The (formerly ) species complex was previously composed of two morphological varieties: var. and var. . Prior attempts to resolve this morphology-based species complex using molecular techniques have been inconclusive or conflicting. The increased availability of sequenced genomes and isolates identified as var. and var. has allowed us to examine these relationships at a higher resolution and with a broader scope than previously possible. Using comparative genomics, we identified highly variable gene regions and designed primers for four new protein-coding genes for phylogenetics. These were then used alongside three known markers to generate a nuclear multigene genealogy of the species complex. From a collection of 163 isolates belonging to the target taxa, a subset of 29 was chosen to be included in this study (verified with nuclear rDNA internal transcribed spacer 1 [ITS1] and mitochondrial cytochrome oxidase subunit 1 [] sequences). Seventeen isolates of var. were selected to be representative of variations in genotype, morphology, and geographic collection location. The 12 isolates of var. included all available specimens identified either morphologically (in previous studies) or through sequence similarity with ITS1 and . Based on the fulfillment of reciprocal monophyly and observed genealogical concordance under the genealogical concordance phylogenetic species recognition, we determined that the species complex is composed of four genetically distinct species: , and .
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http://dx.doi.org/10.1080/00275514.2023.2241980 | DOI Listing |
J Econ Entomol
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School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China.
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Department of Chemistry, Faculty of Science, Cairo University, Giza, 12613, Egypt.
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Intercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, PA, USA.
Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products.
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