A new analysis approach of epidermal growth factor receptor pathway activation patterns provides insights into cetuximab resistance mechanisms in head and neck cancer

The pathways downstream of the epidermal growth factor receptor (EGFR) have often been implicated to play crucial roles in the development and progression of various cancer types. Different authors have proposed models in cell lines in which they study the modes of pathway activities after perturbation experiments. It is prudent to believe that a better understanding of these pathway activation patterns might lead to novel treatment concepts for cancer patients or at least allow a better stratification of patient collectives into different risk groups or into groups that might respond to different treatments. Traditionally, such analyses focused on the individual players of the pathways. More recently in the field of systems biology, a plethora of approaches that take a more holistic view on the signaling pathways and their downstream transcriptional targets has been developed. Fertig et al. have recently developed a new method to identify patterns and biological process activity from transcriptomics data, and they demonstrate the utility of this methodology to analyze gene expression activity downstream of the EGFR in head and neck squamous cell carcinoma to study cetuximab resistance. Please see related article: http://www.biomedcentral.com/1471-2164/13/16

phiSITE: database of gene regulation in bacteriophages

We have developed phiSITE, database of gene regulation in bacteriophages. To date it contains detailed information about more than 700 experimentally confirmed or predicted regulatory elements (promoters, operators, terminators and attachment sites) from 32 bacteriophages belonging to Siphoviridae, Myoviridae and Podoviridae families. The database is manually curated, the data are collected mainly form scientific papers, cross-referenced with other database resources (EMBL, UniProt, NCBI taxonomy database, NCBI Genome, ICTVdb, PubMed Central) and stored in SQL based database system. The system provides full text search for regulatory elements, graphical visualization of phage genomes and several export options. In addition, visualizations of gene regulatory networks for five phages (Bacillus phage GA-1, Enterobacteria phage lambda, Enterobacteria phage Mu, Enterobacteria phage P2 and Mycoplasma phage P1) have been defined and made available. The phiSITE is accessible at http://www.phisite.org/

Design of Specific Mammalian Promoters by in silico Prediction

The purpose of this RFC is to provide a) a method for the design of rational synthetic promoter sequences based on a statistical analysis about the spatial preference of transcription factor binding sites in human promoter sequences and b) further introduce standards to provide compatibility with data formats introduced in this RFC. Description of promoters generated by this method can be found at http://2009.igem.org/Team:Heidelberg/HEARTBEAT_database

Mutation screening of melatonin-related genes in patients with autism spectrum disorders

<p>Abstract</p> <p>Background</p> <p>One consistent finding in autism spectrum disorders (ASD) is a decreased level of the pineal gland hormone melatonin and it has recently been demonstrated that this decrease to a large extent is due to low activity of the acetylserotonin O-methyltransferase (ASMT), the last enzyme in the melatonin synthesis pathway. Moreover, mutations in the <it>ASMT </it>gene have been identified, including a splice site mutation, that were associated with low ASMT activity and melatonin secretion, suggesting that the low ASMT activity observed in autism is, at least partly, due to variation within the <it>ASMT </it>gene.</p> <p>Methods</p> <p>In the present study, we have investigated all the genes involved in the melatonin pathway by mutation screening of <it>AA-NAT </it>(arylalkylamine N-acetyltransferase), <it>ASMT, MTNR1A, MTNR1B </it>(melatonin receptor 1A and 1B) and <it>GPR50 </it>(G protein-coupled receptor 50), encoding both synthesis enzymes and the three main receptors of melatonin, in 109 patients with autism spectrum disorders (ASD). A cohort of 188 subjects from the general population was used as a comparison group and was genotyped for the variants identified in the patient sample.</p> <p>Results</p> <p>Several rare variants were identified in patients with ASD, including the previously reported splice site mutation in <it>ASMT </it>(IVS5+2T>C). Of the variants affecting protein sequence, only the V124I in the <it>MTNR1B </it>gene was absent in our comparison group. However, mutations were found in upstream regulatory regions in three of the genes investigated, <it>ASMT, MTNR1A</it>, and <it>MTNR1B</it>.</p> <p>Conclusions</p> <p>Our report of another ASD patient carrying the splice site mutation IVS5+2T>C, in <it>ASMT </it>further supports an involvement of this gene in autism. Moreover, our results also suggest that other melatonin related genes might be interesting candidates for further investigation in the search for genes involved in autism spectrum disorders and related neurobehavioral phenotypes. However, further studies of the novel variants identified in this study are warranted to shed light on their potential role in the pathophysiology of these disorders.</p

FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system

FlyFactorSurvey (http://pgfe.umassmed.edu/TFDBS/) is a database of DNA binding specificities for Drosophila transcription factors (TFs) primarily determined using the bacterial one-hybrid system. The database provides community access to over 400 recognition motifs and position weight matrices for over 200 TFs, including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within our database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. Together, this database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences

DoOPSearch: a web-based tool for finding and analysing common conserved motifs in the promoter regions of different chordate and plant genes

BACKGROUND: The comparative genomic analysis of a large number of orthologous promoter regions of the chordate and plant genes from the DoOP databases shows thousands of conserved motifs. Most of these motifs differ from any known transcription factor binding site (TFBS). To identify common conserved motifs, we need a specific tool to be able to search amongst them. Since conserved motifs from the DoOP databases are linked to genes, the result of such a search can give a list of genes that are potentially regulated by the same transcription factor(s). RESULTS: We have developed a new tool called DoOPSearch for the analysis of the conserved motifs in the promoter regions of chordate or plant genes. We used the orthologous promoters of the DoOP database to extract thousands of conserved motifs from different taxonomic groups. The advantage of this approach is that different sets of conserved motifs might be found depending on how broad the taxonomic coverage of the underlying orthologous promoter sequence collection is (consider e.g. primates vs. mammals or Brassicaceae vs. Viridiplantae). The DoOPSearch tool allows the users to search these motif collections or the promoter regions of DoOP with user supplied query sequences or any of the conserved motifs from the DoOP database. To find overrepresented gene ontologies, the gene lists obtained can be analysed further using a modified version of the GeneMerge program. CONCLUSION: We present here a comparative genomics based promoter analysis tool. Our system is based on a unique collection of conserved promoter motifs characteristic of different taxonomic groups. We offer both a command line and a web-based tool for searching in these motif collections using user specified queries. These can be either short promoter sequences or consensus sequences of known transcription factor binding sites. The GeneMerge analysis of the search results allows the user to identify statistically overrepresented Gene Ontology terms that might provide a clue on the function of the motifs and genes

Screening Driving Transcription Factors in the Processing of Gastric Cancer

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer. Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed. Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer. Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis

Linking genes to diseases: it's all in the data

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches

Transcriptome Analysis of Systems Biology for Schizophrenia

Transcriptome analysis of postmortem brain samples provides more insights to evaluate biological dysfunctions by analysis of differential expression and genetic interactions in schizophrenia. The growing development of new technologies such as next-generation sequencing (NGS) helps to explore detailed and underlying molecular changes from global perspective of view, not only focus in single SNP variants. It is implicated that schizophrenia genetic and protein interactions may give rise to biological dysfunction not only in dopamine dysfunction but also in immune, energy metabolism, mitochondrial dysfunction and hemostasis. Epigenetic investigation of schizophrenia provides important information on how the environmental factors affect the genetic architecture of the disease. DNA methylation plays a pivotal role in etiology for schizophrenia. The schizophrenia differential methylation genes and differential expression genes were analyzed to find the potential protein complexes related to the etiology of schizophrenia from alteration of DNA methylation. The protein complexes and pathways involved in schizophrenia differential methylation network may be responsible for the etiology and potential treatment targets. It is implicated that the interaction between differential expression candidate genes and differential methylation genes may describe the global view of disease mechanisms and it has important roles in the pathogenesis for schizophrenia