Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes

Gene regulation in eukaryotes is mainly effected through transcription factors binding to rather short recognition motifs generally located upstream of the coding region. We present a novel computational method to identify regulatory elements in the upstream region of eukaryotic genes. The genes are grouped in sets sharing an overrepresented short motif in their upstream sequence. For each set, the average expression level from a microarray experiment is determined: If this level is significantly higher or lower than the average taken over the whole genome, then the overerpresented motif shared by the genes in the set is likely to play a role in their regulation. The method was tested by applying it to the genome of Saccharomyces cerevisiae, using the publicly available results of a DNA microarray experiment, in which expression levels for virtually all the genes were measured during the diauxic shift from fermentation to respiration. Several known motifs were correctly identified, and a new candidate regulatory sequence was determined.Comment: Published version available from http://www.biomedcentral.com/1471-2105/3/

Precision Revisited: Targeting Microcephaly Kinases in Brain Tumors

Glioblastoma multiforme and medulloblastoma are the most frequent high-grade brain tumors in adults and children, respectively. Standard therapies for these cancers are mainly based on surgical resection, radiotherapy, and chemotherapy. However, intrinsic or acquired resistance to treatment occurs almost invariably in the first case, and side effects are unacceptable in the second. Therefore, the development of new, effective drugs is a very important unmet medical need. A critical requirement for developing such agents is to identify druggable targets required for the proliferation or survival of tumor cells, but not of other cell types. Under this perspective, genes mutated in congenital microcephaly represent interesting candidates. Congenital microcephaly comprises a heterogeneous group of disorders in which brain volume is reduced, in the absence or presence of variable syndromic features. Genetic studies have clarified that most microcephaly genes encode ubiquitous proteins involved in mitosis and in maintenance of genomic stability, but the effects of their inactivation are particularly strong in neural progenitors. It is therefore conceivable that the inhibition of the function of these genes may specifically affect the proliferation and survival of brain tumor cells. Microcephaly genes encode for a few kinases, including CITK, PLK4, AKT3, DYRK1A, and TRIO. In this review, we summarize the evidence indicating that the inhibition of these molecules could exert beneficial effects on different aspects of brain cancer treatment

Cenpe inhibition leads to mitotic catastrophe and dna damage in medulloblastoma cells

Medulloblastoma (MB) is the most frequent brain tumor in children. The standard treatment consists in surgery, followed by radiotherapy and chemotherapy. These therapies are only partially effective since many patients still die and those who survive suffer from neurological and endocrine disorders. Therefore, more effective therapies are needed. Primary microcephaly (MCPH) is a rare disorder caused by mutations in 25 different genes. Centromere-associated protein E (CENPE) heterozygous mutations cause the MCPH13 syndrome. As for other MCPH genes, CENPE is required for normal proliferation and survival of neural progenitors. Since there is evidence that MB shares many molecular features with neural progenitors, we hypothesized that CENPE could be an effective target for MB treatment. In ONS-76 and DAOY cells, CENPE knockdown induced mitotic defects and apoptosis. Moreover, CENPE depletion induced endogenous DNA damage accumulation, activating TP53 or TP73 as well as cell death signaling pathways. To consolidate CENPE as a target for MB treatment, we tested GSK923295, an allosteric inhibitor already in clinical trial for other cancer types. GSK923295, induced effects similar to CENPE depletion with higher penetrance, at low nM levels, suggesting that CENPE’s inhibition could be a therapeutic strategy for MB treatment

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites. google.com/site/gaussianbhc