Molecular phenotyping of sub-groups within a study population using next generation sequencing

The promise of omics technologies to identify diet and lifestyle interventions that restore, maintain or improve health status has yet to deliver effective strategies. However, studies using omics technologies to identify molecular phenotypes demonstrate unequivocally the inherent inter-individual variation in study participants and importantly their varied responsiveness to the interventions being tested. Previous studies identified distinct molecular sub-groups within a study population based on whole blood profiles measured by our custom multiplex assay of 28 cell defence gene markers. Molecular subgroups defined at baseline and characterised by differential SIRT1 were associated with markers of health status. SIRT1, associated with aging, oxidative and metabolic stress, has established roles in obesity and cancer and promotes metabolic efficiency in vivo. The molecular subgroup with lower levels of SIRT1 had reduced plasma HDL, increased TNFα and demonstrated a suppressed post-prandial response. This phenotype may reflect compromised SIRT1 stress responses and a key role in health status. More in-depth molecular phenotyping is required to gain further insight into molecular networks and biological processes associated with dysregulated SIRT1. This study used next generation sequencing to explore differential gene expression across the transcriptome in healthy individuals stratified according to SIRT1 levels. Total RNA was extracted from fasted whole blood (males 21 to 60 years with no evidence of chronic illness) (n=16) collected using PAXgene® blood RNA tubes. Illumina dual indexed TruSeq mRNA libraries were prepared from 500ng total RNA (duplicate samples from each participant collected on two separate occasions). Libraries were quantified by qPCR, equimolar pooled, sequenced at 32plex on a NextSeq500 with 75bp single reads and 129 Gb total Q30 output. Reads were quality and adapter trimmed, aligned to reference, globin reads were removed and reads were normalised in DESeq2. Normalised reads were imported to Partek Genomics Suite v6.6 for differential gene expression and downstream Pathway and Gene Ontology enrichment analyses. Principal component analysis of the blood transcriptome clustered individuals by SIRT1 group. 404 genes were differentially expressed between low and high SIRT1 groups (< vs. ≥ median SIRT1; P<0.05 and Fold change ≥1.5). GO analysis identified cell senescence, aging, lipid metabolism, cellular homoeostasis and cellular response to nutrient levels as significantly enriched in this gene set. Pathway enrichment analysis identified cell cycle and wnt signalling as candidate SIRT1 regulated networks. The data supports SIRT1 as a candidate health status biomarker and dysregulated SIRT1 molecular networks warrant further investigation.