Poster Presentation ESA-SRB-APEG-NZSE 2022

An investigation of the plasma lipidome using a mouse model of endometriosis and pelvic pain (#422)

Disha Shah 1 , Berin Boughton 1 , Joel Castro 2 , Stuart Brierley 2 , Peter Rogers 3 , Jane Girling 4 , Sarah Holdsworth-Carson 5
  1. Australian National Phenome Centre, Harry Perkins Institute of Medical Research, Murdoch, WA, Australia
  2. South Australian Health and Medical Research Institute, University of Adeliade, Adelaide, SA, Australia
  3. Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia
  4. Department of Anatomy, University of Otago, Dunedin, New Zealand
  5. Julia Argyrou Endometriosis Centre, Epworth HealthCare, Ease Melbourne, VIC, Australia

Aims

Endometriosis can be modelled in mice with features (ectopic lesions) and symptoms (pain) of the disease echoing human aetiology.  Furthermore, mouse models of endometriosis have demonstrated altered metabolism (aberrant plasma and peritoneal fluid metabolite and lipid profiles).  The aim of this study was to analyse the plasma metabolome/lipidome from sham and endometriosis mice to determine if altered metabolic profiles could be:

  • linked to pathways associated with poor cardiometabolic health outcomes, and
  • employed as biomarkers to diagnose or monitor disease phenotypes.

Methods

Autologous transplantation of uterine horn fragments (or fat, sham surgery) in female C57BL/6J mice were performed to induce endometriosis (HREC number SAM342).  Uterine horn fragments were attached to the small intestinal mesentery and alongside the uterus.  Histological assessment to confirm lesion (or sham) morphology was assessed between week 8-10.  Plasma from n=6 endometriosis and n=5 sham controls were also collected (stored -80°C) at 8-10 weeks.  Plasma was analysed using untargeted high-throughput liquid chromatography electrospray ionisation coupled with tandem MS (LC ESI-MS/MS) using reverse-phase LC and trapped ion mobility spectrometry time-of-flight (TIMS-TOF) (Bruker Daltonics).  MetaboScape software was used for spectral data processing and databank searching.

Results

A total of 5651 m/z features were detected from plasma across positive and negative modes.  Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed m/z features that differed in the endometriosis group compared to sham controls (1146.52, 677.55, 816.58, 990.66).  The metabolite classes of interest were triradylglycerols, glycerophosphates, glycerophosphoglycerols, and ceramides.

Conclusions

This pilot study using high-throughput MS-based lipidomics successfully identified 5651 m/z features in an endometriosis mouse model.  Case-control analysis revealed differing metabolic profiles in association with endometriosis.  Further validation is warranted, including comparisons to matched tissues (autologous endometrial lesions).  Discovery of aberrant metabolic profile biomarkers using plasma offers a minimally-invasive approach to better diagnose disease and increase our understanding of endometriosis pathophysiology.