RESUMO
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
RESUMO
Ewing Sarcoma (ES) is a rare malignant tumor occurring most frequently in adolescents and young adults. The ES hallmark is a chromosomal translocation between the chromosomes 11 and 22 that results in an aberrant transcription factor (TF) through the fusion of genes from the FET and ETS families, commonly EWSR1 and FLI1. The regulatory mechanisms behind the ES transcriptional alterations remain poorly understood. Here, we reconstruct the ES regulatory network using public available transcriptional data. Seven TFs were identified as potential MRs and clustered into two groups: one composed by PAX7 and RUNX3, and another composed by ARNT2, CREB3L1, GLI3, MEF2C, and PBX3. The MRs within each cluster act as reciprocal agonists regarding the regulation of shared genes, regulon activity, and implications in clinical outcome, while the clusters counteract each other. The regulons of all the seven MRs were differentially methylated. PAX7 and RUNX3 regulon activity were associated with good prognosis while ARNT2, CREB3L1, GLI3, and PBX3 were associated with bad prognosis. PAX7 and RUNX3 appear as highly expressed in ES biopsies and ES cell lines. This work contributes to the understanding of the ES regulome, identifying candidate MRs, analyzing their methilome and pointing to potential prognostic factors.
RESUMO
Chemoreception is among the most important sensory modalities in animals. Organisms use the ability to perceive chemical compounds in all major ecological activities. Recent studies have allowed the characterization of chemoreceptor gene families. These genes present strikingly high variability in copy numbers and pseudogenization degrees among different species, but the mechanisms underlying their evolution are not fully understood. We have analyzed the functional networks of these genes, their orthologs distribution, and performed phylogenetic analyses in order to investigate their evolutionary dynamics. We have modeled the chemosensory networks and compared the evolutionary constraints of their genes in Mus musculus, Homo sapiens, and Rattus norvegicus. We have observed significant differences regarding the constraints on the orthologous groups and network topologies of chemoreceptors and signal transduction machinery. Our findings suggest that chemosensory receptor genes are less constrained than their signal transducing machinery, resulting in greater receptor diversity and conservation of information processing pathways. More importantly, we have observed significant differences among the receptors themselves, suggesting that olfactory and bitter taste receptors are more conserved than vomeronasal receptors.
