RNA- SEQ ANALYSIS OF DIFFERENTIAL GENE EXPRESSION PATTERNS IN SUBCUTANEOUS ADIPOSE TISSUE BIOPSIES FROM PEOPLE WITH LOW VERSUS NORMAL BIRTH WEIGHT – IMPLICATIONS FOR RISK OF DEVELOPING TYPE2 DIABETES

Background: Fetal development and programming has lifelong implications for heath and risk of disease, and overnutrition or undernutrition is known to cause fetal adaptations and developmental changes via epigenetic mechanism. Such adaptations can play an important factor in perspective to metabolic disorders including risk of type 2 diabetes (T2D). Low birthweight (LBW) may also result in reduced adult height, increased abdominal obesity and various metabolic risk factors including non-alcoholic fatty liver disease - (NAFLD), which is on the path to development of T2D. Metabolic changes in subcutaneous adipose tissue (SAT) of LBW individuals has taken a vivid role in causing the variation of metabolic traits. In this comparative study, SAT gene expression patterns were compared between age- and BMI matched LBW and normal birthweight (NBW) aged 37 years. Objective: To compare RNA expression levels from SAT between LBW and NBW subjects to understand the molecular mechanisms underlying increased risk of T2D in people born with LBW. Methods: A total of 133 samples were analysed via RNA sequencing, which includes 85 adipose tissue samples (i.e., from baseline, overfeeding and randomization) and 48 preadipocyte samples. For my thesis, the analysis of only the samples from baseline biopsies was included. Non-stranded and polyA-selected mRNA library preparation has been done on all samples, followed by PE100 sequencing resulting fastq files. The pipeline included FastQC tool for quality check, STAR for Alignment, Featurecounts for quantifying, EdgeR for the differential expression analysis. Pathway analysis was done using Reactome. Results: Fastqc reports were generated and the data was in good quality and met the standards. After the alignment and quantifying, GeneCounts file with a total of 60483 gene count data was obtained. Among the groups of LBW (n=17) vs NBW (n=12) there were 50 significant genes without FDR adjustment (pvalue<0.05), when all features are considered. In contrast, 31 significant differential gene expression levels were found when only protein coding genes were considered. Pathway analysis showed the significant pathways like metallothioneins bind metals, response to metal ions regulation of complement cascade and peptide ligand-binding receptors. Network analysis of these results co-relates with the areas of signal transduction, metabolism, gene expression in developmental biology and associated networks. Conclusion: Differential SAT gene expression levels were identified between LBW at increased risk of T2D compared with matched NBW controls. Interestingly, these genes associated with the cellular ion homeostasis, apoptotic process, cellular response to stimuli and stress were found among the negative log fold change (logFC) genes. Whereas, negative logFC genes were seen in the pathways related to lipid metabolic process, cholesterol homeostasis, steroid and glycoprotein metabolic pathways. These differences may play a role for the increased risk of T2D in LBW subjects.