Metabolite mass spectrometry profiling of cacao genotypes reveals contrasting resistances to Ceratocystis cacaofunesta phytopathogen.

Ceratocystis wilt is a lethal disease of cacao, and the search for resistant genotypes may provide the best way to deal with the disease. Resistance or susceptibility behavior of some cacao genotypes when infected by Ceratocystis cacaofunesta is not yet understood. Herein, we report an LC-MS metabolomic screening analysis based on high-resolution MS to obtain comprehensive metabolic profile associated with multivariate data analysis of PLS-DA, which was effective to classify CCN-51 and TSH-1188 as resistant genotypes to C. cacaofunesta fungus, while CEPEC2002 was classified as a susceptible one. Using reversed-phase LC method, electrospray interface, and high-resolution tandem MS by the quadrupole-TOF analyzer, the typical profiles of metabolites, such as phenylpropanoids, flavonoids, lipids, alkaloids, and amino acids, were obtained. Untargeted metabolite profiles were used to construct discriminant analysis by partial least squares (PLS-DA)-derived loading plots, which placed the cacao genotypes into two major clusters related to susceptible or resistant groups. Linolenic, linoleic, oleic, stearic, arachidonic, and asiatic acids were annotated metabolites of infected, susceptible, and resistant genotypes, while methyl jasmonate, jasmonic acid, hydroxylated jasmonic acid, caffeine, and theobromine were annotated as constituents of the resistant genotypes. Trends of these typical metabolites levels revealed that CCN51 is susceptible, CEPEC2002 is moderately susceptible, and TSH1188 is resistant to C. cacaofunesta. Therefore, profiles of major metabolites as screened by LC-MS offer an efficient tool to reveal the level of resistance of cacao genotypes to C. cacaofunesta present in any farm around the world.

Major phytopathogens and strains from cocoa (Theobroma cacao L.) are differentiated by MALDI-MS lipid and/or peptide/protein profiles.

Phytopathogens are the main disease agents that promote attack of cocoa plantations in all tropical countries. The similarity of the symptoms caused by different phytopathogens makes the reliable identification of the diverse species a challenge. Correct identification is important in the monitoring and management of these pests. Here we show that matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in combination with multivariate data analysis is able to rapidly and reliably differentiate cocoa phytopathogens, namely Moniliophthora perniciosa, Phytophthora palmivora, P. capsici, P. citrophthora, P. heveae, Ceratocystis cacaofunesta, C. paradoxa, and C. fimbriata. MALDI-MS reveals unique peptide/protein and lipid profiles which differentiate these phytopathogens at the level of genus, species, and single strain coming from different hosts or cocoa tissues collected in several plantations/places. This fast methodology based on molecular biomarkers is also shown to be sufficiently reproducible and selective and therefore seems to offer a suitable tool to guide the correct application of sanitary defense approaches for infected cocoa plantations. International trading of cocoa plants and products could also be efficiently monitored by MALDI-MS. It could, for instance, prevent the entry of new phytopathogens into a country, e.g., as in the case of Moniliophthora roreri fungus that is present in all cocoa plantations of countries bordering Brazil, but that has not yet attacked Brazilian plantations. Graphical Abstract Secure identification of phytopathogens attacking cocoa plantations has been demonstrated via typical chemical profiles provided by mass spectrometric screening.