Gene Expression Profiles of Visceral and Subcutaneous Adipose Tissues in Children with Overweight or Obesity: The KIDADIPOSEQ Project

Childhood obesity is a multifactorial disease influencing the development of a range of metabolic disorders, where adipose tissue has been proved to be fundamental. The adipose tissue can be distributed throughout the body as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), and there are considerable anatomical differences between both adipose tissues in the body. Importantly, VAT is associated with low-grade systemic inflammation and insulin resistance, which are key factors underlying metabolic alterations associated with childhood obesity [1]. This study aimed to identify the molecular signatures underlying obesity and overweight in children, differentiating between shared and individual signatures in VAT and SAT. Both tissue samples were collected from 18 children (11 girls) aged 0.54 to 16.63 years and hospitalized for abdominal surgery, of which 6 children (2 girls) had overweight or obesity. RNAseq analysis was performed to identify gene expression patterns associated with obesity and overweight in each tissue. The software tools used in the RNAseq data analysis were FastQC, to perform sequencing quality checks; HISAT, to map reads to the human genome; featureCounts to quantify raw counts; and DESeq2 to differential gene expression analysis. In VAT there were 759 genes showing statistically significant differential expression between groups (nominal p-value < 0.05), from which 48 passed an FDR threshold of 0.05. VAT’s differential expression results may be observed in Fig. 1‐2. In SAT there were 945 genes showing statistically significant differential expression, from which 28 passed the FDR threshold. SAT’s differential expression results are shown in Fig. 3‐4. We were specially interested in the identification of shared genes associated with overweight and obesity in both tissues, for which we performed a gene ontology analysis of all differently expressed genes, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Among significantly associated genes, there were 126 common genes, as it is shown in Fig. 5. The reliability of our results was assessed by matching our list of significant genes with a list of genes whose influence on obesity has been previously described in the literature (e.g., LEP and TNMD) [2, 3]. Additionally, our research identified new molecular targets, highlighting the results of VAT (i.e., XIST, PRKY and TTTY10). In conclusion, our approach identified independent and shared gene expression patterns in VAT and SAT associated with overweight and obesity in children. Understanding the molecular architecture of obesity with approaches like these is crucial for the identification of powerful molecular targets and developing of effective precision medicine therapies.