In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2023-7-3), p. e0286069-
Kurzfassung:
Identifying plant, fungal, and animal ingredients in a specific mixture remains challenging during the limitation of PCR amplification and low specificity of traditional methods. Genomic DNA was extracted from mock and pharmaceutical samples. Four type of DNA barcodes were generated from shotgun sequencing dataset with the help of a local bioinformatic pipeline. Taxa of each barcode was assigned by blast to TCM-BOL, BOLD, and GenBank. Traditional methods including microscopy, thin layer chromatography (TLC), and high-performance liquid chromatography (HPLC) were carried out according to Chinese pharmacopoeia. On average, 6.8 Gb shotgun reads were sequenced from genomic DNA of each sample. Then, 97, 11, 10, 14, and one operational taxonomic unit (OTU) were generated for ITS2 , psbA-trnH , rbcL , matK , and COI , respectively. All the labeled ingredients including eight plant, one fungal, and one animal species were successfully detected in both the mock and pharmaceutical samples, in which Chebulae Fructus, Poria, and Fritilariae Thunbergia Bulbus were identified via mapping reads to organelle genomes. In addition, four unlabeled plant species were detected from pharmaceutical samples, while 30 genera of fungi, such as Schwanniomyces , Diaporthe , Fusarium were detected from mock and pharmaceutical samples. Furthermore, the microscopic, TLC, and HPLC analysis were all in accordance with the standards stipulated by Chinese Pharmacopoeia. This study indicated that shotgun metabarcoding could simultaneously identified plant, fungal, and animal ingredients in herbal products, which has the ability to serve as a valuable complement to traditional methods.
Materialart:
Online-Ressource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0286069
DOI:
10.1371/journal.pone.0286069.g001
DOI:
10.1371/journal.pone.0286069.g002
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10.1371/journal.pone.0286069.g003
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10.1371/journal.pone.0286069.g004
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10.1371/journal.pone.0286069.t001
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10.1371/journal.pone.0286069.t002
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10.1371/journal.pone.0286069.t003
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10.1371/journal.pone.0286069.s001
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10.1371/journal.pone.0286069.s002
DOI:
10.1371/journal.pone.0286069.s003
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10.1371/journal.pone.0286069.s004
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10.1371/journal.pone.0286069.s005
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10.1371/journal.pone.0286069.s006
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10.1371/journal.pone.0286069.s007
DOI:
10.1371/journal.pone.0286069.s008
DOI:
10.1371/journal.pone.0286069.s009
DOI:
10.1371/journal.pone.0286069.s010
DOI:
10.1371/journal.pone.0286069.s011
DOI:
10.1371/journal.pone.0286069.s012
DOI:
10.1371/journal.pone.0286069.s013
DOI:
10.1371/journal.pone.0286069.s014
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2023
ZDB Id:
2267670-3