Introducing biological information in the superparamagnetic clustering algorithm of gene expression data

Tesis (Doctorado en Nanociencias y Nanotecnología)"Los microarreglos proporcionan informaciòn de la actividad a nivel transcripcional de los genes de un organismo, bajo distintas circunstancias. Esto puede llevar al descubrimiento de genes clave en procesos celulares, clasificación molecular de enfermedades o identificar funciones para los genes, entre otras cosas. En el proceso de obtención de esta información, los algoritmos de clustering son una pieza importante al ayudar en la clasificación de los datos provenientes de microarreglos. En este trabajo modificamos el algoritmo de Clustering Superparamagnético añadiendo un peso extra en la fórmula de interacción que aprovecha la información que se tiene sobre los genes regulados por un mismo factor de transcripción. Con este algoritmo modificado, que nombramos SPCTF, analizamos los datos de microarreglos de Spellman et al. para ciclo celular en levadura (Saccharomyces cerevisiae) y encontramos clusters con un número mayor de integrantes, comparando con el algoritmo original SPC. Algunos de los genes que pudimos incorporar no fueron detectados por Spellman et al. en un principio, pero fueron identificados por otros estudios posteriormente. Otros de los genes que fueron incorporados aún no han sido clasificados, por lo que analizamos los clusters compuestos en su mayoría por estos genes sin identificar con el algoritmo MUSA y esto nos permitió seleccionar aquellos cuyos genes contienen sitios de unión a factores de transcripción correspondientes a ciclo celular. Estos clusters pueden ser estudiados ahora de manera experimental para descubrir nuevos genes involucrados en el ciclo celular. La idea de introducir la información biológica ya disponible para optimizar la clasificación de genes puede ser implementada para otros algoritmos de clustering.""Microarray technology allow researchers to examine the transcriptional activity of thousands of genes under different conditions. Microarrays have been used, for example, to discover key genes involved in cellular processes, disease classification, drug development and gene function annotation. Clustering algorithms have become an important step in the microarray data analysis in order to discover biologically relevant information. We modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. This combined similarity measure for two genes relies on two types of information: their expression profiles generated by a microarray, and the number of shared transcription factors that have been proved (experimentally) to bind to their promoters. With this modified algorithm which we call SPCTF, we analyze the Spellman et al. microarray data for cell cycle genes in yeast (Saccharomyces cerevisiae), and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worthy of further experimental studies because they would probably lead to new cell cycle genes. Our idea of introducing the available information about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms.

Extracellular vesicles from induced neurons trigger epigenetic silencing of a brain neurotransmitter.

Introduction: Antithrombin (AT) is a glycoprotein involved in the regulation of blood coagulation. It belongs to the family of serine-protease inhibitors and acts as the most important antagonist of different clot- ting factors. Two types of inherited AT deficiency can be distinguished: Type I (quantitative deficit), and Type II (qualitative deficit). The latter is characterized by an impaired inhibitory activity related to dysfunc- tional domains of the protein. Three Type II subtypes can be defined: Type IIa (reactive site defect), Type IIb (heparin binding site defect) and Type IIc (pleiotropic defect). This classification has clinical importance since these subtypes have a different thrombotic risk. No functional routine diagnostic assay, however, can be assumed to detect all forms of Type II deficiencies since false-negative results may hamper the diagnosis. Methods: We analysed the biochemical/biophysical association of ATT to EVs. We separated EVs from plasma of healthy or Type II affected patients or from cultured hepatocytes through differential ultracentrifu- gation followed by sucrose density gradient and/or immunoprecipitation. We next combined dot blot ana- lysis, WB, 2D electrophoresis and enzymatic assays to reveal the nature of ATT association to EVs. Results: We evidenced that ATT is associated to the external leaflet of EVs. We also found that specific ATT isoforms are enriched in EV preparations in respect to total plasma and that those isoforms are selectively associated to EVs when comparing healthy or ATT type II deficient patients. Summary/Conclusion: ATT selective association pat- tern to EVs might be related either to mutations in the primary sequence of the protein or alterations in the glycosylation process, hence experiments are ongoing to reveal the nature of this phenomenon. Our findings suggest that analysis of ATT enriched in EV prepara- tions might be useful to gain insights into the patho- genesis and be of support in the diagnostic algorithm of ATT deficiency. Funding: This work acknowledges FFABR (Fondo finanziamento attività Base di ricerca from MIUR, Ministry of Education, Universities and Research, Italy) for financial support