Literature DB >> 27611697

A quantitative protocol for DNA metabarcoding of springtails (Collembola).

Seikoh Saitoh1, Hiroaki Aoyama1, Saori Fujii2, Haruki Sunagawa3, Hideki Nagahama1, Masako Akutsu4, Naoya Shinzato1, Nobuhiro Kaneko2, Taizo Nakamori2.   

Abstract

We developed a novel protocol with superior quantitative analysis results for DNA metabarcoding of Collembola, a major soil microarthropod order. Degenerate PCR primers were designed for conserved regions in the mitochondrial cytochrome c oxidase subunit I (mtCOI) and 16S ribosomal RNA (mt16S) genes based on published collembolan mitogenomes. The best primer pair was selected based on its ability to amplify each gene, irrespective of the species. DNA was extracted from 10 natural communities sampled in a temperate forest (with typically 25-30 collembolan species per 10 soil samples) and 10 mock communities (with seven cultured collembolan species). The two gene regions were then amplified using the selected primers, ligated with adapters for 454 technology, and sequenced. Examination of the natural community samples showed that 32 and 36 operational taxonomic units (defined at a 90% sequence similarity threshold) were recovered from the mtCOI and mt16S data, respectively, which were comparable to the results of the microscopic identification of 25 morphospecies. Further, sequence abundances for each collembolan species from the mtCOI and mt16S data of the mock communities, after normalization by using a species as the internal control, showed good correlation with the number of individuals in the samples (R = 0.91-0.99), although relative species abundances within a mock community sample estimated from sequences were skewed from community composition in terms of the number of individuals or biomass of the species. Thus, this protocol enables the comparison of collembolan communities in a quantitative manner by metabarcoding.

Entities:  

Keywords:  16S; COX1; Collembola; collemboles; metabarcoding; métacodage à barres; quantification

Mesh:

Substances:

Year:  2016        PMID: 27611697     DOI: 10.1139/gen-2015-0228

Source DB:  PubMed          Journal:  Genome        ISSN: 0831-2796            Impact factor:   2.166


  10 in total

1.  Comparison of traditional and DNA metabarcoding samples for monitoring tropical soil arthropods (Formicidae, Collembola and Isoptera).

Authors:  Yves Basset; Mehrdad Hajibabaei; Michael T G Wright; Anakena M Castillo; David A Donoso; Simon T Segar; Daniel Souto-Vilarós; Dina Y Soliman; Tomas Roslin; M Alex Smith; Greg P A Lamarre; Luis F De León; Thibaud Decaëns; José G Palacios-Vargas; Gabriela Castaño-Meneses; Rudolf H Scheffrahn; Marleny Rivera; Filonila Perez; Ricardo Bobadilla; Yacksecari Lopez; José Alejandro Ramirez Silva; Maira Montejo Cruz; Angela Arango Galván; Héctor Barrios
Journal:  Sci Rep       Date:  2022-06-24       Impact factor: 4.996

2.  DNA in a bottle-Rapid metabarcoding survey for early alerts of invasive species in ports.

Authors:  Yaisel J Borrell; Laura Miralles; Hoang Do Huu; Khaled Mohammed-Geba; Eva Garcia-Vazquez
Journal:  PLoS One       Date:  2017-09-05       Impact factor: 3.240

3.  Metabarcoding analysis of the stomach contents of the Antarctic Toothfish (Dissostichus mawsoni) collected in the Antarctic Ocean.

Authors:  Tae-Ho Yoon; Hye-Eun Kang; Soo Rin Lee; Jae-Bong Lee; Gun Wook Baeck; Hyun Park; Hyun-Woo Kim
Journal:  PeerJ       Date:  2017-11-07       Impact factor: 2.984

4.  Evaluation of detection probabilities at the water-filtering and initial PCR steps in environmental DNA metabarcoding using a multispecies site occupancy model.

Authors:  Hideyuki Doi; Keiichi Fukaya; Shin-Ichiro Oka; Keiichi Sato; Michio Kondoh; Masaki Miya
Journal:  Sci Rep       Date:  2019-03-05       Impact factor: 4.379

Review 5.  Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance.

Authors:  Alexander M Piper; Jana Batovska; Noel O I Cogan; John Weiss; John Paul Cunningham; Brendan C Rodoni; Mark J Blacket
Journal:  Gigascience       Date:  2019-08-01       Impact factor: 6.524

6.  Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches.

Authors:  Jana Batovska; Alexander M Piper; Isabel Valenzuela; John Paul Cunningham; Mark J Blacket
Journal:  Sci Rep       Date:  2021-04-12       Impact factor: 4.996

7.  Unearthing soil arthropod diversity through DNA metabarcoding.

Authors:  Monica R Young; Paul D N Hebert
Journal:  PeerJ       Date:  2022-02-01       Impact factor: 2.984

8.  Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding.

Authors:  Henrik Krehenwinkel; Madeline Wolf; Jun Ying Lim; Andrew J Rominger; Warren B Simison; Rosemary G Gillespie
Journal:  Sci Rep       Date:  2017-12-15       Impact factor: 4.379

Review 9.  High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities.

Authors:  Susan R Kennedy; Stefan Prost; Isaac Overcast; Andrew J Rominger; Rosemary G Gillespie; Henrik Krehenwinkel
Journal:  Dev Genes Evol       Date:  2020-02-10       Impact factor: 0.900

10.  Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics.

Authors:  Katie M McGee; Teresita M Porter; Michael Wright; Mehrdad Hajibabaei
Journal:  Sci Rep       Date:  2020-10-28       Impact factor: 4.379

  10 in total

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