Browsing by Author "Oliveira, Daniel V."
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- CITED2 cooperates with ISL1 and promotes cardiac differentiation of mouse embryonic stem cellsPublication . Pacheco-Leyva, Ivette; Matias, Ana Catarina; Oliveira, Daniel V.; Santos, João; Nascimento, Rita; Guerreiro, Eduarda; Michell, Anna C.; van De Vrugt, Annebel M.; Machado-Oliveira, Gisela; Ferreira, Guilherme; Domian, Ibrahim; Bragança, JoséThe transcriptional regulator CITED2 is essential for heart development. Here, we investigated the role of CITED2 in the specification of cardiac cell fate from mouse embryonic stem cells (ESC). The overexpression of CITED2 in undifferentiated ESC was sufficient to promote cardiac cell emergence upon differentiation. Conversely, the depletion of Cited2 at the onset of differentiation resulted in a decline of ESC ability to generate cardiac cells. Moreover, loss of Cited2 expression impairs the expression of early mesoderm markers and cardiogenic transcription factors (Isl1, Gata4, Tbx5). The cardiogenic defects in Cited2-depleted cells were rescued by treatment with recombinant CITED2 protein. We showed that Cited2 expression is enriched in cardiac progenitors either derived from ESC or mouse embryonic hearts. Finally, we demonstrated that CITED2 and ISL1 proteins interact physically and cooperate to promote ESC differentiation toward cardiomyocytes. Collectively, our results show that Cited2 plays a pivotal role in cardiac commitment of ESC.
- StemChecker: a web-based tool to discover and explore stemness signatures in gene setsPublication . Pinto, Jose P.; Kalathur, Ravi Kiran Reddy; Oliveira, Daniel V.; Barata, Tania; Machado, Rui; Machado, Susana; Pacheco-Leyva, Ivette; Duarte, Isabel; Futschik, Matthias E.Stem cells present unique regenerative abilities, offering great potential for treatment of prevalent pathologies such as diabetes, neurodegenerative and heart diseases. Various research groups dedicated significant effort to identify sets of genes-so-called stemness signatures-considered essential to define stem cells. However, their usage has been hindered by the lack of comprehensive resources and easy-to-use tools. For this we developed StemChecker, a novel stemness analysis tool, based on the curation of nearly fifty published stemness signatures defined by gene expression, RNAi screens, Transcription Factor (TF) binding sites, literature reviews and computational approaches. StemChecker allows researchers to explore the presence of stemness signatures in user-defined gene sets, without carrying-out lengthy literature curation or data processing. To assist in exploring underlying regulatory mechanisms, we collected over 80 target gene sets of TFs associated with pluri- or multipotency. StemChecker presents an intuitive graphical display, as well as detailed statistical results in table format, which helps revealing transcriptionally regulatory programs, indicating the putative involvement of stemness-associated processes in diseases like cancer. Overall, StemChecker substantially expands the available repertoire of online tools, designed to assist the stem cell biology, developmental biology, regenerative medicine and human disease research community. StemChecker is freely accessible at http://stemchecker.sysbiolab.eu.
- StemMapper: a curated gene expression database for stem cell lineage analysisPublication . Pinto, Jose P.; Machado, Rui S. R.; Magno, Ramiro; Oliveira, Daniel V.; Machado, Susana; Andrade, Raquel P.; Braganca, Jose; Duarte, Isabel; Futschik, Matthias E.Transcriptomic data have become a fundamental resource for stem cell (SC) biologists as well as for a wider research audience studying SC-related processes such as aging, embryonic development and prevalent diseases including cancer, diabetes and neurodegenerative diseases. Access and analysis of the growing amount of freely available transcriptomics datasets for SCs, however, are not trivial tasks. Here, we present StemMapper, a manually curated gene expression database and comprehensive resource for SC research, built on integrated data for different lineages of human and mouse SCs. It is based on careful selection, standardized processing and stringent quality control of relevant transcriptomics datasets to minimize artefacts, and includes currently over 960 transcriptomes covering a broad range of SC types. Each of the integrated datasets was individually inspected andmanually curated. StemMapper's user-friendly interface enables fast querying, comparison, and interactive visualization of quality-controlled SC gene expression data in a comprehensive manner. A proof-of-principle analysis discovering novel putative astrocyte/neural SC lineage markers exemplifies the utility of the integrated data resource. We believe that StemMapper can open the way for new insights and advances in SC research by greatly simplifying the access and analysis of SC transcriptomic data.