A polymorphism in the dysbindin gene (DTNBP1) associated with multiple psychiatric disorders including schizophrenia

Background A number of studies have found associations between dysbindin (DTNBP1) polymorphisms and schizophrenia. Recently we identified a DTNBP1 SNP (rs9370822) that is strongly associated with schizophrenia. Individuals diagnosed with schizophrenia were nearly three times as likely to carry the CC genotype compared to the AA genotype. Methods To investigate the importance of this SNP in the function of DTNBP1, a number of psychiatric conditions including addictive behaviours and anxiety disorders were analysed for association with rs9370822. Results The DTNBP1 polymorphism was significantly associated with post-traumatic stress disorder (PTSD) as well as nicotine and opiate dependence but not alcohol dependence. Individuals suffering PTSD were more than three times as likely to carry the CC genotype compared to the AA genotype. Individuals with nicotine or opiate dependence were more than twice as likely to carry the CC genotype compared to the AA genotype. Conclusions This study provides further support for the importance of DTNBP1 in psychiatric conditions and suggests that there is a common underlying molecular defect involving DTNBP1 that contributes to the development of several anxiety and addictive disorders that are generally recognised as separate clinical conditions. These disorders may actually be different expressions of a single metabolic pathway perturbation. As our participant numbers are limited our observations should be viewed with caution until they are independently replicated.

Review of regulatory framework for environmental information

If Australian scientists are to fully and actively participate in international scientific collaborations utilising online technologies, policies and laws must support the data access and reuse objectives of these projects. To date Australia lacks a comprehensive policy and regulatory framework for environmental information and data generally. Instead there exists a series of unconnected Acts that adopt historically-based, sector-specific approaches to the collection, use and reuse of environmental information. This paper sets out the findings of an analysis of a representative sample of Australian statutes relating to environmental management and protection to determine the extent to which they meet best practice criteria for access to and reuse of environmental information established in international initiatives. It identifies issues that need to be addressed in the legislation governing environmental information to ensure that Australian scientists are able to fully engage in international research collaborations.

Highly discriminatory single-nucleotide polymorphism interrogation of Escherichia coli by use of allele-specific real-time PCR and eBURST analysis

In total, 782 Escherichia coli strains originating from various host sources have been analyzed in this study by using a highly discriminatory single-nucleotide polymorphism (SNP) approach. A set of eight SNPs, with a discrimination value (Simpson's index of diversity [D]) of 0.96, was determined using the Minimum SNPs software, based on sequences of housekeeping genes from the E. coli multilocus sequence typing (MLST) database. Allele-specific real-time PCR was used to screen 114 E. coli isolates from various fecal sources in Southeast Queensland (SEQ). The combined analysis of both the MLST database and SEQ E. coli isolates using eight high-D SNPs resolved the isolates into 74 SNP profiles. The data obtained suggest that SNP typing is a promising approach for the discrimination of host-specific groups and allows for the identification of human-specific E. coli in environmental samples. However, a more diverse E. coli collection is required to determine animal- and environment-specific E. coli SNP profiles due to the abundance of human E. coli strains (56%) in the MLST database.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Streptococcus pneumoniae infection suppresses allergic airways disease by inducing regulatory T cells

An inverse association exists between some bacterial infections and the prevalence of asthma. We investigated whether Streptococcus pneumoniae infection protects against asthma using mouse models of ovalbumin (OVA)-induced allergic airway disease (AAD). Mice were intratracheally infected or treated with killed S. pneumoniae before, during or after OVA sensitisation and subsequent challenge. The effects of S. pneumoniae on AAD were assessed. Infection or treatment with killed S. pneumoniae suppressed hallmark features of AAD, including antigen-specific T-helper cell (Th) type 2 cytokine and antibody responses, peripheral and pulmonary eosinophil accumulation, goblet cell hyperplasia, and airway hyperresponsiveness. The effect of infection on the development of specific features of AAD depended on the timing of infection relative to allergic sensitisation and challenge. Infection induced significant increases in regulatory T-cell (Treg) numbers in lymph nodes, which correlated with the degree of suppression of AAD. Tregs reduced T-cell proliferation and Th2 cytokine release. The suppressive effects of infection were reversed by anti-CD25 treatment. Respiratory infection or treatment with S. pneumoniae attenuates allergic immune responses and suppresses AAD. These effects may be mediated by S. pneumoniae-induced Tregs. This identifies the potential for the development of therapeutic agents for asthma from S. pneumoniae.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the λ 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.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

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 performing as stochastic switches

Recent studies have shown that small genetic regulatory networks (GRNs) can be evolved in silico displaying certain dynamics in the underlying mathematical model. It is expected that evolutionary approaches can help to gain a better understanding of biological design principles and assist in the engineering of genetic networks. To take the stochastic nature of GRNs into account, our evolutionary approach models GRNs as biochemical reaction networks based on simple enzyme kinetics and simulates them by using Gillespie’s stochastic simulation algorithm (SSA). We have already demonstrated the relevance of considering intrinsic stochasticity by evolving GRNs that show oscillatory dynamics in the SSA but not in the ODE regime. Here, we present and discuss first results in the evolution of GRNs performing as stochastic switches.