DOI: 10.5061/dryad.rfj6q57n2
title: Miniaturised laboratorial equipment as a solution to implement conservation genetics tools and education in West African countries with limited infrastructures: an application to the study of illegal wildlife trade in Guinea-Bissau

description: Illegal wildlife trade (IWT) is considered one of the largest global
 illegal industries that negatively impacts biodiversity and sustainable
 development worldwide. DNA barcoding coupled with high-throughput
 sequencing has been shown to be useful in identifying taxa affected by IWT
 and has been routinely used during the last decades. However, for
 countries lacking laboratory infrastructures and sequencing units or
 trained staff, the application of DNA barcoding tools in conservation
 actions and policies is limited and dependent on slow sample export
 processes and molecular analyses carried out abroad. Guinea-Bissau (GB) is
 located on the West-African coast and has one of the lowest human
 development indices worldwide, while being a biodiversity hotspot facing
 many conservation challenges due to illegal commercial hunting and trade
 in bushmeat and live individuals. Here, we explore the potential of using
 inexpensive and portable miniaturised laboratory equipment (MLE) to i)
 identify species illegally traded in GB using DNA barcoding methods and
 ii) improve molecular biology and conservation genetic education and
 training in GB. Our overarching aim is to raise awareness of the current
 gap between the need to apply conservation genetic technologies in GB and
 the inability to do so due to a lack of laboratory infrastructures,
 sequencing units and opportunities for molecular biology training. We show
 that MLE can be a solution to accelerate the use of DNA barcoding methods
 to understand IWT and to train students, technicians and staff from
 governmental agencies dedicated to investigating environmental crimes,
 ultimately advancing the discipline of conservation genetics in the
 country.
description: This study used 33 tissue samples collected as part of other
 studies that investigated wild meat hunting, trade and consumption in
 several locations in Guinea-Bissau (Minhós et al. 2013; Ferreira da Silva
 et al. 2021). DNA was extracted using ThermoFisher’s
 MagMAX DNA Multi-Sample Ultra 2.0 Kit on the ThermoFisher Kingfisher Apex
 DNA extraction platform using default parameters. For the PCR
 amplification we used (1) primers designed by Gaubert et al. (2015) for
 the identification of bushmeat samples that amplify the first 402 bp of
 cytochrome b (<em>cyt b</em>) and a 390 bp of the 12S region,
 and (2) approximately 250 bp of the 16S rRNA gene using primers from
 Vences et al. (2016) that were designed to amplify vertebrate DNA from
 environmental DNA samples. Fragments were amplified by PCR in 30ul total
 volume reaction, using 15 ul of Qiagen Multiplex PCR Mix (Qiagen, GER),
 0.6 uM of forward and reverse primers and 1ul of DNA. Thermal cycling was
 performed in a T100 Thermal Cycler (BioRad). Cycling conditions consisted
 of 94°C for 3 min, followed by 35 cycles at 92 °C for 30’’, the primers
 specific annealing temperature for 30 s (CytB – 48°C, 12S – 56°C and 16S –
 59°C) and 72°C for 30 s, followed by a final extension of 72°C for 15 min.
 A template-free PCR was included in each amplification to control for
 potential contamination. Amplification success was checked on 2% agarose
 gels. The sequencing was carried out on the ONT’s
 MinION Mk1C platform using the native barcoding kit (ONT, SQK-NBD114.96)
 according to the manufacturer’s manual with minimal exceptions. In brief,
 the ends of the DNA amplicons were first prepared for index ligation
 using the NEBNext Ultra II End Repair / dA-tailing module (NEB, USA), here
 we extended the reaction time to 30 min at 20C and 30 min at 65C, then
 individual indices were aligned using the NEB Blunt/TA Ligase Master Mix
 (NEB, USA), cleaned with AMPure XP Beads (Beckman Coulter, USA) and then
 pooled in equal ratios. Sequencing adapters were ligated to the
 indices using the NEBNext Quick Ligation Module (NEB, USA) and the
 reaction cleaned using AMPureXP Beads (Beckman Coulter, USA) with ONT’s
 Short Fragment Buffer (SFB). The final library was quantified using the
 Qubit 4 Fluorometer and about 200ng loaded onto an ONT Flongle sequencing
 flowcell. We followed the bioinformatic pipeline
 described in Pomerantz et al. 2022. First the raw pod5 files were
 converted to the fastq format and demultiplexed using Dorado 0.5.0 (https://github.com/nanoporetech/dorado). Next, the number of reads and their quality were assessed using NanoPlot (https://github.com/wdecoster/NanoPlot) and we only retained reads with a Phred score of at least 17 with NanoFilt (https://github.com/wdecoster/nanofilt). Priming sites were removed from the reads using cutadapt (https://github.com/marcelm/cutadapt/; Martin 2011) and the consensus sequences reconstructed using NGSpeciesID (Sahlin et al. 2021). We carried out consensus polishing within NGSpeciesID using ONT’s Medaka software (https://github.com/nanoporetech/medaka). To identify the sample to the species level, we compared the sequences to available data on NCBI (http://www.ncbi.nlm.nih.gov/) using Nucleotide BLAST (Boratyn et al. 2013). References Minhós T, Nixon E, Sousa C, Vicente LM, da Silva MF, Sá R, Bruford MW (2013a) Genetic evidence for spatio-temporal changes in the dispersal patterns of two sympatric African colobine monkeys. <em>Am J Phys Anthropol</em>, 150, 464-474. Ferreira da Silva MJ, Camará M, Egeter B, Minhós T, Bruford MW, Godinho R (2021a) Using meta-barcoding tools to monitor primate meat consumption at dedicated establishments in Guinea-Bissau, West Africa. In: <em>Collection: Trends in Biodiversity and Evolution | TiBE2020 – Metabarcoding and Metagenomics</em> p. e65575. ARPHA Conference Abstracts, Vairão, Portugal. Gaubert P, Njiokou F, Olayemi A, Pagani P, Dufour S, Danquah E, Nutsuakor MEK, Ngua G, Missoup A-D, Tedesco PA, Dernat R, Antunes A (2015) Bushmeat genetics: setting up a reference framework for the DNA typing of African forest bushmeat. <em>Molecular Ecology Resources</em>, 15, 633-651. Vences M, Lyra ML, Perl RGB, Bletz MC, Stanković D, Lopes CM, Jarek M, Bhuju S, Geffers R, Haddad CFB, Steinfartz S (2016) Freshwater vertebrate metabarcoding on Illumina platforms using double-indexed primers of the mitochondrial 16S rRNA gene. <em>Conservation Genetics Resources</em>, 8, 323-327. Pomerantz A, Sahlin K, Vasiljevic N, Seah A, Lim M, Humble E, Kennedy S, Krehenwinkel H, Winter S, Ogden R, Prost S (2022) Rapid in situ identification of biological specimens via DNA amplicon sequencing using miniaturized laboratory equipment. <em>Nature Protocols</em>, 17, 1415-1443. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. <em>EMBnet journal</em>, 17, 10-12. Sahlin K, Lim MCW, Prost S (2021) NGSpeciesID: DNA barcode and amplicon consensus generation from long-read sequencing data. <em>Ecology and Evolution</em>, 11, 1392-1398. Boratyn GM, Camacho C, Cooper PS, Coulouris G, Fong A, Ma N, Madden TL, Matten WT, McGinnis SD, Merezhuk Y, Raytselis Y, Sayers EW, Tao T, Ye J, Zaretskaya I (2013) BLAST: a more efficient report with usability improvements. <em>Nucleic Acids Res</em>, 41, W29-33.
description: # Miniaturised laboratorial equipment as a solution to implement
 conservation genetics tools and education in West African countries with
 limited infrastructures: an application to the study of illegal wildlife
 trade in Guinea-Bissau Dataset DOI:
 [10.5061/dryad.rfj6q57n2](10.5061/dryad.rfj6q57n2) ## Description of the
 data and file structure Three independent sequence alignments of 33
 samples for fragments of mitochondrial DNA cyt b, 12S, and 16S genes.
 Samples codes and species identification in the work is described in the
 following table: | **Samples ID** | **Species identification in this
 work** | | | | | :------------- | :--------------------------------------
 | :------------------------ | ------------------------- | - | | | **cytb**
 | **12S** | **16S** | | | 224 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 225 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 276 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 278 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 277 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 280 | *C. petaurista* | *C. petaurista* | *C.
 petaurista* | | | 258 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 256 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 254 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | 255 | *C. petaurista* | *C. petaurista* |
 *C. petaurista* | | | B24 | *P. papio* | *P. papio* | *P. papio* | | |
 B115 | *P. papio* | *P. papio* | *P. papio* | | | B197 | *P. papio* | *P.
 papio* | *P. papio* | | | B155 | *P. papio* | *P. papio* | *P. papio* | |
 | B20 | *E. patas* | *E. patas* | *E. patas* | | | B126 | *P. papio* | *P.
 papio* | *P. papio* | | | B415 | *P. papio* | *P. papio* | *P. papio* | |
 | B127 | *P. papio* | *P. papio* | *P. papio* | | | B120 | *P. papio* |
 *P. papio* | *P. papio* | | | B119 | *P. papio* | *P. papio* | *P. papio*
 | | | B17 | *P. papio* | *P. papio* | *P. papio* | | | B117 | *P. papio* |
 *P. papio* | *P. papio* | | | 205 | *Piliocolobus badius* | *Piliocolobus
 badius* | *Piliocolobus badius* | | | 209 | *Erythrocebus patas* |
 *Erythrocebus patas* | *Erythrocebus patas* | | | 206 | *P. papio* | *P.
 papio* | *P. papio* | | | 208 | *Chlorocebus sabaeus* | *Chlorocebus
 sabaeus* | *Chlorocebus sabaeus* | | | 203 | *Cercopithecus campbelli* |
 *Cercopithecus campbelli* | *Cercopithecus campbelli* | | | 202 |
 *Chlorocebus sabaeus* | *Chlorocebus sabaeus* | *Chlorocebus sabaeus* | |
 | 20 | *P. papio* | *P. papio* | *P. papio* | | | 201 | *P. papio* | *P.
 papio* | *P. papio* | | | 204 | *Piliocolobus badius* | *Piliocolobus
 badius* | *Piliocolobus badius* | | | 21 | *Cercopithecus campbelli* |
 *Cercopithecus mona* | *Cercopithecus campbelli* | | | 1 | *Chlorocebus
 sabaeus* | *Chlorocebus sabaeus* | *Chlorocebus sabaeus* | | ### Files and
 variables #### File: monkey_16S_final.aln **Description:** 33 samples of
 240 base pairs for fragment 16S #### File: monkey_cytB_final.aln
 **Description:** 33 samples of 420 base pairs for fragment cytb #### File:
 monkey_12S_final.aln **Description:** 33 samples of 390 base pairs for
 fragment 12S