Analysis of early C2C12 myogenesis identifies stably and differentially expressed transcriptional regulators whose knock-down inhibits myoblast differentiation.

Myogenesis is a tightly controlled process involving the transcriptional activation and repression of thousands of genes. Although many components of the transcriptional network regulating the later phases of myogenesis have been identified, relatively few studies have described the transcriptional landscape during the first 24 h, when myoblasts commit to differentiate. Through dense temporal profiling of differentiating C2C12 myoblasts, we identify 193 transcriptional regulators (TRs) whose expression is significantly altered within the first 24 h of myogenesis. A high-content shRNA screen of 77 TRs involving 427 stable lines identified 42 genes whose knockdown significantly inhibits differentiation of C2C12 myoblasts. Of the TRs that were differentially expressed within the first 24 h, over half inhibited differentiation when knocked down, including known regulators of myogenesis (Myod1, Myog, and Myf5), as well as 19 TRs not previously associated with this process. Surprisingly, a similar proportion (55%) of shRNAs targeting TRs whose expression did not change also inhibited C2C12 myogenesis. We further show that a subset of these TRs inhibits myogenesis by downregulating expression of known regulatory and structural proteins. Our findings clearly illustrate that several TRs critical for C2C12 myogenesis are not differentially regulated, suggesting that approaches that focus functional studies on differentially-expressed transcripts will fail to provide a comprehensive view of this complex process.

An overview of image-processing methods for Affymetrix GeneChips.

We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.

Heritability of alternative splicing in the human genome.

Alternative pre-mRNA splicing increases proteomic diversity and provides a potential mechanism underlying both phenotypic diversity and susceptibility to genetic disorders in human populations. To investigate the variation in splicing among humans on a genome-wide scale, we use a comprehensive exon-targeted microarray to examine alternative splicing in lymphoblastoid cell lines (LCLs) derived from the CEPH HapMap population. We show the identification of transcripts containing sequence verified exon skipping, intron retention, and cryptic splice site usage that are specific between individuals. A number of novel alternative splicing events with no previous annotations in either the RefSeq and EST databases were identified, indicating that we are able to discover de novo splicing events. Using family-based linkage analysis, we demonstrate Mendelian inheritance and segregation of specific splice isoforms with regulatory haplotypes for three genes: OAS1, CAST, and CRTAP. Allelic association was further used to identify individual SNPs or regulatory haplotype blocks linked to the alternative splicing event, taking advantage of the high-resolution genotype information from the CEPH HapMap population. In one candidate, we identified a regulatory polymorphism that disrupts a 5' splice site of an exon in the CAST gene, resulting in its exclusion in the mutant allele. This report illustrates that our approach can detect both annotated and novel alternatively spliced variants, and that such variation among individuals is heritable and genetically controlled.

Prediction and uncertainty in the analysis of gene expression profiles.

We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers. The study is aimed at defining molecular characteristics of tumors that reflect estrogen receptor tatus. In addition to good predictive performance with respect to pure classification of the expression profiles, the model also uncovers conflicts in the data with respect to the classification of some of the tumors, highlighting them as critical cases for which additional investigations are appropriate.