Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Quantifying the lipidome for respiratory disease: A rapid and comprehensive targeted approach (#567)

Nyasha Munjoma 1 , Giorgis Isaac 2 , David Heywood 1 , Lee Gethings 1 , Robert Plumb 2
  1. Waters Corporation, Wilmslow, CHESHIRE, United Kingdom
  2. Waters Corporation, Milford, MA, USA

Respiratory linked conditions associated with chronic obstructive pulmonary disease (COPD), asthma and infection are increasing with associated socio-economic costs. Recent reports have shown costs to exceed ₤11 billion per year for cases recorded in the UK. COPD in particular is a heterogeneous disease which is a major cause of illness and death worldwide. The combination of genetic and lifestyle factors are known to contribute towards increasing the probability of encountering the condition. Here, we describe a lipidomic approach to reveal molecular factors that may be involved in these biomolecular processes.

The analyses of plasma samples from three biological states of varying phenotype (control, COPD and asthma patients) were conducted. Lipid analysis was performed using LipidQuan, which is a streamlined and integrated lipidomic workflow. This platform consists of highly specific MRM transitions based on the fatty acyl chain fragments which were used for identification and quantification of multiple lipid species. Chromatographic conditions allowed for the separation of individual lipid classes with a complete analysis time of 8 minutes per sample. Data were processed using both TargetLynx and Skyline. Statistical analysis was conducted using SIMCA and additional data visualisation provided by MetaboAnalyst.

Biological significance of the results was established by merging the data from all experiments and performing pathway analysis. Statistical analysis of the data revealed clear separation between the various cohorts. Unsupervised PCA resulted in separation of healthy controls, COPD and asthma patients. Application of the metadata also revealed significant differences between smoking status, with subsets readily observed within the COPD population. Loadings analysis revealed FFA, LPC, PC and SM classes to be the main contributors to sample type clustering. Pathway analysis revealed a number of components related to inflammation, oxidative and immunity processes were identified as significant and associated with signalling, metabolic and regulatory pathways.