forme la plus commune et meurtrière, le glioblastome (GBM, ou astrocytome de grade IV selon l'OMS), demeure à ce jour un défi. Aux États-Unis et en Europe, un nouveau diagnostic de GBM est prononcé dans plus de 30Ό00 cas par an. En dépit de tests en cours, les meilleures approches thérapeutiques combinées actuellement disponibles comprennent la résection chirurgicale de la tumeur, suivie d'une radiothérapie adjuvante ainsi que d'un traitement au temozolomide (RT/TMZ), thérapies dont résulte une médiane de survie globale basse (overall survival, OS), comprise entre 12.2 et 15.9 mois. On reconnaît de plus en plus le rôle majeur de l'ADN, de l'ARN et de l'altération des protéines ainsi que des modifications épigénétiques, secondaires par rapport au microenvironnement de la tumeur et à la pression de sélection extérieure (les interventions thérapeutiques). Dans le traitement du GBM, le centre d'intérêt se déplace vers une thérapie centrée sur le cas individuel du patient. Dans ce but, en particulier les microARN sont de plus en plus analysés. Les microARN sont de petits ARN non-codants (les protéines) qui servent de régulateurs négatifs de gènes en s'attachant à une séquence spécifique dans la région promotrice d'un gène-cible, régulant ainsi l'expression du gène. Un seul microARN cible potentiellement des centaines de gènes; on a ainsi découvert que les microARN et leurs gènes-cibles apparentés ont une fonction importante en tant que suppresseurs de tumeurs et d'oncogènes, ainsi que comme régulateurs de diverses caractéristiques cellulaires spécifiques du cancer, comme la prolifération, l'apoptose, l'invasion et la métastase. On peut s'attendre à ce que l'identification de réseaux microARN régulateurs de gènes, distincts selon les patients de GBM, fournisse une approche thérapeutique inédite par la détermination des patients susceptibles de réagir favorablement à des thérapies ciblées.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

Mutational activation of niche-specific genes provides insight into regulatory networks and bacterial function in a complex environment

The genome of the plant-colonizing bacterium Pseudomonas fluorescens SBW25 harbors a subset of genes that are expressed specifically on plant surfaces. The function of these genes is central to the ecological success of SBW25, but their study poses significant challenges because no phenotype is discernable in vitro. Here, we describe a genetic strategy with general utility that combines suppressor analysis with IVET (SPyVET) and provides a means of identifying regulators of niche-specific genes. Central to this strategy are strains carrying operon fusions between plant environment-induced loci (EIL) and promoterless 'dapB. These strains are prototrophic in the plant environment but auxotrophic on laboratory minimal medium. Regulatory elements were identified by transposon mutagenesis and selection for prototrophs on minimal medium. Approximately 106 mutants were screened for each of 27 strains carrying 'dapB fusions to plant EIL and the insertion point for the transposon determined in approximately 2,000 putative regulator mutants. Regulators were functionally characterized and used to provide insight into EIL phenotypes. For one strain carrying a fusion to the cellulose-encoding wss operon, five different regulators were identified including a diguanylate cyclase, the flagella activator, FleQ, and alginate activator, AmrZ (AlgZ). Further rounds of suppressor analysis, possible by virtue of the SPyVET strategy, revealed an additional two regulators including the activator AlgR, and allowed the regulatory connections to be determined.

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.