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- Exosomal aβ-binding proteins identified by “in silico” analysis represent putative blood-derived biomarker candidates for alzheimer´s diseasePublication . Martins, Tânia Soares; Marçalo, Rui; Ferreira, Maria; Vaz, Margarida; Silva, Raquel M.; Rosa, Ilka Martins; Vogelgsang, Jonathan; Wiltfang, Jens; Silva, Odete A. B. da Cruz e; Henriques, Ana GabrielaThe potential of exosomes as biomarker resources for diagnostics and even for therapeutics has intensified research in the field, including in the context of Alzheimer´s disease (AD). The search for disease biomarkers in peripheral biofluids is advancing mainly due to the easy access it offers. In the study presented here, emphasis was given to the bioinformatic identification of putative exosomal candidates for AD. The exosomal proteomes of cerebrospinal fluid (CSF), serum and plasma, were obtained from three databases (ExoCarta, EVpedia and Vesiclepedia), and complemented with additional exosomal proteins already associated with AD but not found in the databases. The final biofluids’ proteomes were submitted to gene ontology (GO) enrichment analysis and the exosomal Aβ-binding proteins that can constitute putative candidates were identified. Among these candidates, gelsolin, a protein known to be involved in inhibiting Abeta fibril formation, was identified, and it was tested in human samples. The levels of this Aβ-binding protein, with anti-amyloidogenic properties, were assessed in serum-derived exosomes isolated from controls and individuals with dementia, including AD cases, and revealed altered expression patterns. Identification of potential peripheral biomarker candidates for AD may be useful, not only for early disease diagnosis but also in drug trials and to monitor disease progression, allowing for a timely therapeutic intervention, which will positively impact the patient’s quality of life.
- Essential genetic findings in neurodevelopmental disordersPublication . Cardoso, Ana R.; Lopes-Marques, Mónica; Silva, Raquel M.; Serrano, Catarina; Amorim, António; Prata, Maria J.; Azevedo, LuísaNeurodevelopmental disorders (NDDs) represent a growing medical challenge in modern societies. Ever-increasing sophisticated diagnostic tools have been continuously revealing a remarkably complex architecture that embraces genetic mutations of distinct types (chromosomal rearrangements, copy number variants, small indels, and nucleotide substitutions) with distinct frequencies in the population (common, rare, de novo). Such a network of interacting players creates difficulties in establishing rigorous genotype-phenotype correlations. Furthermore, individual lifestyles may also contribute to the severity of the symptoms fueling a large spectrum of gene-environment interactions that have a key role on the relationships between genotypes and phenotypes.Herein, a review of the genetic discoveries related to NDDs is presented with the aim to provide useful general information for the medical community.
- Merging microarray studies to identify a common gene expression signature to several structural heart diseasesPublication . Fajarda, Olga; Duarte-Pereira, Sara; Silva, Raquel M.; Oliveira, José LuísBackground: Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used. Results: Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified. Conclusions: We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.