DEVELOPMENT OF SOFTWARE TOOLS FOR THE PREDICTION OF CELULAR FATE IN STEM CELL DIFERENTIATION

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Publications

StemCellNet: an interactive platform for network-oriented investigations in stem cell biology

Publication . Pinto, Jose P.; Kalathur, Ravi Kiran Reddy; Machado, Rui; JM Xavier; Bragança, José; Futschik, Matthias E.

Stem cells are characterized by their potential for self-renewal and their capacity to differentiate into mature cells. These two key features emerge through the interplay of various factors within complex molecular networks. To provide researchers with a dedicated tool to investigate these networks, we have developed StemCellNet, a versatile web server for interactive network analysis and visualization. It rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer. The StemCellNet web server is freely accessible at http://stemcellnet.sysbiolab.eu.

2014-07Journal article

Open access

StemChecker: a web-based tool to discover and explore stemness signatures in gene sets

Publication . 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.

2015-07Journal article

Open access

StemMapper: a curated gene expression database for stem cell lineage analysis

Publication . 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.

2018-01Journal article

Open access

Parallel Genome-wide Profiling of Coding and Non-coding RNAs to Identify Novel Regulatory Elements in Embryonic and Maturated Heart

Publication . Sabour, Davood; Machado, Rui; Pinto, José P.; Rohani, Susan; Sahito, Raja G. A.; Hescheler, Jurgen; Futschik, Matthias; Sachinidis, Agapios

Heart development is a complex process, tightly regulated by numerous molecular mechanisms. Key components of the regulatory network underlying heart development are transcription factors (TFs) and microRNAs (miRNAs), yet limited investigation of the role of miRNAs in heart development has taken place. Here, we report the first parallel genome-wide profiling of polyadenylated RNAs and miRNAs in a developing murine heart. These data enable us to identify dynamic activation or repression of numerous biological processes and signaling pathways. More than 200 miRNAs and 25 long non-coding RNAs were differentially expressed during embryonic heart development compared to the mature heart; most of these had not been previously associated with cardiogenesis. Integrative analysis of expression data and potential regulatory interactions suggested 28 miRNAs as novel regulators of embryonic heart development, representing a considerable expansion of the current repertoire of known cardiac miRNAs. To facilitate follow-up investigations, we constructed HeartMiR (http://heartmir.sysbiolab.eu), an open access database and interactive visualization tool for the study of gene regulation by miRNAs during heart development.

2018-09Journal article

Open access

HDNetDB: A Molecular Interaction Database for Network-Oriented Investigations into Huntington's Disease

Publication . Reddy Kalathur, Ravi Kiran; Pinto, Jose Pedro; Sahoo, Biswanath; Chaurasia, Gautam; Futschik, Matthias E.

Huntington's disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Although HD is monogenic, its molecular manifestation appears highly complex and involves multiple cellular processes. The recent application of high throughput platforms such as microarrays and mass-spectrometry has indicated multiple pathogenic routes. The massive data generated by these techniques together with the complexity of the pathogenesis, however, pose considerable challenges to researchers. Network-based methods can provide valuable tools to consolidate newly generated data with existing knowledge, and to decipher the interwoven molecular mechanisms underlying HD. To facilitate research on HD in a network-oriented manner, we have developed HDNetDB, a database that integrates molecular interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms. We illustrated several HDNetDB functionalities through a case study and identified proteins that constitute potential cross-talk between HD and the unfolded protein response (UPR). HDNetDB is publicly accessible at http://hdnetdb.sysbiolab.eu.

2017-07Journal article

Open access