Periodic Reporting for period 2 - Plant.ID (Molecular Identification of Plants)
Okres sprawozdawczy: 2020-01-01 do 2021-12-31
DNA metabarcoding is a research method developed for biodiversity assessment from DNA containing substrates. For plant identification it holds great promise, but more markers need to be developed for accurate species identification. ESRs in WP2 have worked on developing and testing new approaches in molecular plant diversity assessment that move beyond amplicon-based sequencing and instead use shotgun sequencing, metagenomics, artificial intelligence and machine-learning. Such methods enable accurate identification as well as relative quantification of plant species in substrates, and enhance the ability to screen, authenticate and monitor such substrates, including herbal supplements, food stuffs, pollen traps, soil sediments, water and wood samples.
Genome sequencing data in metabarcoding as this avoids amplification biases and enables constituent quantification. WP3 therefore aimed to develop novel genetic and molecular approaches for plant identification. Each project has continued to follow on the work that was completed during the first reporting period, and have followed through on these envisioned objectives in the second reporting period. To this end, chemical and molecular methods have been developed for the historically important medicinal plant, the Cinchona tree. Customised target capture bait panels were developed for the commercially valuable Aloe, which lead to the largest-to-date produced dataset on carbon isotope values of aloes. Target capture and metagenomic sequencing of commercial samples of salep powder from online vendors and markets have been developed to test their composition and provenance. A range of bioinformatic tools have been tested against a Begonia population with a known genetic makeup in order to establish workflows for testing Begonia from different species radiations.
An increasing number of organisations routinely apply DNA-based techniques for monitoring purposes and quality assessments. For plant identification, tailor-made applications for societal end-users is still in its infancy. The ESR projects in WP4 thus all aimed to develop solutions to present research results that are easy for end users to interpret. Thus, African hardwood samples important in trade were analyzed by ESR12, resulting in new markers to discriminate between those that can be legally traded from those that are protected. ESR13 developed new bioinformatic pipelines to analyze the level of adulteration in herbal medicines and foods. ESR14 developed methods to understand the historical geography and lineage of medicinally important plants in Africa. ESR15 developed AI methods to analyze traded ebony and identify species that are protected from those that are protected.
This work has been disseminated through 42 oral and poster presentations, 11 peer-reviewed journal articles, with 6800 members of the scientific community reached. Outreach efforts using various forms of dissemination have resulted in 470000 members of the general public also being reached.