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Cited by 244Environmental DNA in Biodiversity StudiesInvertebrate Taxonomy and EcologyFreshwater macroinvertebrate diversity and ecology

The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.

Environmental monitoring using next generation sequencing: rapid identification of macroinvertebrate bioindicator species

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Carew, Melissa E, Pettigrove, Vincent J, Metzeling, Leon, Hoffmann, Ary A·2013·Frontiers in Zoology
DOI 10.1186/1742-9994-10-45PMC PMC3750358
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Abstract

INTRODUCTION: Invertebrate communities are central to many environmental monitoring programs. In freshwater ecosystems, aquatic macroinvertebrates are collected, identified and then used to infer ecosystem condition. Yet the key step of species identification is often not taken, as it requires a high level of taxonomic expertise, which is lacking in most organizations, or species cannot be identified as they are morphologically cryptic or represent little known groups. Identifying species using DNA sequences can overcome many of these issues; with the power of next generation sequencing (NGS), using DNA sequences for routine monitoring becomes feasible. RESULTS: In this study, we test if NGS can be used to identify species from field-collected samples in an important bioindicator group, the Chironomidae. We show that Cytochrome oxidase I (COI) and Cytochrome B (CytB) sequences provide accurate DNA barcodes for chironomid species. We then develop a NGS analysis pipeline to identifying species using megablast searches of high quality sequences generated using 454 pyrosequencing against comprehensive reference libraries of Sanger-sequenced voucher specimens. We find that 454 generated COI sequences successfully identified up to 96% of species in samples, but this increased up to 99% when combined with CytB sequences. Accurate identification depends on having at least five sequences for a species; below this level species not expected in samples were detected. Incorrect incorporation of some multiplex identifiers (MID's) used to tag samples was a likely cause, and most errors could be detected when using MID tags on forward and reverse primers. We also found a strong quantitative relationship between the number of 454 sequences and individuals showing that it may be possible to estimate the abundance of species from 454 pyrosequencing data. CONCLUSIONS: Next generation sequencing using two genes was successful for identifying chironomid species. However, when detecting species from 454 pyrosequencing data sets it was critical to include known individuals for quality control and to establish thresholds for detecting species. The NGS approach developed here can lead to routine species-level diagnostic monitoring of aquatic ecosystems.

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Figure 1 Bootstrapped Kirma-2-parameter trees examining the genetic distance between the species found in this study. Neighbour joining trees are based on the 46 chironomid species that occurred at the ten field sites for two gene regions a) COI b) CytB used in this study. Both trees are construct using the same regions used to identify species in the 454 pyrosequencing experiments (395 bps for COI and 343 bps for CytB) and show the level of intraspecific variation (represented by black triangles) based on sequences from up to ten individuals pre species (the number of individuals is given in parentheses) from our DNA reference libraries.
Figure 1 Bootstrapped Kirma-2-parameter trees examining the genetic distance between the species found in this study. Neighbour joining trees are based on the 46 chironomid species that occurred at the ten field sites for two gene regions a) COI b) CytB used in this study. Both trees are construct using the same regions used to identify species in the 454 pyrosequencing experiments (395 bps for COI and 343 bps for CytB) and show the level of intraspecific variation (represented by black triangles) based on sequences from up to ten individuals pre species (the number of individuals is given in parentheses) from our DNA reference libraries.CC BY · PubMed Central
Figure 2 Relationship between the number of individuals of a species at a site and the average number of 454 sequence reads. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.
Figure 2 Relationship between the number of individuals of a species at a site and the average number of 454 sequence reads. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.CC BY · PubMed Central
Figure 3 The relationship between the proportion of individuals per species and the proportion of 454 reads per species per site. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.
Figure 3 The relationship between the proportion of individuals per species and the proportion of 454 reads per species per site. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.CC BY · PubMed Central
Figure 4 Relationship between the number of individuals at a site and the average number of 454 sequence reads at a site for four common species a) Chironomus australis, b) Dicrotendipes pseudoconjunctus, c) Chironomus oppositus, and d) Kiefferulus intertinctus. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.
Figure 4 Relationship between the number of individuals at a site and the average number of 454 sequence reads at a site for four common species a) Chironomus australis, b) Dicrotendipes pseudoconjunctus, c) Chironomus oppositus, and d) Kiefferulus intertinctus. The R2 values are shown for ‘all reads’ that combines the data for COI and CytB, and for each gene individually.CC BY · PubMed Central
Figure 5 Experimental design and data analysis pipeline. The first half of the pipeline (in blue) shows the experimental set up, where species in samples were amplified individually (using morphology, PCR-RFLP and Sanger sequencing) and in bulk using 454 pyrosequencing. The second half of the pipeline (in orange) deals with the analysis of the sequences generated with 454 pyrosequencing.
Figure 5 Experimental design and data analysis pipeline. The first half of the pipeline (in blue) shows the experimental set up, where species in samples were amplified individually (using morphology, PCR-RFLP and Sanger sequencing) and in bulk using 454 pyrosequencing. The second half of the pipeline (in orange) deals with the analysis of the sequences generated with 454 pyrosequencing.CC BY · PubMed Central

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Metadata

Authors
Carew, M. E., Pettigrove, V. J., Metzeling, L., & Hoffmann, A. A.
Affiliations
  • The University of Melbourne (AU)
  • Environment Protection Authority Victoria (AU)
Corresponding author
Carew, Melissa E
ORCID
Venue
Frontiers in Zoology
Publisher
Springer Science and Business Media LLC
Published
2013
Volume
10
Issue
1
Pages
45
ISSN
1742-9994 (ISSN-L)
DOI
10.1186/1742-9994-10-45
PMC
PMC3750358
OpenAlex
W2159396143
Cited by
243
References
76
Citations by year
2026: 5 · 2025: 4 · 2024: 5 · 2023: 12 · 2022: 8 · 2021: 16 · 2020: 27 · 2019: 23 · 2018: 50 · 2017: 33 · 2016: 24 · 2015: 24 · 2014: 11
FWCI
17.38
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Zoology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • Melbourne Water
  • State Government of Victoria
  • Environment Protection Authority Victoria
  • Australian Research Council
Topics
  • Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Invertebrate Taxonomy and Ecology
  • Physical Sciences › Environmental Science › Ecology › Freshwater macroinvertebrate diversity and ecology
Keywords
DNA barcoding, Chironomidae, aquatic macroinvertebrates, bioindicator species, species identification, COI, cytochrome b, 454 pyrosequencing, environmental monitoring, freshwater ecosystems, species abundance estimation, reference library

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