Modulation of PKM alternative splicing by PTBP1 promotes gemcitabine resistance in pancreatic cancer cells

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and incurable disease. Poor prognosis is due to multiple reasons, including acquisition of resistance to gemcitabine, the first-line chemotherapeutic approach. Thus, there is a strong need for novel therapies, targeting more directly the molecular aberrations of this disease. We found that chronic exposure of PDAC cells to gemcitabine selected a subpopulation of cells that are drug-resistant (DR-PDAC cells). Importantly, alternative splicing (AS) of the pyruvate kinase gene (PKM) was differentially modulated in DR-PDAC cells, resulting in promotion of the cancer-related PKM2 isoform, whose high expression also correlated with shorter recurrence-free survival in PDAC patients. Switching PKM splicing by antisense oligonucleotides to favor the alternative PKM1 variant rescued sensitivity of DR-PDAC cells to gemcitabine and cisplatin, suggesting that PKM2 expression is required to withstand drug-induced genotoxic stress. Mechanistically, upregulation of the polypyrimidine-tract binding protein (PTBP1), a key modulator of PKM splicing, correlated with PKM2 expression in DR-PDAC cell lines. PTBP1 was recruited more efficiently to PKM pre-mRNA in DR- than in parental PDAC cells. Accordingly, knockdown of PTBP1 in DR-PDAC cells reduced its recruitment to the PKM pre-mRNA, promoted splicing of the PKM1 variant and abolished drug resistance. Thus, chronic exposure to gemcitabine leads to upregulation of PTBP1 and modulation of PKM AS in PDAC cells, conferring resistance to the drug. These findings point to PKM2 and PTBP1 as new potential therapeutic targets to improve response of PDAC to chemotherapy.Oncogene advance online publication, 3 August 2015; doi:10.1038/onc.2015.270

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

A framework for 3D vessel analysis using whole slide images of liver tissue sections

Three-dimensional (3D) high resolution microscopic images have high potential for improving the understanding of both normal and disease processes where structural changes or spatial relationship of disease features are significant. In this paper, we develop a complete framework applicable to 3D pathology analytical imaging, with an application to whole slide images of sequential liver slices for 3D vessel structure analysis. The analysis workflow consists of image registration, segmentation, vessel cross-section association, interpolation, and volumetric rendering. To identify biologically-meaningful correspondence across adjacent slides, we formulate a similarity function for four association cases. The optimal solution is then obtained by constrained Integer Programming. We quantitatively and qualitatively compare our vessel reconstruction results with human annotations. Validation results indicate a satisfactory concordance as measured both by region-based and distance-based metrics. These results demonstrate a promising 3D vessel analysis framework for whole slide images of liver tissue sections

FISim: A new similarity measure between transcription factor binding sites based on the fuzzy integral

Background Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources.Results We propose FISim, a new similarity measure between PFMs, based on the fuzzy integral of the distance of the nucleotides with respect to the information content of the positions. Unlike existing methods, FISim is designed to consider the higher contribution of better conserved positions to the binding affinity. FISim provides excellent results when dealing with sets of randomly generated motifs, and outperforms the remaining methods when handling real datasets of related motifs. Furthermore, we propose a new cluster methodology based on kernel theory together with FISim to obtain groups of related motifs potentially bound by the same TFs, providing more robust results than existing approaches.Conclusion FISim corrects a design flaw of the most popular methods, whose measures favour similarity of low information content positions. We use our measure to successfully identify motifs that describe binding sites for the same TF and to solve real-life problems. In this study the reliability of fuzzy technology for motif comparison tasks is proven.This work has been carried out as part of projects P08-TIC-4299 of J. A., Sevilla and TIN2006-13177 of DGICT, Madrid