SedDNA results along the sediment core of the Dzoumogné reservoir, Mayotte Island, Comoros Archipelago, France

This repository is made up of two main folders containing information about the metadata and the database variables:

• Metadata_SedDNA_dzoumogne_reservoir_afoucher.xlsx (description of the metadata associated with the sediment samples)
• Variable_description_dzoumogne_reservoir_afoucher.xlsx (description of the database variables)

as well as 6 folders containing the data sets for each genetic marker:

• Balitzki_12S-afoucher.xlsx
• Hardy_18S-afoucher.xlsx
• Kelly_16S-afoucher.xlsx
• Pornon_ITS-afoucher.xlsx
• Prosser_rtbl-afoucher.xlsx
• White_ITS-afoucher.xlsx

Related publication: Foucher et al. submitted

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Context: Sediment DNA was extracted from 12 layers along a sediment core (IGSN number: 10.58052/IEFOU0003) collected in 2021 in the Dzoumogné reservoir (Mayotte Island, Comoros Archipelago, France), covering the 2012-2021 period. The aim of these analyses was to evaluate the consequences of landscape defragmentation, induced by deforestation and intensification of agricultural practices, on changes in biological communities.

Sample collection, storage and subsampling: Sediment core was collected using an Uwitec gravity corer equipped with a 63mm PVC liner. The core was stored at 4°C in a cold and dark room for 3 weeks prior to subsampling. The sediment core was opened and subsampled in a previously sterilized hoot (DNA cleaner and UV) in a dedicated biological room at the University of Paris-Saclay. Each sample was extracted from the sediment core using sterilized metal plates inserted on either side of the layer to be sampled (the central part of this sample was then sub-sampled to remove the part of the sediment in contact with the PVC tube of the core and the cutting tools). During this process the operators were equipped with gloves, masks and headgear to avoid any risk of contamination. A blank was placed in the fume cupboard to control contamination during core opening and sampling.

Extraction and assignation: SedDNA was retrieved using a standardized protocol that combines the use of commercial kits (Nucleospin soil – Macherey-Nagel) and the extracellular DNA pre-treatment described in Taberlet et al. (1). In order to limit amplification biases and to improve the metabarcoding sensitivity, a combination of 7 genetic markers was used to PCR-amplify each targeted taxonomic group (plants - 50-70 bp – trnL (2),  plants - 150 bp – rbcL (3), plants/fungi – 320 bp –ITS (4), plants – 380 bp – ITS , metazoa – 200 bp – 16S (5), Eukaryotes – 200 bp – 18S (6), vertebrates – 200 bp – 12S (7)). PCRs were realized in octuplicates with positive and negative controls. Extraction controls were also amplified with the dedicated primers. PCR products were cleaned using the MinElute purification kit (QIAGEN) and all purified amplicons were pooled in equimolar concentrations at the exception of the extraction and the PCR negative controls as they were not concentrated enough. Then, each sample pool was run on an Agilent Bioanalyzer using the DNA High Sensitivity LabChip kit (Agilent Technologies, Santa Clara, CA, USA) to verify amplicon length and each sample was finally quantified with a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). 
Libraries were generated with a PCR-free protocol in order to tremendously reduce the number of chimeras induced by the metabarcoding approach. One microgram of each purified PCR products pool was submitted to molecular end-repair and then ligated to Illumina adapters prior to sequencing on Illumina platforms (Next-Generation Sequencing; Fasteris, Switzerland) using a NovaSeq instrument (Illumina, San Diego, CA, USA) with a paired-end read mode covering the amplicon lengths. The objective was to obtain approximately 1 million reads per marker and per sample. 
Bioinformatics analyses were performed using the OBITools (8) and QIIME2 (9) pipelines. Reads were first demultiplexed using OBITOOLS. Then erroneous sequences were removed, and singletons were filtered out. Sequences were then clustered at 97% identity using VSEARCH (10). Sequences that were found in the extraction blanks were removed from the downstream analysis. Taxonomic assignment was realized in two steps. First, sequences were assigned by blasting them against a custom database generated from the GenBank latest release curated and restricted to all the documented taxa. Sequences with over 95% identity match were kept and their last common ancestor was computed using MEGAN (11). A second assignment pass was made using a Naïve Bayesian Classifier using QIIME’s rescript plugin (12).

Data validation: Discuss in the related paper.

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References:

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