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Recamonde-Mendoza, M.

Publications and source records attributed to Recamonde-Mendoza, M..

2 recordsLinked to original sources

Microarray, validation and gene-enrichment approach for assessing differentially expressed circulating miRNAs in obese and lean heart failure patients

BackgroundObesity is a risk factor associated with cardiovascular diseases that may lead to heart failure (HF). However, in HF, overweight and obese patients have longer survival than underweight patients, a phenomenon known as the obesity paradox. MiRNAs play a fundamental role in gene regulation and transcription factors involved in obesity and HF. The main objective of this study was to identify and validate differentially expressed circulating miRNAs in HF-obese and HF-lean patients. Materials and MethodsThis case-control study was carried out in two parts; the discovery and validation phase. In the discovery phase, plasma samples from 20 HF patients and from 10 healthy controls for microarray analysis were selected. The miRNAs were extracted and then analyzed on the miRNA 4.0 Affymetrix GeneChip array following the manufacturers instructions. Raw data were normalized using Robust Microarray Average (RMA), batch effects were adjusted with Surrogate Variable Analysis (SVA), and differential expression analysis was performed with the Limma R package. Differentially expressed miRNAs were ranked based on effect size, p-value, and biological plausibility, and selected for validation. In the validation phase, plasma miRNAs from 80 patients and controls were extracted and miRNAs -451a, - 22-3p, and -548ac were validated by quantitative reverse transcription-PCR. Then, they were subjected to target analysis using miRTarBase release 8.0 and TarBase v8.0. We performed functional enrichment analysis of miRNA targets using pathway annotation from the KEGG Pathway Database using the ClueGO software. ResultsMiRNAs -451a, -22-3p, and -548ac were up-regulated in HF-lean and HF-obese groups compared to control, showing an association of these miRNAs with HF. Enrichment analysis showed an association of validated miRNAs with AKT1, MAPK1, GRB2, and IGF1R genes. These genes are associated with different biological processes, such as apoptosis, carbohydrate metabolism, neurogenesis, sugar transport, translation regulation, transport, cell cycle, actin cytoskeleton reorganization, cardiac neural crest cell development involved in heart development, and aging. ConclusionmiR-451a, miR-22-3p, and miR-548ac are up-regulated in HF, independent of obesity, and are associated with metabolic, morphological, and functional outcomes. These results might help in the selection of targets for studies aiming to underscore the mechanisms of HF.

genomics↗

Pynoma, PyABraOM and BIOVARS: Towards genetic variant data acquisition and integration

MotivationAdvances in genomic sequencing of human populations have generated a large amount of genomics data deposited in multiple sources. Programmatic batch searches executed at once are of great scientific interest to ease genomic investigations by retrieving and integrating this massive and decentralized data with little manual intervention. ResultsPynoma and PyABraOM APIs were developed to offer multiple queries in gnomAD and ABraOM databases, respectively. A centralized search in these databases with data integration is offered by a third API, BIOVARS, which combines the resulting information with statistical and graphical visualizations. The implemented features are demonstrated in a case study using ACE2, ADAM17 and TMPRSS2 genes, which presents a generalizable workflow that shows how our APIs facilitate the access and integration of valuable biological data. AvailabilityAll the APIs are written in Python 3. Graphical visualizations for the retrieved data are provided by using the R language version 4.1. The source codes are publicly available and hosted on GitHub (github.com/bioinfo-hcpa). Contactumatte@hcpa.edu.br Supplementary informationSupplementary data is available at github@nbioinfo-hcpa.

bioinformatics↗