The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

β1-Syntrophin Modulation by miR-222 in mdx Mice

Background: In mdx mice, the absence of dystrophin leads to the deficiency of other components of the dystrophin-glycoprotein complex (DAPC), making skeletal muscle fibers more susceptible to necrosis. The mechanisms involved in the disappearance of the DAPC are not completely understood. The muscles of mdx mice express normal amounts of mRNA for the DAPC components, thus suggesting post-transcriptional regulation. Methodology/Principal Findings: We investigated the hypothesis that DAPC reduction could be associated with the microRNA system. Among the possible microRNAs (miRs) found to be upregulated in the skeletal muscle tissue of mdx compared to wt mice, we demonstrated that miR-222 specifically binds to the 3′-UTR of β1-syntrophin and participates in the downregulation of β1-syntrophin. In addition, we documented an altered regulation of the 3′-UTR of β1-syntrophin in muscle tissue from dystrophic mice. Conclusion/Significance: These results show the importance of the microRNA system in the regulation of DAPC components in dystrophic muscle, and suggest a potential role of miRs in the pathophysiology of dystrophy. © 2010 De Arcangelis et al

Comparative Analyses of SUV420H1 Isoforms and SUV420H2 Reveal Differences in Their Cellular Localization and Effects on Myogenic Differentiation

Methylation of histone H4 on lysine 20 plays critical roles in chromatin structure and function via mono- (H4K20me1), di- (H4K20me2), and trimethyl (H4K20me3) derivatives. In previous analyses of histone methylation dynamics in mid-gestation mouse embryos, we documented marked changes in H4K20 methylation during cell differentiation. These changes were particularly robust during myogenesis, both in vivo and in cell culture, where we observed a transition from H4K20me1 to H4K20me3. To assess the significance of this change, we used a gain-of-function strategy involving the lysine methyltransferases SUV420H1 and SUV420H2, which catalyze H4K20me2 and H4K20me3. At the same time, we characterized a second isoform of SUV420H1 (designated SUV420H1_i2) and compared the activity of all three SUV420H proteins with regard to localization and H4K20 methylation.Immunofluorescence revealed that exogenous SUV420H1_i2 was distributed throughout the cell, while a substantial portion of SUV420H1_i1 and SUV420H2 displayed the expected association with constitutive heterochromatin. Moreover, SUV420H1_i2 distribution was unaffected by co-expression of heterochromatin protein-1α, which increased the targeting of SUV420H1_i1 and SUV420H2 to regions of pericentromeric heterochromatin. Consistent with their distributions, SUV420H1_i2 caused an increase in H4K20me3 levels throughout the nucleus, whereas SUV420H1_i1 and SUV420H2 facilitated an increase in pericentric H4K20me3. Striking differences continued when the SUV420H proteins were tested in the C2C12 myogenic model system. Specifically, although SUV420H1_i2 induced precocious appearance of the differentiation marker Myogenin in the presence of mitogens, only SUV420H2 maintained a Myogenin-enriched population over the course of differentiation. Paradoxically, SUV420H1_i1 could not be expressed in C2C12 cells, which suggests it is under post-transcriptional or post-translational control.These data indicate that SUV420H proteins differ substantially in their localization and activity. Importantly, SUV420H2 can induce a transition from H4K20me1 to H4K20me3 in regions of constitutive heterochromatin that is sufficient to enhance myogenic differentiation, suggesting it can act an as epigenetic ‘switch’ in this process

Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions

Magic-factor 1, a partial agonist of Met, induces muscle hypertrophy by protecting myogenic progenitors from apoptosis.

Hepatocyte Growth Factor (HGF) is a pleiotropic cytokine of mesenchymal origin that mediates a characteristic array of biological activities including cell proliferation, survival, motility and morphogenesis. Its high affinity receptor, the tyrosine kinase Met, is expressed by a wide range of tissues and can be activated by either paracrine or autocrine stimulation. Adult myogenic precursor cells, the so called satellite cells, express both HGF and Met. Following muscle injury, autocrine HGF-Met stimulation plays a key role in promoting activation and early division of satellite cells, but is shut off in a second phase to allow myogenic differentiation. In culture, HGF stimulation promotes proliferation of muscle precursors thereby inhibiting their differentiation

Discovery and characterization of nutritionally regulated genes associated with muscle growth in Atlantic salmon

A genomics approach was used to identify nutritionally regulated genes involved in growth of fast skeletal muscle in Atlantic salmon (Salmo salar L.). Forward and reverse subtractive cDNA libraries were prepared comparing fish with zero growth rates to fish growing rapidly. We produced 7,420 ESTs and assembled them into nonredundant clusters prior to annotation. Contigs representing 40 potentially unrecognized nutritionally responsive candidate genes were identified. Twenty-three of the subtractive library candidates were also differentially regulated by nutritional state in an independent fasting-refeeding experiment and their expression placed in the context of 26 genes with established roles in muscle growth regulation. The expression of these genes was also determined during the maturation of a primary myocyte culture, identifying 13 candidates from the subtractive cDNA libraries with putative roles in the myogenic program. During early stages of refeeding DNAJA4, HSPA1B, HSP90A, and CHAC1 expression increased, indicating activation of unfolded protein response pathways. Four genes were considered inhibitory to myogenesis based on their in vivo and in vitro expression profiles (CEBPD, ASB2, HSP30, novel transcript GE623928). Other genes showed increased expression with feeding and highest in vitro expression during the proliferative phase of the culture (FOXD1, DRG1) or as cells differentiated (SMYD1, RTN1, MID1IP1, HSP90A, novel transcript GE617747). The genes identified were associated with chromatin modification (SMYD1, RTN1), microtubule stabilization (MID1IP1), cell cycle regulation (FOXD1, CEBPD, DRG1), and negative regulation of signaling (ASB2) and may play a role in the stimulation of myogenesis during the transition from a catabolic to anabolic state in skeletal muscle