DNA barcoding and LC-MS metabolite profiling of the lichen-forming genus Melanelia: Specimen identification and discrimination focusing on Icelandic taxa

• Published in: PLOS ONE
• Main Authors: Xu, Maonian, Heidmarsson, Starri, Thorsteinsdottir, Margret, Eiriksson, Finnur F., Omarsdottir, Sesselja, Olafsdottir, Elin S.
• Format: Text
• Language: English
• Published: Public Library of Science 2017
• Subjects: Research Article; Iceland
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5443556/; http://www.ncbi.nlm.nih.gov/pubmed/28542495; https://doi.org/10.1371/journal.pone.0178012

Taxa in the genus Melanelia (Parmeliaceae, Ascomycota) belong to a group of saxicolous lichens with brown to black foliose thalli, which have recently undergone extensive changes in circumscription. Taxa belonging to Parmeliaceae are prolific producers of bioactive compounds, which have also been traditionally used for chemotaxonomic purposes. However, the chemical diversity of the genus Melanelia and the use of chemical data for species discrimination in this genus are largely unexplored. In addition, identification based on morphological characters is challenging due to few taxonomically informative characters. Molecular identification methods, such as DNA barcoding, have rarely been applied to this genus. This study aimed to identify the Melanelia species from Iceland using DNA barcoding approach, and to explore their chemical diversity using chemical profiling. Chemometric tools were used to see if lichen metabolite profiles determined by LC-MS could be used for the identification of Icelandic Melanelia species. Barcoding using the fungal nuclear ribosomal internal transcribed spacer region (nrITS) successfully identified three Melalenlia species occurring in Iceland, together with Montanelia disjuncta (Basionym: Melanelia disjuncta). All species formed monophyletic clades in the neighbor-joining nrITS gene tree. However, high intraspecific genetic distance of M. stygia suggests the potential of unrecognized species lineages. Principal component analysis (PCA) of metabolite data gave a holistic overview showing that M. hepatizon and M. disjuncta were distinct from the rest, without the power to separate M. agnata and M. stygia due to their chemical similarity. Orthogonal partial least–squares to latent structures–discriminate analysis (OPLS-DA), however, successfully distinguished M. agnata and M. stygia by identifying statistically significant metabolites, which lead to class differentiation. This work has demonstrated the potential of DNA barcoding, chemical profiling and chemometrics in identification of ...