Integrating Metabolomics and Computational Tools to Discover Anti-Inflammatory Agents in Ocotea Species

Abstract

The quest for innovative anti-inflammatory agents with reduced side effects is a compelling area of interest in pharmaceutical research. The inhibition of prostaglandin E2 (PGE2) and leukotriene (LTB4) releasing are significant as they are key mediators in different inflammatory diseases. Natural Products (NP) offer a rich source of structurally diverse and functionally distinct specialised metabolites that can assist in the discovery of novel therapeutic agents. In this context, our research explored the ex vivo anti-inflammatory potential of 60 plant extracts from the Ocotea genus, most studied for the first time. Our objectives consist of applying metabolomics and computational tools to annotate biomarkers associated with anti-inflammatory activity. Machine learning models were also built to predict novel anti-inflammatory Ocotea extracts. The chemical composition of various endemic and threatened Ocotea species from different biomes in Brazil were analysed, leading to the annotation of multiple classes of specialized metabolites. Chapter I provides a classic review of all the identified metabolites within the Ocotea genus, culminating in the creation of a comprehensive in-house database named OcoteaDB. This literature review also explores the relevant biosynthetic pathways of the bioactive chemical scaffolds found in the genus. Chapter II is a research article of a metabolomics study that revealed the PGE2 inhibition release of Ocotea spp. can be majorly attributed to aporphine alkaloids. By employing ultra-performance liquid chromatography coupled with highresolution mass spectrometry (UPLC-MS) combined with OcoteaDB, we achieved a rapid and reliable annotation process. Chapter III is a research article on the dual COX/LOX inhibitory biomarkers in promising Ocotea species using a concatenated UPLC-MS - Nuclear Magnetic Resonance (NMR) metabolomics approach and machine learning prediction models. This time, alkaloids, a glycosylated flavonoid and a sesquiterpenoid were correlated with the potential dual anti-inflammatory activity. Chapter IV solidifies the significance of the Ocotea genus as a producer of different classes of alkaloids, lignoids and glycosylated flavonoids using gas-phase fragmentation reactions and molecular networking in the annotation procedure. A UPLC-MS-DIA (data-independent acquisition) open pipeline was developed by integrating data conversion, processing, and metabolomics tools analysis. This thesis provided valuable insights into the metabolome and anti-inflammatory profile of several unstudied Ocotea species, as well as a pipeline for future NP research computational analyses. The scientific knowledge generated can be applied in the search for new anti-inflammatory compounds and also contributes to highlighting the importance of conserving Ocotea species in Brazil, encouraging their preservation for future research.