Cross-species transcriptomics identifies obesity associated genes between human and mouse studies

Abstract

Background: Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined 'health weight'. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions.

Methods: The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty.

Results: Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues.

Conclusions: The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

Keywords: Multi omics; Obesity; Transcriptomics; Translational medicine.

Fig. 1

Overview of the gene target identification workflow using public and internal RNA sequencing data

Fig. 2

Differential gene expression analysis of mouse datasets GSE39375 and GSE49195. A PCA of GSE39375 data used to visualised lean vs. obese samples from mice. B Volcano plots used to visualise up and down regulated genes across lean and obese samples for GSE39375 dataset. C PCA of GSE49195 data used to visualise the lean vs. obese samples from mice. D Volcano plots used to visualise up and down regulated genes across lean and obese samples for GSE49195 dataset

Fig. 3

Differential gene expression analysis of public human datasets GSE24883 and GSE59034. A PCA for the GSE24883 used to visualise lean vs. obese patient adipose tissue samples B Volcano plots used to visualise up and down regulated genes across lean and obese samples for GSE24883 dataset. C PCA for the GSE59034 used to visualise the lean vs. obese samples D Volcano plots used to visualise up and down regulated genes across lean and obese samples for GSE59034 dataset

Fig. 4

Differential gene expression analysis of internal human data set GSE219027. A PCA score plot showing the differences between obese and lean patient joint tissue samples. Red and green ellipse indicates confidence interval of the patient cohorts B Volcano plots used to visualise up and down regulated genes across lean and obese patient joint tissue samples

Fig. 5

The two groups of correlated features identified by the power function are represented by the group member with the largest observed effect size. The effect size of each assessed variable is shown along the y-axis and a series of sample sizes along the x-axis. Power values determined for each effect/sample size combination using a simulated dataset with the same correlation structure as input data and displayed using variably sized/coloured rhombi

Fig. 6

Validation of identified genes of interest in human tissue. Up and down regulated genes from computational analysis were validated using qPCR experiment A CASP1 B IGFBP2 C DOK6 are plotted, where each dot represents an individual patient, error bars represent standard error of the mean and p-value was calculated using unpaired student’s t-test. D Heatmap summarising average expression of the selected genes across lean (NW) and obese (OB) human synovial tissue samples. Red indicated high expression values and blue indicated lower expression. Individual plots can be found in Supplementary Fig. 3