Scientific Reports · 2017 · 63 citations · 44 references
Life SciencesMolecular BiologyPhysiologyGene ExpressionMetabolismPathway AnalysisAdipocyte DifferentiationAdipose TissueLipid MetabolismMetabolic SignalingAdipocyte BiologyAdipocyte MetabolismAdipogenic DifferentiationAdipose Tissue BrowningBeige Adipocyte DifferentiationKegg Pathway EnrichmentWhite Adipocyte DifferentiationWhite Adipose Tissue Biology
The Simpson Golabi Behmel Syndrome (SGBS) pre-adipocyte cell strain is widely considered to be a representative in vitro model of human white pre-adipocytes. A recent study suggested that SGBS adipocytes exhibit an unexpected transient brown phenotype. Here, we comprehensively examined key differences between SGBS adipocytes and primary human white subcutaneous (PHWSC) adipocytes. RNA-Seq analysis revealed that extracellular matrix (ECM)-receptor interaction and metabolic pathways were the top two KEGG pathways significantly enriched in SGBS adipocytes, which included positively enriched mitochondrial respiration and oxidation pathways. Compared to PHWSC adipocytes, SGBS adipocytes showed not only greater induction of adipogenic gene expression during differentiation but also increased levels of UCP1 mRNA and protein expression. Functionally, SGBS adipocytes displayed higher ISO-induced basal leak respiration and overall oxygen consumption rate, along with increased triglyceride accumulation and insulin-stimulated glucose uptake. In conclusion, we confirmed that SGBS adipocytes, which are considered of white adipose tissue origin can shift towards a brown/beige adipocyte phenotype. These differences indicate SGBS cells may help to identify mechanisms leading to browning, and inform our understanding for the use of SGBS vis-à-vis primary human subcutaneous adipocytes as a human white adipocyte model, guiding the selection of appropriate cell models in future metabolic research.
44
Trimmomatic: a flexible trimmer for Illumina sequence data
Anthony Bolger, Marc Lohse, Björn Usadel · Bioinformatics · 2014 · 69.7K citations · Full text
HTSeq—a Python framework to work with high-throughput sequencing data
Simon Anders, Paul Theodor Pyl, Wolfgang Huber · Bioinformatics · 2014 · 22.7K citations · Full text