An Ambystoma mexicanum EST sequencing project: analysis of 17,352 expressed sequence tags from embryonic and regenerating blastema cDNA libraries

BACKGROUND: The ambystomatid salamander, Ambystoma mexicanum (axolotl), is an important model organism in evolutionary and regeneration research but relatively little sequence information has so far been available. This is a major limitation for molecular studies on caudate development, regeneration and evolution. To address this lack of sequence information we have generated an expressed sequence tag (EST) database for A. mexicanum. RESULTS: Two cDNA libraries, one made from stage 18-22 embryos and the other from day-6 regenerating tail blastemas, generated 17,352 sequences. From the sequenced ESTs, 6,377 contigs were assembled that probably represent 25% of the expressed genes in this organism. Sequence comparison revealed significant homology to entries in the NCBI non-redundant database. Further examination of this gene set revealed the presence of genes involved in important cell and developmental processes, including cell proliferation, cell differentiation and cell-cell communication. On the basis of these data, we have performed phylogenetic analysis of key cell-cycle regulators. Interestingly, while cell-cycle proteins such as the cyclin B family display expected evolutionary relationships, the cyclin-dependent kinase inhibitor 1 gene family shows an unusual evolutionary behavior among the amphibians. CONCLUSIONS: Our analysis reveals the importance of a comprehensive sequence set from a representative of the Caudata and illustrates that the EST sequence database is a rich source of molecular, developmental and regeneration studies. To aid in data mining, the ESTs have been organized into an easily searchable database that is freely available online

Examination of Apoptosis Signaling in Pancreatic Cancer by Computational Signal Transduction Analysis

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction. METHODOLOGY/PRINCIPAL FINDINGS: Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway. CONCLUSIONS/SIGNIFICANCE: Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC

Gene Expression Profiling of Microdissected Pancreatic Ductal Carcinomas Using High-Density DNA Microarrays,

Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of malignancy-related death and is the eighth most common cancer with the lowest overall 5-year relative survival rate. To identify new molecular markers and candidates for new therapeutic regimens, we investigated the gene expression profile of microdissected cells from 11 normal pancreatic ducts, 14 samples of PDAC, and 4 well-characterized pancreatic cancer cell lines using the Affymetrix U133 GeneChip set. RNA was extracted from microdissected samples and cell lines, amplified, and labeled using a repetitive in vitro transcription protocol. Differentially expressed genes were identified using the significance analysis of microarrays program. We found 616 differentially expressed genes. Within these, 140 were also identified in PDAC by others, such as Galectin-1, Galectin-3, and MT-SP2. We validated the differential expression of several genes (e.g., CENPF, MCM2, MCM7, RAMP, IRAK1, and PTTG1) in PDAC by immunohistochemistry and reverse transcription polymerase chain reaction. We present a whole genome expression study of microdissected tissues from PDAC, from microdissected normal ductal pancreatic cells and pancreatic cancer cell lines using highdensity microarrays. Within the panel of genes, we identified novel differentially expressed genes, which have not been associated with the pathogenesis of PDAC before