Inverse bifurcation analysis: application to simple gene systems

BACKGROUND: Bifurcation analysis has proven to be a powerful method for understanding the qualitative behavior of gene regulatory networks. In addition to the more traditional forward problem of determining the mapping from parameter space to the space of model behavior, the inverse problem of determining model parameters to result in certain desired properties of the bifurcation diagram provides an attractive methodology for addressing important biological problems. These include understanding how the robustness of qualitative behavior arises from system design as well as providing a way to engineer biological networks with qualitative properties. RESULTS: We demonstrate that certain inverse bifurcation problems of biological interest may be cast as optimization problems involving minimal distances of reference parameter sets to bifurcation manifolds. This formulation allows for an iterative solution procedure based on performing a sequence of eigen-system computations and one-parameter continuations of solutions, the latter being a standard capability in existing numerical bifurcation software. As applications of the proposed method, we show that the problem of maximizing regions of a given qualitative behavior as well as the reverse engineering of bistable gene switches can be modelled and efficiently solved

Identical NR5A1 Missense Mutations in Two Unrelated 46,XX Individuals with Testicular Tissues

The role of monogenic mutations in the development of 46,XX testicular/ovotesticular disorders of sex development (DSD) remains speculative. Although mutations in NR5A1 are known to cause 46,XY gonadal dysgenesis and 46,XX ovarian insufficiency, such mutations have not been implicated in testicular development of 46,XX gonads. Here, we identified identical NR5A1 mutations in two unrelated Japanese patients with 46,XX testicular/ovotesticular DSD. The p.Arg92Trp mutation was absent from the clinically normal mothers and from 200 unaffected Japanese individuals. In silico analyses scored p.Arg92Trp as probably pathogenic. In vitro assays demonstrated that compared with wild‐type NR5A1, the mutant protein was less sensitive to NR0B1‐induced suppression on the SOX9 enhancer element. Other sequence variants found in the patients were unlikely to be associated with the phenotype. The results raise the possibility that specific mutations in NR5A1 underlie testicular development in genetic females

Chemotherapy induces Notch1-dependent MRP1 up-regulation, inhibition of which sensitizes breast cancer cells to chemotherapy

Background Multi-drug Resistance associated Protein-1 (MRP1) can export chemotherapeutics from cancer cells and is implicated in chemoresistance, particularly as is it known to be up-regulated by chemotherapeutics. Our aims in this study were to determine whether activation of Notch signalling is responsible for chemotherapy-induced MRP1 expression Notch in breast cancers, and whether this pathway can be manipulated with an inhibitor of Notch activity. Methods MRP1 and Notch1 were investigated in 29 patients treated with neoadjuvant chemotherapy (NAC) for breast cancer, using immunohistochemistry on matched biopsy (pre-NAC) and surgical samples (post-NAC). Breast epithelial cell cultures (T47D, HB2) were treated with doxorubicin in the presence and absence of functional Notch1, and qPCR, siRNA, Western blots, ELISAs and flow-cytometry were used to establish interactions. Results In clinical samples, Notch1 was activated by neoadjuvant chemotherapy (Wilcoxon signed-rank p < 0.0001) and this correlated with induction of MRP1 expression (rho = 0.6 p = 0.0008). In breast cell lines, doxorubicin induced MRP1 expression and function (non-linear regression p < 0.004). In the breast cancer line T47D, doxorubicin activated Notch1 and, critically, inhibition of Notch1 activation with the γ-secretase inhibitor DAPT abolished the doxorubicin-induced increase in MRP1 expression and function (t-test p < 0.05), resulting in enhanced cellular retention of doxorubicin and increased doxorubicin-induced apoptosis (t-test p = 0.0002). In HB2 cells, an immortal but non-cancer derived breast cell line, Notch1-independent MRP1 induction was noted and DAPT did not enhance doxorubicin-induced apoptosis. Conclusions Notch inhibitors may have potential in sensitizing breast cancer cells to chemotherapeutics and therefore in tackling chemoresistance

Multi-level engineering facilitates the production of phenylpropanoid compounds in tomato

Phenylpropanoids comprise an important class of plant secondary metabolites. A number of transcription factors have been used to upregulate-specific branches of phenylpropanoid metabolism, but by far the most effective has been the fruit-specific expression of AtMYB12 in tomato, which resulted in as much as 10% of fruit dry weight accumulating as flavonols and hydroxycinnamates. We show that AtMYB12 not only increases the demand of flavonoid biosynthesis but also increases the supply of carbon from primary metabolism, energy and reducing power, which may fuel the shikimate and phenylalanine biosynthetic pathways to supply more aromatic amino acids for secondary metabolism. AtMYB12 directly binds promoters of genes encoding enzymes of primary metabolism. The enhanced supply of precursors, energy and reducing power achieved by AtMYB12 expression can be harnessed to engineer high levels of novel phenylpropanoids in tomato fruit, offering an effective production system for bioactives and other high value ingredients

Nucleosome-coupled expression differences in closely-related species

<p>Abstract</p> <p>Background</p> <p>Genome-wide nucleosome occupancy is negatively related to the average level of transcription factor motif binding based on studies in yeast and several other model organisms. The degree to which nucleosome-motif interactions relate to phenotypic changes across species is, however, unknown.</p> <p>Results</p> <p>We address this challenge by generating nucleosome positioning and cell cycle expression data for <it>Saccharomyces bayanus </it>and show that differences in nucleosome occupancy reflect cell cycle expression divergence between two yeast species, <it>S. bayanus </it>and <it>S. cerevisiae</it>. Specifically, genes with nucleosome-depleted MBP1 motifs upstream of their coding sequence show periodic expression during the cell cycle, whereas genes with nucleosome-shielded motifs do not. In addition, conserved cell cycle regulatory motifs across these two species are more nucleosome-depleted compared to those that are not conserved, suggesting that the degree of conservation of regulatory sites varies, and is reflected by nucleosome occupancy patterns. Finally, many changes in cell cycle gene expression patterns across species can be correlated to changes in nucleosome occupancy on motifs (rather than to the presence or absence of motifs).</p> <p>Conclusions</p> <p>Our observations suggest that alteration of nucleosome occupancy is a previously uncharacterized feature related to the divergence of cell cycle expression between species.</p

Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

<p>Abstract</p> <p>Background</p> <p>Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.</p> <p>Results</p> <p>To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.</p> <p>To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.</p> <p>Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.</p> <p>Conclusion</p> <p>Based on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.</p

Evolving Sensitivity Balances Boolean Networks

We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks

m^6A RNA methylation promotes XIST-mediated transcriptional repression

The long non-coding RNA X-inactive specific transcript (XIST) mediates the transcriptional silencing of genes on the X chromosome. Here we show that, in human cells, XIST is highly methylated with at least 78 N^6-methyladenosine (m^6A) residues—a reversible base modification of unknown function in long non-coding RNAs. We show that m^6A formation in XIST, as well as in cellular mRNAs, is mediated by RNA-binding motif protein 15 (RBM15) and its paralogue RBM15B, which bind the m^6A-methylation complex and recruit it to specific sites in RNA. This results in the methylation of adenosine nucleotides in adjacent m^6A consensus motifs. Furthermore, we show that knockdown of RBM15 and RBM15B, or knockdown of methyltransferase like 3 (METTL3), an m^6A methyltransferase, impairs XIST-mediated gene silencing. A systematic comparison of m^6A-binding proteins shows that YTH domain containing 1 (YTHDC1) preferentially recognizes m^6A residues on XIST and is required for XIST function. Additionally, artificial tethering of YTHDC1 to XIST rescues XIST-mediated silencing upon loss of m^6A. These data reveal a pathway of m^6A formation and recognition required for XIST-mediated transcriptional repression

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

One of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases