Natcha Chaisoung

Litsea species have been used for herbal medicine by many ethnic groups. However, defining the morphological characteristics of the species remains difficult, leading to confusion and misuse of Litsea names. We examined Litsea classification, focusing on folk taxonomy. A field survey revealed that Litsea cubeba, L. elliptica, L. mollis, L. glutinosa, and L. martabanica have the highest use values. Using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) analysis and multivariate statistical methods, we examined metabolites from these species to assess consistency across plant parts. Principal coordinate analysis (PCoA) and cluster analysis revealed distinct metabolite patterns, grouping species into four significant clusters: Group I (L. elliptica and L. martabanica), Group II (L. martabanica roots), Group III (L. cubeba and L. mollis bark and roots), and Group IV (L. glutinosa and L. cubeba and L. mollis leaves). Chemical compounds are clustered by species rather than by plant parts. Our study reveals a significant correlation (p < 0.05) between phylogenetic distances and chemical differences among Litsea species, elucidating the evolutionary links through metabolite variations. This predictive approach could help with more efficient selection for traditional medicine discovery and should be the first to be pharmacologically tested for drug development.

Chaisoung N, Panyadee P, Long C, Ngernsaengsaruay C, Chaowasku T, Inta A. 2023. Ethnobotanical study in the underexplored species of Genus Litsea (Lauraceae) in Northern Thailand. Biodiversitas 24: 3977-4000. Ethnobotanical studies offer valuable insights into medicinal plant use, but accuracy is limited by the difficulty in identifying similar species during field surveys. This study aims to investigate the Genus Litsea Lam. (Lauraceae) plants in Thailand, specifically focusing on underexplored species with ethnobotanical value for future utilization. The present research aims to comprehensively analyze the ethnobotanical data by comparing the results from previous ethnobotanical documents with those obtained from our field surveys. From a comprehensive review of ethnobotanical documents published from 1996-2022, the study discovered seven species of Litsea report to be used in Thailand. In addition, a subsequent field survey from November 2019 to December 2021 revealed that 13 species were used, with 11 of these used for medicinal purposes. The study identified seven additional species not previously documented for their medicinal uses. Among these species, Litsea mollis Hemsl., Litsea glutinosa (Lour.) C.B.Rob. and Litsea martabanica (Kurz) Hook.fil. have the highest use values and hold potential for future development, particularly L. mollis and L. martabanica. We also found that Litsea species were primarily used for treating digestive disorders. This study highlighted the importance of considering taxonomic complexities and conducting field surveys to assess plant uses accurately and can serve as a foundation for future research on these species, including their chemical analysis, biological activities, and pharmacological properties.

Synthetic biology is a principle that aims to create new biological systems with particular functions or to redesign the existing ones through bioengineering. Therefore, this principle is often utilized as a tool to put the knowledge learned to practical use in actual fields. However, there is still a great deal of information remaining to be found, and this limits the possible utilization of synthetic biology, particularly on the topic that is the focus of the present work—heavy metal bio-removal. In this work, we aim to construct a comprehensive library of putative proteins that might support heavy metal bio-removal. Hypothetical proteins were discovered from Chlorella and Scenedesmus genomes and extensively annotated. The protein structures of these putative proteins were also modeled through Alphafold2. Although a portion of this workflow has previously been demonstrated to annotate hypothetical proteins from whole genome sequences, the adaptation of such steps is yet to be done for library construction purposes. We also demonstrated further downstream steps that allow a more accurate function prediction of the hypothetical proteins by subjecting the models generated to structure-based annotation. In conclusion, a total of 72 newly discovered putative proteins were annotated with ready-to-use predicted structures available for further investigation.