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Cavalcante, G. C.

Publications and source records attributed to Cavalcante, G. C..

3 recordsLinked to original sources

mtDNA-Network: a web tool to explore mitochondrial variant profiles in complex diseases

The mitochondrial genome (mtDNA) provides valuable insights into human evolution, population diversity, and disease etiology. Here, we present the mtDNA-network (https://apps.lghm.ufpa.br/mtdna/), an integrative bioinformatics database and tool for the visualization and analysis of mitochondrial variants (single-nucleotide variants and insertions/deletions). The mtDNA-network was upgraded to enable investigation of mtDNA in admixed Brazilian individuals with substantial contributions from uniparental Indigenous and African ancestries. We implement a bioinformatics pipeline to harmonize variant calling across 339 mtDNA samples. The dataset supports general genetic population analysis and variant mapping for complex diseases, including Parkinsons disease (104 cases and 75 controls), leprosy (33 cases and 37 controls), and somatic gastric cancer (40 cases and 50 controls). The mtDNA-network tool features an intuitive interface and analytical dashboards for transitions, transversions, heteroplasmy, and variant-disease networks, and serves as a strategic resource for advancing research in population genetics and precision medicine in underrepresented populations. We reinforce the critical need to expand non-European genomic representation in global databases to promote more equitable genomic diversity studies and clinically relevant discoveries.

bioinformatics↗

An Open-Source Code To Analyze Mitochondrial Intracellular Distribution From Fluorescence Microscopy Images

Mitochondria have a plethora of roles in cells, many of which are related to dynamic changes in their size, shape, and intracellular location. Mitochondrial morphology is commonly assessed by microscopy with targeted fluorescent probes. However, tools to easily estimate mitochondrial localization within a cell are still lacking. A code was designed to estimate per-cell mitochondrial radial localization (perinuclear or peripheral) from fluorescence microscopy files in a variety of formats and using different mitochondrial markers (https://github.com/cavalcantegc/mito_localization.git). Three case studies with different cell types and stainings demonstrate that mitochondrial localization can be easily extracted and plotted with this code.

cell biology↗

Downregulation of the Ca2+ sensor Synaptotagmin-1 (SYT1) in Parkinson's Disease: Insights from Gene Expression Profiling

Parkinsons disease (PD) is a neurodegenerative disease characterized by the progressive loss of dopaminergic neurons and by the intracellular accumulation of alpha-synuclein, leading to motor and non-motor symptoms. Despite being a widely studied disease, so new mechanisms should be investigated as possible paths for future diagnostics and treatments in PD. Here, we performed an in silico analysis of the global gene expression of tissues from different brain regions (prefrontal area, putamen, and substantia nigra) in PD patients and controls, to demonstrate differentially expressed genes (DEGs). We analyzed the dataset series GSE20295 from GEO, which comprises GSE20168, GSE20291, and GSE20292. We identified 13 DEGs, all exhibiting downregulation in PD tissues compared to controls. Notably, the SYT1 (Synaptotagmin-1) gene demonstrated the lowest expression level and nearly the most significant adjusted p-value. SYT1 is implicated in calcium ion sensor activity, a functional domain showing substantial fold enrichment in our study. This gene encodes a protein pivotal for neurotransmitter release at synapses. We also found a significant role of calcium-related processes in PD pathology through GSEA, indicating an overall increase in their activity and a disruption in neurotransmitter release mechanisms mediated by Ca2+. Despite limited research on the correlation between SYT1 and PD to date, our findings suggest the SYT1 gene holds promise as a possible target for PD. Further investigations are needed to elucidate this association fully.

bioinformatics↗