Comparative analyses of 18S rDNA regions for assessing the eukaryotic community in ancient lake sediment samples

Abstract The study of ancient sedimentary DNA (sedaDNA) aims to reconstruct the past environments and understand the historical biodiversity in or around aquatic ecosystems. The 18S ribosomal RNA gene (18S rDNA) is usually the target for discerning a broad spectrum of eukaryotic organisms, mainly microbial. Depending on the scientific context, metabarcoding of three hypervariable gene regions (V4, V7, and V9) are the most used alternatives for characterizing aquatic communities. Here, we evaluate the pros and cons of the three regions using sedaDNA samples up to about 3300 years old from a high mountain lake sediment. In samples with sufficient DNA content (ca. 1 µg DNA/g wet sediment) and preservation, V4, the longest region, provides community reconstructions richer in taxa. However, when DNA deterioration becomes more pronounced (i.e., > 1300-year-old samples in this study), the performance of the shortest V9 region is more satisfactory. The V7 region provides intermediate results. The three regions show biases concerning high taxonomic ranks, with V7 and V9 regions performing similarly. Microbial eukaryotes are better assessed than metazoans, and Kinetoplastea (Euglenozoa) are particularly persistent in ancient samples.

Authors

Institutions

Publication Details

Journal
Journal of Paleolimnology
Published
2025-06-27
DOI
https://doi.org/10.1007/s10933-025-00369-9
Primary Topic
Environmental DNA in Biodiversity Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative analyses of 18S rDNA regions for assessing the eukaryotic community in ancient lake sediment samples

Elena Fagín, Marisol Felip, Jordi Catalán
Journal of Paleolimnology
Environmental DNA in Biodiversity Studies
article

Comparative analyses of 18S rDNA regions for assessing the eukaryotic community in ancient lake sediment samples

Elena Fagín, Marisol Felip, Jordi Catalán
article en

Abstract

Abstract The study of ancient sedimentary DNA (sedaDNA) aims to reconstruct the past environments and understand the historical biodiversity in or around aquatic ecosystems. The 18S ribosomal RNA gene (18S rDNA) is usually the target for discerning a broad spectrum of eukaryotic organisms, mainly microbial. Depending on the scientific context, metabarcoding of three hypervariable gene regions (V4, V7, and V9) are the most used alternatives for characterizing aquatic communities. Here, we evaluate the pros and cons of the three regions using sedaDNA samples up to about 3300 years old from a high mountain lake sediment. In samples with sufficient DNA content (ca. 1 µg DNA/g wet sediment) and preservation, V4, the longest region, provides community reconstructions richer in taxa. However, when DNA deterioration becomes more pronounced (i.e., > 1300-year-old samples in this study), the performance of the shortest V9 region is more satisfactory. The V7 region provides intermediate results. The three regions show biases concerning high taxonomic ranks, with V7 and V9 regions performing similarly. Microbial eukaryotes are better assessed than metazoans, and Kinetoplastea (Euglenozoa) are particularly persistent in ancient samples.

Journal of PaleolimnologyVol. 73(5)
Centre for Research on Ecology and Forestry Applications (ES), Institut de Biologia Evolutiva (ES), Universitat de Barcelona (ES)
Universitat de Barcelona, Agencia Estatal de Investigación
Openalex Percentile: Top 85%
Environmental DNA in Biodiversity Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.