Biology subjects

Silva, W. A.

Publications and source records attributed to Silva, W. A..

4 recordsLinked to original sources

bioRxiv2021

Multidimensional Single-Nuclei RNA-Seq Reconstruction of Adipose Tissue Reveals Adipocyte Plasticity Underlying Thermogenic Response

Adipose tissue has been classified based on its morphology and function as white, brown, or beige / brite. It plays an essential role as a regulator of systemic metabolism through paracrine and endocrine signals. Recently, multiple adipocyte subtypes have been revealed using RNA sequencing technology, going beyond simply defined morphology but by their cellular origin, adaptation to metabolic stress, and plasticity. Here, we performed an in-depth analysis of publicly available single-nuclei RNAseq from adipose tissue and utilized a workflow template to characterize adipocyte plasticity, heterogeneity, and secretome profiles. The reanalyzed dataset led to the identification of different subtypes of adipocytes including three subpopulations of thermogenic adipocytes and provided a characterization of distinct transcriptional profiles along the adipocyte trajectory under thermogenic challenges. This study provides a useful resource for further investigations regarding mechanisms related to adipocyte plasticity and trans-differentiation. HighlightsMultidimensional transcriptome analysis at single-nucleus resolution recovers nuclei of cell types in adipose tissue Adaptative thermogenic response results in 3 distinct mature adipose cell types Single-nuclei transcriptomic-based secretome analysis reveals adipose cell-type-specific genes The in vivo trajectory of adipocyte plasticity for thermogenic response reveals sets of trans-differentiation genes Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/431320v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@291767org.highwire.dtl.DTLVardef@1bfa3b1org.highwire.dtl.DTLVardef@93e051org.highwire.dtl.DTLVardef@6bef5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

5-Azacytidine: Effects on the Expression of alpha-Cardiac Actin in Pericytes from Human Adipose Tissue

DNA methylation patterns are closely related to the chromatin structure, and its remodeling is considered an important mechanism in the control of gene transcription during cell differentiation. In rodent, several studies have related the possibility that multipotent mesenchymal stromal cells (MSCs) undergo cardiomyogenesis. However, it has not been completely elucidated if human adult stem cell exhibits true differentiation potential for a cardiac lineage. In this study, the action of the DNA methylation inhibitor 5-azacytidine (5-aza) was examined in human adipose tissue pericytes (hATPCs: 3G5+) regarding their possible capacity to induce myocytes in vitro. Real-Time PCR revealed that cells treated with 5-aza presented time-dependent decrease in the mRNA expression of -cardiac actin (-CA). At 24 h, this diminution was statistically significant; however, there was not a correlation with the highest level of DNA demethylation at the same period using Methylation-Sensitive High Resolution Melting-PCR (MS-HRM-PCR). An evident increase in the -CA protein expression was observed by Western blotting in hATPCs treated with 5-aza at 24 h. The mRNA expression of -SMA (-smooth actin) also showed a time-dependent decrease after the treatment, however, it was not significant. The ultrastructural analysis showed similar structures such as like-cell junctions, caveolae, and actin myofilaments, which aligned in parallel. These phenotypic alterations were found only after the treatment; however, the hTAPCs after 5-aza treatment were not able to form thick myofilaments and consequently sarcomeres. These results indicated that a terminal cardiac differentiation of hTAPCs was not achieved and that the cardiomyogenesis failure could be related to the non-muscle origin of the adipose tissue.

cell biology↗

bioRxiv2020

Genetic variability in COVID-19-related genes in the Brazilian population

SARS-CoV-2 employs the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane serine protease (TMPRSS2) to infect human lung cells. Previous studies have suggested that different host genetic backgrounds in ACE2 and TMPRSS2 could contribute to differences in the rate of infection or severity of COVID-19. Recent studies also showed that variants in 15 genes related to type I interferon immunity to influenza virus could predispose to life-threatening COVID-19 pneumonia. Additional genes (SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, IL6, CTSL, ABO, and FURIN) and HLA alleles have also been implicated in response to infection with SARS-CoV-2. Currently, Brazil has recorded the third-highest number of COVID-19 patients worldwide. We aim to investigate the genetic variation present in COVID-19-related genes in the Brazilian population. We analysed 27 candidate genes and HLA alleles in 954 admixed Brazilian exomes. We used the information available in two public databases (http://www.bipmed.org and http://abraom.ib.usp.br/), and additional exomes from individuals born in southeast Brazil, the region with the highest number of COVID-19 patients in the country. Variant allele frequencies were compared with the 1000 Genomes Project phase 3 (1KGP) and the gnomAD databases. We found 395 non-synonymous variants; of these, 325 were also found in the 1000 Genome Project phase 3 (1KGP) and/or gnomAD. Six of these variants were previously reported as putatively influencing the rate of infection or clinical prognosis for COVID-19. The remaining 70 variants were identified exclusively in the Brazilian sample, with a mean allele frequency of 0.0025. In silico prediction of the impact in protein function revealed that three of these rare variants were pathogenic. Furthermore, we identified HLA alleles that were previously associated with COVID-19 response at loci DQB1 and DRB1. Our results showed genetic variability common to other populations, but also rare and ultra-rare variants exclusively found in the Brazilian population. These findings could potentially lead to differences in the rate of infection or response to infection by SARS-CoV-2 and should be further investigated in patients with the disease.

genetics↗

bioRxiv2020

CeTF: an R package to Coexpression forTranscription Factors using Regulatory ImpactFactors (RIF) and Partial Correlation andInformation (PCIT) analysis

SummaryFinding meaningful gene-gene associations and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. CeTF is an R package that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems -- for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. AvailabilityCeTF is available as R package in Bioconductor (https://bioconductor.org/packages/CeTF), GitHub (https://github.com/cbiagii/CeTF) and as docker image (https://hub.docker.com/r/biagii/cetf). More information on how to use the package can be found in the Supplemental File 1.

bioinformatics↗