Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.

No abstract

Splicing is the most frequently altered biological process by mutations within gene regions. Information for splicing is recognized by several factors that bind pre-mRNA sequence and, through coordinated interaction, yield mature transcripts. Some in silico methods have been developed to predict if a mutation leads to aberrant splicing patterns. We previously created SpliceAid tool that is able to minimize false positive predictions because it adopts strictly experimental RNA target motifs bound by splicing proteins in humans. In order to improve prediction accuracy and better understand the splicing outcome, the tissue specificity of each splicing regulatory factor has to be taken into account. Here, we have developed SpliceAid 2 by adding the expression data related to the splicing factors extracted from the main proteomic and transcriptomic databases, true 5' and 3' splice sites, polypyrimidine tracts, and branch point sequences. The new version collects 2,220 target sites of 62 human splicing proteins and their expression data in 320 tissues per cell. SpliceAid 2 can be useful to foresee the splicing pattern alteration, to guide the identification of the molecular effect due to the mutations and to understand the tissue-specific alternative splicing. SpliceAid 2 is freely accessible at www.introni.it/spliceaid.html.

No abstract

High mortality and low survival rates for pancreatic ductal adenocarcinoma (PDAC) mainly result from the delay in diagnosis and treatment. Therefore there is an urgent need to identify early PDAC biomarkers and new therapeutic targets. In this study, we applied a commonly used systems biology approach, the weighted gene co-expression network analysis (WGCNA), on lncRNA expression data. Eleven lncRNAs, namely A2M-AS1, DLEU2, LINC01133, LINC00675, MIR155HG, SLC25A25-AS1, LINC01857, LOC642852 (LINC00205), ITGB2-AS1, TSPOAP1-AS1 and PSMB8-AS1 have been identified and validated on an independent PDAC expression dataset. Furthermore, we characterised them by functional and pathway enrichment analysis and identified which lncRNAs showed differential expression, differential promoter methylation levels and copy number alterations between normal and PDAC samples. Finally, we also performed a survival analysis and identified A2M-AS1, LINC01133, LINC00205 and TSPOAP1-AS1 as prognostic biomarkers for PDAC. Interestingly, although only a few cancer-associated lncRNAs have been functionally characterized, LINC00675 and LINC01133 lncRNAs have been already demonstrated to be involved in PDAC development and progression. Therefore, our results provide new potential diagnostic/prognostic biomarkers and therapeutic targets for PDAC that deserve to be further investigated. Moreover, these lncRNAs may improve the understanding about molecular pathogenesis of PDAC.