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Gewählte Publikation:

Publikationstyp: Zeitschriftenaufsatz
Dokumentart: Originalarbeit

Publikationsjahr: 2018

AutorInnen: Schmiderer, C; Lukas, B; Ruzicka, J; Novak, J

Titel: What Else Is in Salviae officinalis folium? Comprehensive Species Identification of Plant Raw Material by DNA Metabarcoding.

Quelle: Planta Med. 2018; 84(6-07):428-433



Autor/innen der Vetmeduni Vienna:

Lukas Brigitte
Novak Johannes
Ruzicka Joana
Schmiderer Corinna

Beteiligte Vetmed-Organisationseinheiten
Institut für Tierernährung und funktionelle Pflanzenstoffe


Abstract:
Quality control of drugs consists of identifying the raw material to avoid unwanted admixtures or exchange of material as well as looking for abiotic and biotic contaminations. So far, identity and microbial contamination are analyzed by separate processes and separate methods. Species identification by their DNA ("DNA barcoding") has the potential to supplement existing methods of identification. The introduction of next-generation sequencing methods offers completely new approaches like the identification of whole communities in one analysis, termed "DNA metabarcoding". Here we present a next-generation sequencing assessment to identify plants and fungi of two commercial sage samples ( Salvia officinalis ) using the standard DNA barcoding region "internal transcribed spacer" consisting of internal transcribed spacer 1 and internal transcribed spacer 2, respectively. The main species in both samples was identified as S. officinalis . The spectrum of accompanying plant and fungal species, however, was completely different between the samples. Additionally, the composition between internal transcribed spacer 1 and internal transcribed spacer 2 within the samples was different and demonstrated the influence of primer selection and therefore the need for harmonization. This next-generation sequencing approach does not result in quantitative species composition but gives deeper insight into the composition of additional species. Therefore, it would allow for a better knowledge-based risk assessment than any other method available. However, the method is only economically feasible in routine analysis if a high sample throughput can be guaranteed.


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