Identity-by-Descent Mapping to Detect Rare Variants Conferring Susceptibility to Multiple Sclerosis

Genome-wide association studies (GWAS) have identified around 60 common variants associated with multiple sclerosis (MS), but these loci only explain a fraction of the heritability of MS. Some missing heritability may be caused by rare variants that have been suggested to play an important role in the aetiology of complex diseases such as MS. However current genetic and statistical methods for detecting rare variants are expensive and time consuming. 'Population-based linkage analysis' (PBLA) or so called identity-by-descent (IBD) mapping is a novel way to detect rare variants in extant GWAS datasets. We employed BEAGLE fastIBD to search for rare MS variants utilising IBD mapping in a large GWAS dataset of 3,543 cases and 5,898 controls. We identified a genome-wide significant linkage signal on chromosome 19 (LOD = 4.65; p = 1.9×10-6). Network analysis of cases and controls sharing haplotypes on chromosome 19 further strengthened the association as there are more large networks of cases sharing haplotypes than controls. This linkage region includes a cluster of zinc finger genes of unknown function. Analysis of genome wide transcriptome data suggests that genes in this zinc finger cluster may be involved in very early developmental regulation of the CNS. Our study also indicates that BEAGLE fastIBD allowed identification of rare variants in large unrelated population with moderate computational intensity. Even with the development of whole-genome sequencing, IBD mapping still may be a promising way to narrow down the region of interest for sequencing priority. © 2013 Lin et al.

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.

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).

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Mutations in cardiac T-Box Factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy

The T-box family transcription factor gene TBX20 acts in a conserved regulatory network, guiding heart formation and patterning in diverse species. Mouse Tbx20 is expressed in cardiac progenitor cells, differentiating cardiomyocytes, and developing valvular tissue, and its deletion or RNA interference-mediated knockdown is catastrophic for heart development. TBX20 interacts physically, functionally, and genetically with other cardiac transcription factors, including NKX2-5, GATA4, and TBX5, mutations of which cause congenital heart disease (CHD). Here, we report nonsense (Q195X) and missense (I152M) germline mutations within the T-box DNA-binding domain of human TBX20 that were associated with a family history of CHD and a complex spectrum of developmental anomalies, including defects in septation, chamber growth, and valvulogenesis. Biophysical characterization of wild-type and mutant proteins indicated how the missense mutation disrupts the structure and function of the TBX20 T-box. Dilated cardiomyopathy was a feature of the TBX20 mutant phenotype in humans and mice, suggesting that mutations in developmental transcription factors can provide a sensitized template for adult-onset heart disease. Our findings are the first to link TBX20 mutations to human pathology. They provide insights into how mutation of different genes in an interactive regulatory circuit lead to diverse clinical phenotypes, with implications for diagnosis, genetic screening, and patient follow-up.

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.

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.

A mathematical model for genetic regulation of the lactose operon

In this paper we construct a mathematical model for the genetic regulatory network of the lactose operon. This mathematical model contains transcription and translation of the lactose permease (LacY) and a reporter gene GFP. The probability of transcription of LacY is determined by 14 binding states out of all 50 possible binding states of the lactose operon based on the quasi-steady-state assumption for the binding reactions, while we calculate the probability of transcription for the reporter gene GFP based on 5 binding states out of 19 possible binding states because the binding site O2 is missing for this reporter gene. We have tested different mechanisms for the transport of thio-methylgalactoside (TMG) and the effect of different Hill coefficients on the simulated LacY expression levels. Using this mathematical model we have realized one of the experimental results with different LacY concentrations, which are induced by different concentrations of TMG.

Failure to launch: The self-regulating Md -MYB10R6 gene from apple is active in flowers but not leaves of Petunia

Coloured foliage due to anthocyanin pigments (bronze/red/black) is an attractive trait that is often lacking in many bedding, ornamental and horticultural plants. Apples (Malus × domestica) containing an allelic variant of the anthocyanin regulator, Md-MYB10R6, are highly pigmented throughout the plant, due to autoregulation by MYB10 upon its own promoter. We investigated whether Md-MYB10R6 from apple is capable of functioning within the heterologous host Petunia hybrida to generate plants with novel pigmentation patterns. The Md-MYB10R6 transgene (MYB10–R6pro:MYB10:MYB10term) activated anthocyanin synthesis when transiently expressed in Antirrhinumroseadorsea petals and petunia leaf discs. Stable transgenic petunias containing Md-MYB10R6 lacked foliar pigmentation but had coloured flowers, complementing the an2 phenotype of ‘Mitchell’ petunia. The absence of foliar pigmentation was due to the failure of the Md-MYB10R6 gene to self-activate in vegetative tissues, suggesting that additional protein partners are required for Md-MYB10 to activate target genes in this heterologous system. In petunia flowers, where endogenous components including MYB-bHLH-WDR (MBW) proteins were present, expression of the Md-MYB10R6 promoter was initiated, allowing auto-regulation to occur and activating anthocyanin production. Md-MYB10 is capable of operating within the petunia MBW gene regulation network that controls the expression of the anthocyanin biosynthesis genes, AN1 (bHLH) and MYBx (R3-MYB repressor) in petals.

Proteomics Approaches in the Identification of Molecular Signatures of Mesenchymal Stem Cells

Mesenchymal stem cells (MSCs) are undifferentiated, multi-potent stem cells with the ability to renew. They can differentiate into many types of terminal cells, such as osteoblasts, chondrocytes, adipocytes, myocytes, and neurons. These cells have been applied in tissue engineering as the main cell type to regenerate new tissues. However, a number of issues remain concerning the use of MSCs, such as cell surface markers, the determining factors responsible for their differentiation to terminal cells, and the mechanisms whereby growth factors stimulate MSCs. In this chapter, we will discuss how proteomic techniques have contributed to our current knowledge and how they can be used to address issues currently facing MSC research. The application of proteomics has led to the identification of a special pattern of cell surface protein expression of MSCs. The technique has also contributed to the study of a regulatory network of MSC differentiation to terminal differentiated cells, including osteocytes, chondrocytes, adipocytes, neurons, cardiomyocytes, hepatocytes, and pancreatic islet cells. It has also helped elucidate mechanisms for growth factor–stimulated differentiation of MSCs. Proteomics can, however, not reveal the accurate role of a special pathway and must therefore be combined with other approaches for this purpose. A new generation of proteomic techniques have recently been developed, which will enable a more comprehensive study of MSCs. Keywords

DRB2 is required for microRNA biogenesis in Arabidopsis thaliana

Background The Arabidopsis thaliana (Arabidopsis) DOUBLE-STRANDED RNA BINDING (DRB) protein family consists of five members, DRB1 to DRB5. The biogenesis of two developmentally important small RNA (sRNA) species, the microRNAs (miRNAs) and trans-acting small interfering RNAs (tasiRNAs) by DICER-LIKE (DCL) endonucleases requires the assistance of DRB1 and DRB4 respectively. The importance of miRNA-directed target gene expression in plant development is exemplified by the phenotypic consequence of loss of DRB1 activity (drb1 plants). Principal Findings Here we report that the developmental phenotype of the drb235 triple mutant plant is the result of deregulated miRNA biogenesis in the shoot apical meristem (SAM) region. The expression of DRB2, DRB3 and DRB5 in wild-type seedlings is restricted to the SAM region. Small RNA sequencing of the corresponding tissue of drb235 plants revealed altered miRNA accumulation. Approximately half of the miRNAs detected remained at levels equivalent to those of wild-type plants. However, the accumulation of the remaining miRNAs was either elevated or reduced in the triple mutant. Examination of different single and multiple drb mutants revealed a clear association between the loss of DRB2 activity and altered accumulation for both the elevated and reduced miRNA classes. Furthermore, we show that the constitutive over-expression of DRB2 outside of its wild-type expression domain can compensate for the loss of DRB1 activity in drb1 plants. Conclusions/Significance Our results suggest that in the SAM region, DRB2 is both antagonistic and synergistic to the role of DRB1 in miRNA biogenesis, adding an additional layer of gene regulatory complexity in this developmentally important tissue.

PRMT2 and RORγ expression are associated with breast cancer survival outcomes

Protein arginine methyltransferases (PRMTs) methylate arginine residues on histones and target transcription factors that play critical roles in many cellular processes, including gene transcription, mRNA splicing, proliferation, and differentiation. Recent studies have linked PRMT-dependent epigenetic marks and modifications to carcinogenesis and metastasis in cancer. However, the role of PRMT2-dependent signaling in breast cancer remains obscure. We demonstrate PRMT2 mRNA expression was significantly decreased in breast cancer relative to normal breast. Gene expression profiling, Ingenuity and protein-protein interaction network analysis after PRMT2-short interfering RNA transfection into MCF-7 cells, revealed that PRMT2-dependent gene expression is involved in cell-cycle regulation and checkpoint control, chromosomal instability, DNA repair, and carcinogenesis. For example, PRMT2 depletion achieved the following: 1) increased p21 and decreased cyclinD1 expression in (several) breast cancer cell lines, 2) decreased cell migration, 3) induced an increase in nucleotide excision repair and homologous recombination DNA repair, and 4) increased the probability of distance metastasis free survival (DMFS). The expression of PRMT2 and retinoid-related orphan receptor-γ (RORγ) is inversely correlated in estrogen receptor-positive breast cancer and increased RORγ expression increases DMFS. Furthermore, we found decreased expression of the PRMT2-dependent signature is significantly associated with increased probability of DMFS. Finally, weighted gene coexpression network analysis demonstrated a significant correlation between PRMT2-dependent genes and cell-cycle checkpoint, kinetochore, and DNA repair circuits. Strikingly, these PRMT2-dependent circuits are correlated with pan-cancer metagene signatures associated with epithelial-mesenchymal transition and chromosomal instability. This study demonstrates the role and significant correlation between a histone methyltransferase (PRMT2)-dependent signature, RORγ, the cell-cycle regulation, DNA repair circuits, and breast cancer survival outcomes.

Identification and characterization of novel proteolytic interactions of prostate cancer-expressed kallikrein-related peptidases, type II transmembrane serine proteases and matrix metalloproteinases

This study investigated interactions of protein-cleaving enzymes (or proteases) that promote prostate cancer progression. It provides the first evidence of a novel regulatory network of protease activity at the surface of cells. The proteases kallikrein-related peptidases 4 and 14, and matrix metalloproteinases 3 and 9 are cleaved at the cell surface by the cell surface proteases hepsin and TMPRSS2. These cleavage events potentially regulate activation of downstream targets of kallikrein 4 and 14 such as cell surface signalling via the protease-activated receptors (PARs) and cell growth-promoting factors such as hepatocyte-growth factor.

EphB4 localises to the nucleus of prostate cancer cells

The EphB4 receptor tyrosine kinase is over-expressed in a variety of different epithelial cancers including prostate where it has been shown to be involved in survival, migration and angiogenesis. We report here that EphB4 also resides in the nucleus of prostate cancer cell lines. We used in silico methods to identify a bipartite nuclear localisation signal (NLS) in the extracellular domain and a monopartite NLS sequence in the intracellular kinase domain of EphB4. To determine whether both putative NLS sequences were functional, fragments of the EphB4 sequence containing each NLS were cloned to create EphB4NLS-GFP fusion proteins. Localisation of both NLS-GFP proteins to the nuclei of transfected cells was observed, demonstrating that EphB4 contains two functional NLS sequences. Mutation of the key amino residues in both NLS sequences resulted in diminished nuclear accumulation. As nuclear translocation is often dependent on importins we confirmed that EphB4 and importin-α can interact. To assess if nuclear EphB4 could be implicated in gene regulatory functions potential EphB4-binding genomic loci were identified using chromatin immunoprecipitation and Lef1 was confirmed as a potential target of EphB4-mediated gene regulation. These novel findings add further complexity to the biology of this important cancer-associated receptor.

Integrated genome-wide chromatin occupancy and expression analyses identify key myeloid pro-differentiation transcription factors repressed by Myb

To gain insight into the mechanisms by which the Myb transcription factor controls normal hematopoiesis and particularly, how it contributes to leukemogenesis, we mapped the genome-wide occupancy of Myb by chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) in ERMYB myeloid progenitor cells. By integrating the genome occupancy data with whole genome expression profiling data, we identified a Myb-regulated transcriptional program. Gene signatures for leukemia stem cells, normal hematopoietic stem/progenitor cells and myeloid development were overrepresented in 2368 Myb regulated genes. Of these, Myb bound directly near or within 793 genes. Myb directly activates some genes known critical in maintaining hematopoietic stem cells, such as Gfi1 and Cited2. Importantly, we also show that, despite being usually considered as a transactivator, Myb also functions to repress approximately half of its direct targets, including several key regulators of myeloid differentiation, such as Sfpi1 (also known as Pu.1), Runx1, Junb and Cebpb. Furthermore, our results demonstrate that interaction with p300, an established coactivator for Myb, is unexpectedly required for Myb-mediated transcriptional repression. We propose that the repression of the above mentioned key pro-differentiation factors may contribute essentially to Myb's ability to suppress differentiation and promote self-renewal, thus maintaining progenitor cells in an undifferentiated state and promoting leukemic transformation. © 2011 The Author(s).