anno 2011

Abstract

PlantPIs is a web querying system for a database collection of plant protease inhibitors data. Protease inhibitors in plants are naturally occurring proteins that inhibit the function of endogenous and exogenous proteases. In this paper the design and development of a web framework providing a clear and very flexible way of querying plant protease in- hibitors data is reported. The web resource is based on a relational database, containing data of plants protease inhibitors publicly accessible, and a graphical user interface providing all the necessary browsing tools, including a data exporting function. PlantPIs contains information extracted principally from MEROPS database, filtered, annotated and compared with data stored in other protein and gene public databases, using both automated techniques and domain expert evalua- tions. The data are organized to allow a flexible and easy way to access stored information. The database is accessible at http://www.plantpis.ba.itb.cnr.it.

Abstract

Alternative splicing is emerging as a major mechanism for the expansion of the transcriptome and proteome diversity, particularly in human and other vertebrates. However, the proportion of alternative transcripts and proteins actually endowed with functional activity is currently highly debated. We present here a new release of ASPicDB which now provides a unique annotation resource of human protein variants generated by alternative splicing. A total of 256,939 protein variants from 17,191 multi-exon genes have been extensively annotated through state of the art machine learning tools providing information of the protein type (globular and transmembrane), localization, presence of PFAM domains, signal peptides, GPI-anchor propeptides, transmembrane and coiled-coil segments. Furthermore, full-length variants can be now specifically selected based on the annotation of CAGE-tags and polyA signal and/or polyA sites, marking transcription initiation and termination sites, respectively. The retrieval can be carried out at gene, transcript, exon, protein or splice site level allowing the selection of data sets fulfilling one or more features settled by the user. The retrieval interface also enables the selection of protein variants showing specific differences in the annotated features. ASPicDB is available at http://www.caspur.it/ASPicDB.

Abstract

Microarray data are a rich source of information, containing the collected expression values of thousands of genes for well-defined states of a cell or tissue. Vast amounts of data (thousands of arrays) are publicly available and ready for analysis, for example to scrutinise correlations between genes at the level of gene expression. The large variety of arrays available makes it possible to combine different independent experiments to extract new knowledge. Starting with a large set of data, relevant information can be isolated for further analysis. To extract the required information from data-sets of such size and complexity requires an appropriate and powerful analysis method. In this study, we chose to use an unsupervised hierarchical clustering algorithm, Chaotic Map Clustering (CMC), in a coupled two-way approach to analyse such data. However, the clustering approach is intrinsically difficult, both in terms of the unknown structure of the data and interpretation of the clustering results. It is therefore critical to evaluate the quality of any unsupervised procedure for such a complex set of data and to validate the results, separating those clusters that are due simply to noise or statistical fluctuations. We used a resampling method to perform this validation. The resampling procedure applies the clustering algorithm to a large number of random sub-samples of the original data-matrix and, consequently, the whole process becomes computationally intensive and time consuming. Using Grid technology, we show that we can drastically speed up this process by distributing the clustering of each matrix to a separate worker node, and thus retrieve resampling results within a few hours instead of several days. Further, we offer an online service to cluster large microarray data sets and conduct the subsequent validation described in this paper.

Abstract

BCL-2 is an integral protein of the external mitochondrial membrane that inhibits cell apoptotic death. We investigated the effect of androgen deprivation therapy (ADT) on BCL-2 expression in prostate cancer tissues. We studied BCL-2 expression in vivo in prostate cancer tissues obtained from patients who underwent radical prostatectomy after neoadjuvant ADT, by Northern and Western blot analysis, and immunohistochemistry. Moreover, gene transcriptional activity was also measured by nuclear run-on experiments. We demonstrated an increase of BCL-2 mRNA expression in patients who underwent neoadjuvant ADT for 1 month in comparison to patients who had not received any therapy. Moreover, we demonstrated that there were no significant modifications of BCL-2 mRNA levels in patients who underwent neoadjuvant ADT for 3 and 6 months. Furthermore, BCL-2 protein levels in patients who underwent neoadjuvant ADT for 1 month were upregulated in comparison to patients who had not received any therapy. Immunohistochemical analysis showed a strong positivity of prostate cells depending on ADT administration for 1 month. Finally, transcriptional activity was not modified in patients who underwent neoadjuvant ADT, suggesting the absence of hormonal regulation on BCL-2 gene expression at the transcriptional level. Our data show that short-term administration of ADT interferes with BCL-2 expression, suggesting that androgen-mediated mechanisms may act through BCL-2-mediated apoptotic pathways. Moreover, since short-term ADT administration does not interfere with BCL-2 expression at the transcriptional level, the androgen-mediated mechanisms involving BCL-2 pathways, probably act at the post-transcriptional level.

Abstract
OBJECTIVE
Approximately 6% of newborns at term are small for gestational age (SGA) and present a birth weight and/or length less than -2SD from the mean. SGA infants are at increased risk for perinatal morbidity, associated psychological and/or mental problems, persistent short stature (about 15% of subjects) and metabolic alterations. Insulin-like growth factors (IGFs), their common receptor (IGF1R) and their binding proteins (IGFBPs) play a critical role in fetal and postnatal growth. In these genes common polymorphisms, such as single nucleotide polymorphisms and variable number of tandem repeats, have been investigated with conflicting results with respect to SGA-related outcomes, and the functional role of these gene variants remains to be elucidated.

DESIGN
The study group consisted of 100 pre-pubertal short children born SGA and 94 healthy controls, matched for sex and age, recruited at the Department of Biomedicine of Development Age of the Bari University and at the Paediatric Department of the Messina Hospital. In the present study we analyzed the allelic frequency of the polymorphisms -795 G/A, -667 G/A, -396 C/T in the IGFBP3 in SGA children and their influence on the basal and insulin-stimulated transcriptional activity of the gene.

RESULTS
We found that the polymorphisms -667 G/A and -396 C/T in the IGFBP3 promoter region are capable of having an effect on the transcriptional activity of the gene, although with opposing effects. Interestingly, the -667 G/A polymorphism has a negative impact on the IGFBP3 transcription, while the -396 C/T polymorphism determines an increase of the transcriptional activity of the IGFBP3 gene promoter. Interestingly, we found that the -396 C/T polymorphism correlates with lower birth length in SGA children. Most importantly, while the diminished IGFBP3 transcriptional activity induced by the -667A polymorphism was significantly recovered after insulin administration (p-value<0.05), the increased transcriptional activity caused by the -396T polymorphism was not restored to baseline levels by insulin.

CONCLUSIONS
Altogether our results demonstrated that the -667 G/A and the -396 C/T polymorphisms in IGFBP3 promoter region influence the basal transcriptional activity of the gene.

Abstract

Microarray data are a rich source of information, containing the collected expression values of thousands of genes for well-defined states of a cell or tissue. Vast amounts of data (thousands of arrays) are publicly available and ready for analysis, for example to scrutinise correlations between genes at the level of gene expression. The large variety of arrays available makes it possible to combine different independent experiments to extract new knowledge. Starting with a large set of data, relevant information can be isolated for further analysis. To extract the required information from data-sets of such size and complexity requires an appropriate and powerful analysis method. In this study, we chose to use an unsupervised hierarchical clustering algorithm, Chaotic Map Clustering (CMC), in a coupled two-way approach to analyse such data. However, the clustering approach is intrinsically difficult, both in terms of the unknown structure of the data and interpretation of the clustering results. It is therefore critical to evaluate the quality of any unsupervised procedure for such a complex set of data and to validate the results, separating those clusters that are due simply to noise or statistical fluctuations. We used a resampling method to perform this validation. The resampling procedure applies the clustering algorithm to a large number of random sub-samples of the original data-matrix and, consequently, the whole process becomes computationally intensive and time consuming. Using Grid technology, we show that we can drastically speed up this process by distributing the clustering of each matrix to a separate worker node, and thus retrieve resampling results within a few hours instead of several days. Further, we offer an online service to cluster large microarray data sets and conduct the subsequent validation described in this paper.