Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils

Abstract

Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).

Data fusion discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data

“…16 Case III. As another example, GC-MS data and the bioactivity effect against the larval population of Aedes aegypti 17 is also demonstrated to describe Option 4 (MS + Bioactivity correlations) (Figure 4-A). The .csv file with the correlations output from STOCSY indexed according to each MS-feature was imported as a node attribute to the available molecular network to produce a simplified view of the chemical space analyzed (Figure 4-B).…”

“…Molecular network from 17 where the nodes were color coded according to the resulting correlation output from STOCSY using the larvicidal effect against A. aegypti (B). Published data 17 , provided by Dr. Alan Pilon (USP, Brazil).…”

“…This dataset was provided and coordinated with Professor Camila Manoel Crnkovic (USP, Brazil). Case III represents a dataset comprising GC-MS and a bioassay against the larval population of Aedes aegypti 17 . This dataset was provided and coordinated with Dr. Alan Pilon (USP, Brazil).…”

“…Output scheme from DAFdiscovey on a set of GC-MS and bioactivity data. The retention time vs scan number scatterplot shows MS features highly correlated to the bioactivity readouts (A).Molecular network from17 where the nodes were color coded according to the resulting correlation output from STOCSY using the larvicidal effect against A. aegypti (B). Published data17 , provided by Dr. Alan Pilon (USP, Brazil).…”

Data fusion discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data

“…Thus, the goal here is to spot highly correlated MSfeatures with negative covariance. Molecular network from 17 where the nodes were color coded according to the resulting correlation output from STOCSY using the larvicidal effect against A. aegypti (B). Published data 17 , provided by Dr. Alan Pilon (USP, Brazil).…”