DOI: 10.5061/dryad.ht76hdrqg
title: Data from: Multiplex vs. singleplex assay for the simultaneous identification of the three components of avian malaria vector-borne disease by DNA metabarcoding

description: Accurate detection and identification of vector-host-parasite systems are
 key to understanding their evolutionary dynamics and to design effective
 disease prevention strategies. Traditionally, microscopical and
 serological techniques were employed to analyse arthropod blood meals for
 host/parasite detection, but these were limited in taxonomic resolution
 and only to pre-selected taxa. In recent years, molecular techniques have
 emerged as a promising alternative, offering enhanced resolution and
 taxonomic range. While singleplex PCR assays were used at first to
 identify host, vector and parasite components in separate reactions, today
 multiple primer pairs can be combined in a single reaction, i.e.
 multiplex, offering substantial time and cost savings. Nonetheless,
 despite the potential benefits of multiplex PCR, studies quantifying its
 efficacy compared to singleplex reactions are scarce. In this study, we
 used partially digested mosquito blood meals within an avian malaria
 framework to jointly identify the host, vector and parasite using
 multiplex DNA metabarcoding, and to compare it with separate singleplex
 PCRs. We aimed to compare the detection probabilities and taxonomic
 assignments between both approaches. We found both to have similar
 performances in terms of detection for the host and the vector, but
 singleplex performed better than multiplex for the parasite component. We
 suggest adjusting the relative concentrations of the PCR primers used in
 the multiplex assay can increase the efficiency in detecting all the
 components of the studied multi-species system. Overall, the results show
 that DNA metabarcoding is an effective approach that could be applied to
 any vector-borne interaction involving blood-feeding arthropods. Our
 insights will not only refine laboratory procedures, but also enhance
 research efforts and medical diagnosis of vector-borne diseases.
description: <strong>Sample collection</strong>
 36 wild gravid female mosquitoes were captured between June and
 September 2019 in two distinct locations in Switzerland (Dorigny (46°31’N,
 6°34’E) and Monods (46°34’N, 6°24’E) forests, Canton Vaud). In addition,
 15 <em>Culex pipiens</em> mosquitoes reared in the laboratory
 and fed on experimentally <em>Plasmodium</em>-infected captive
 birds (<em>Serinus canaria, </em>Fringillidae, and
 <em>Passer domesticus</em>, Passeridae) were included as
 positive controls. The abdomens of all 51 mosquitoes were dissected under
 a binocular microscope using sterilised Vannas-Tübingen Spring Scissor
 (n°15003-08, Fine Science Tools) and stored at -80°C until DNA
 extraction. <strong>DNA
 extraction</strong> Prior to DNA extraction,
 mosquito abdomens were grinded with homogenisation beads on a tissue
 homogeniser (Bertin Technologies, Montigny-le-Bretonneux, France). DNA
 extraction was done using a DNeasy Blood & Tissue Kit (Qiagen,
 Hilden, Germany) following the manufacturers' protocol but with the
 elution step modified as follows: (i) 75 µL of elution buffer and 15
 minutes of incubation at room temperature, followed by centrifugation, and
 (ii) 100 µL of elution buffer and one minute of incubation followed by
 centrifugation. <strong>Primer description and
 preliminary tests</strong> Three primer pairs
 targeting different mitochondrial DNA (mtDNA) genes were used to identify
 the blood meal origin (i.e. host), the mosquito species (i.e. vector) and
 the parasite based on the DNA extracted from the 51 mosquito
 abdomens. First, a metabarcode was amplified to
 identify the blood source using <em>Aves02 </em>(Taberlet
 et al., 2018), targeting the 12S rDNA gene in birds. As
 <em>Aves02</em> primers were only tested using <em>in
 silico</em> PCR so far, preliminary PCRs with these primers were
 done on tissue DNA extracts (liver, muscle, blood) of 21 avian species
 from a wide range of orders (Table S1). The presence of DNA bands at the
 expected molecular size was verified on an 2% agarose gel. The results of
 this test showed that the targeted <em>Aves02</em> metabarcode
 was not amplified for the Eurasian blue tit (<em>Cyanistes
 caeruleus</em>), which is a common species in the study sites.
 Therefore, based on Passeriformes sequences complementary to
 <em>Aves02</em> primers found in GenBank, two new versions of
 the forward <em>Aves02</em> primer targeting the blue tit
 <em>Cyanistes caeruleus</em>
 (<em>Aves02_Cyan_F</em>) and the great tit <em>Parus
 major</em> (<em>Aves02_Parus_F</em>; also common in the
 study sites) were designed (Table S2) to better cover the avian taxonomic
 panel. These two modified versions were mixed, in a proportion of 25%
 each, with the standard forward <em>Aves02</em> primer for the
 final PCRs (the mixture of these three forward and the original reverse
 PCR primers is thereafter noted as <em>Aves02_mix</em>). In
 case of failed amplifications with <em>Aves02_mix</em>
 primers, samples were tested using a more generalist vertebrate primer
 (<em>Vert01</em>,(Riaz et
 al., 2011; Taberlet et al., 2018)) to check if the host was a
 non-avian vertebrate or to confirm the negative detection of the host in
 the blood. Second, to identify the mosquito vector, we used a primer pair
 targeting the 16S rDNA gene in Culicidae
 (<em>Culi01</em>;(Schneider
 et al., 2016)). Third, primers targeting the genus
 <em>Plasmodium</em> and amplifying a 37-44bp portion of the
 cytochrome <em>b </em>gene<em> </em>were newly
 designed for this study (<em>Plas01</em>) using
 the<em>OBITools</em> package (Boyer et
 al., 2016). In brief: we used <em>ecoPrimers</em>
 software(Ficetola
 et al., 2010; Riaz et al., 2011) to find the best suitable
 primer for this purpose based on all complete mtDNA genomes available in
 GenBank. It was validated with a <em>in silico</em> PCR using
 <em>ecoPCR</em> software(Ficetola
 et al., 2010) for the targeted taxonomic group, using the EMBL
 database release 142 (January 2020). As <em>Plas01</em>
 primers were also only tested using <em>in silico</em> PCR so
 far, preliminary PCRs were performed on DNA extracts of 18
 <em>Plasmodium</em> mtDNA lineages (Table S1). The presence of
 DNA bands at the expected molecular size was verified on an agarose
 gel. All blood meal samples were amplified in
 triplicate with the three primer pairs in both singleplex and multiplex
 assays. <strong>Singleplex DNA
 metabarcoding</strong> To test for the presence
 of PCR inhibitors, preliminary PCRs were performed with each primer pair
 on 8 randomly selected samples with different dilutions, and the amplicons
 band intensities checked on agarose gels. Based on these results, DNA
 extracts from wild blood-fed mosquitoes were diluted 2-fold and those from
 laboratory blood-fed mosquitoes 2000-fold before being amplified with
 <em>Aves02_mix</em>. DNA extracts were diluted 10-fold before
 being amplified with <em>Culi01</em>, and no dilution was
 applied for the amplification with <em>Plas01</em>.
 The PCR reagents and their final concentrations were the
 following: AmpliTaq Gold 360 Master Mix 1x, tagged forward and reverse
 primers at 0.5 µM, BSA at 0.16 mg/mL. The final volume per well was 20 µl
 including 2 µL of template DNA. The PCR thermal profile started with
 denaturation at 95°C for 10 minutes, followed by 40 cycles with
 <em>Aves02_mix</em>, 45 cycles with
 <em>Plas01</em> and 50 cycles with
 <em>Culi01</em>. Each cycle was composed of 30 seconds at
 95°C, 30 seconds at 56°C, 55°C and 60°C for
 <em>Aves02_mix</em>, for <em>Plas01</em> and for
 <em>Culi01</em> primers, respectively, and one minute at 72°C,
 before a final elongation at 72°C for seven minutes.
 <strong>Multiplex DNA
 metabarcoding</strong> The three primer pairs
 used for singleplex were mixed to perform a single PCR reaction using the
 following final concentrations: <em>Aves02_mix</em> 0.4 µM,
 <em>Plas01</em> 0.4 µM, <em>Culi01</em> 0.15 µM.
 Final concentrations of the other PCR reagents were the same as in the
 singleplex assay, as well as final and template DNA volumes. We reduced
 the concentration of <em>Culi01</em> because the preliminary
 tests revealed an overamplification of mosquito sequences with equal
 primer concentration (data not shown). For the multiplex PCR reactions,
 all DNA extracts were diluted 10-fold. The PCR conditions were as follows:
 initial denaturation at 95°C for 10 minutes, followed by 45 cycles with 30
 seconds at 95°C, 30 seconds at 55°C and 1 min at 72°C; final elongation at
 72°C for seven minutes. <strong>Purification,
 library preparation and sequencing</strong> For
 each assay, extraction negative, PCR negative and positive controls (Table
 S3) as well as blanks were included in each PCR plate (for details on
 plate layout, see Taberlet et al., 2018). Before pooling amplicons per
 plate, amplification success was verified on an agarose gel for a subset
 of samples. Amplicon pools were purified using the
 MinElute PCR Purification Kit (Qiagen, Hilden, Germany) and quantified
 using a Qubit 2.0 Fluorometer (Life Technology Corporation, USA). Library
 preparation was performed using the TruSeq DNA PCR-Free Library Prep Kit
 (Illumina, San Diego, CA, USA) starting at the repair ends and library
 size selection step, with an adjusted beads ratio of 1.8 to remove small
 fragments. After adapter ligation, libraries were validated on a fragment
 analyser (Advanced Analytical Technologies, USA). According to the
 results, one or two, depending on the libraries, SPRIselect bead
 purifications were performed again to better select the fragments of
 interest. Final libraries were quantified by qPCR, normalised and pooled
 before 150 paired-end sequencing on the Illumina Miniseq Sequencing System
 with a Mid-Output Kit (Illumina, San Diego, CA, USA).
 <strong>Bioinformatics</strong>
 Processing of raw sequences was conducted separately for each
 library using the <em>OBITools</em> package(Boyer et
 al., 2016). Initially, reads were assembled with a minimum
 quality score of 40. Sequences were then assigned to samples based on
 unique tags combinations. OTUs with less than 100 reads per library were
 discarded as well as those not fitting the range of metabarcode lengths.
 Afterwards, pairwise dissimilarities between OTUs were computed and lesser
 abundant ones with single nucleotide dissimilarity were clustered into the
 most abundant ones. We used the <em>sumaclust</em>
 algorithm(Mercier C,
 2013) to further refine the resulting clusters based on a
 sequence similarity of 97 %. It uses the same clustering algorithm as
 UCLUST(Prasad,
 D.V., 2015) and identifies erroneous sequences produced during
 amplification and sequencing. Remaining sequences were assigned to taxa
 using a reference database. We built a database using the
 <em>ecoPCR</em> for <em>Aves02_mix</em>,
 <em>Culi01</em> and <em>Plas01</em> by running an
 <em>in silico</em> PCR based on all the potentially
 amplifiable sequences available in the EMBL database (European Molecular
 Biology Laboratory). Since not all of these bird species were present in
 our database, missing sequences for frequent species occurring in the
 study area were generated by DNA Sanger-sequencing and added manually to
 the database (Aves02_mix Database, Supplementary data). Using these
 databases, we retained OTUs with a similarity match above 90% for
 Aves02_mix, 95% for Culi01 and 90% for Plas01. After taxonomic assignment
 using the abovementioned databases, all remaining OTUs were then
 double-checked using BLASTn on the NCBI database.
 Further data cleaning and statistical analyses were performed in
 R 4.02. First, we used the <em>metabaR</em> package(Zinger et
 al., 2021) to assess the coverage, tag-jump rate and
 contamination of remaining PCR replicates. PCR replicates with too small
 reads count were also discarded. Removal of tag-leaked sequences was done
 independently for each library. This approach allowed us to discard single
 OTUs instead of whole PCR replicates. Remaining PCR replicates were
 grouped by sample and primer pair, and the mean number of sequences and
 mean relative read abundance (RRA) was calculated for each. We also
 re-calculated RRA to obtain the amount of reads per sample and primer pair
 relative to the total amount of sequences retrieved combining the three
 primers. We then visualised the differences between singleplex and
 multiplex in terms of primer pair, sample and taxonomy.
description: # Multiplex vs. singleplex assay for the simultaneous identification of
 the three components of avian malaria vector-borne disease by DNA
 metabarcoding This Multiplex_README.txt file was generated on 2024-07-24
 by Eduard Mas-Carrió **GENERAL INFORMATION**  1. Title of Dataset: Data
 from: Multiplex vs. singleplex assay for the simultaneous identification
 of the three components of avian malaria vector-borne disease by DNA
 metabarcoding  2. Author Information  E. Mas-Carrió(1), J. Schneider(1),
 V. Othenin-Girard(1), R. Pigeault(2,3), P. Taberlet(4,5), P. Christe(2),
 O. Glaizot(2,6, §) and L. Fumagalli(1,7 § )  (1)Laboratory for
 Conservation Biology, Department of Ecology and Evolution, Biophore,
 University of Lausanne, 1015 Lausanne, Switzerland. (2)Department of
 Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne,
 Switzerland. (3)Laboratoire EBI, Equipe EES, UMR CNRS 7267, 86000
 Poitiers, France (4)Université Grenoble Alpes, CNRS, Laboratoire
 d’Ecologie Alpine, 38000 Grenoble, France. (5)UiT – The Arctic University
 of Norway, Tromsø Museum, Tromsø, Norway. (6)State Museum of Natural
 History, Department of Zoology, Place de la Riponne 6, 1014 Lausanne,
 Switzerland. (7)Swiss Human Institute of Forensic Taphonomy, University
 Centre of Legal Medicine Lausanne-Geneva, Lausanne University Hospital and
 University of Lausanne, Ch. de la Vulliette 4, 1000 Lausanne 25,
 Switzerland.  (§)joint senior authors  3. Date of data collection: June
 2019 – Sept 2019  4. Geographic location of data collection: two distinct
 locations in Switzerland (Dorigny (46°31’N, 6°34’E) and Monods (46°34’N,
 6°24’E) forests, Canton Vaud)  5. Funding sources that supported the
 collection of the data: FBM funding Vaud, Switzerland and SNF funding,
 Switzerland.  6. Recommended citation for this dataset: Mas-Carrio et al.
 (2024), Multiplex vs. singleplex assay for the simultaneous identification
 of the three components of avian malaria vector-borne disease by DNA
 metabarcoding, Dryad, Dataset ####  **DATA & FILE OVERVIEW**  **1.
 Description of dataset**  We used short DNA metabarcodes for the
 amplification and high-throughput sequencing of partially digested
 mosquito blood meals in an avian malaria framework as a case study system.
 This vector-borne disease is caused by apicomplexan parasites belonging to
 the Plasmodium genus, which are transmitted in birds by mosquitoes. For
 the first time, mosquito blood meals were used here to simultaneously
 identify, using multiplex DNA metabarcoding, the host-vector-parasite
 triad in a tripartite vector-borne system.  In order to improve the
 identification methodology, we compared the detection probabilities by
 singleplex and multiplex DNA metabarcoding of the three components of this
 system and the differences in their taxonomic assignments.  Sample
 collection  36 wild gravid female mosquitoes were captured between June
 and September 2019 in two distinct locations in Switzerland (Dorigny
 (46°31’N, 6°34’E) and Monods (46°34’N, 6°24’E) forests, Canton Vaud). In
 addition, 15 *Culex pipiens* mosquitoes reared in the laboratory and fed
 on experimentally *Plasmodium*-infected captive birds (*Serinus canaria,
 *Fringillidae, and *Passer domesticus*, Passeridae) were included as
 positive controls. The abdomens of all 51 mosquitoes were dissected under
 a binocular microscope using sterilised Vannas-Tübingen Spring Scissor
 (n°15003-08, Fine Science Tools) and stored at -80°C until DNA extraction.
  **2. File List:** Filename: db_aves02_mix.fasta Description: Database
 used for assigning sequences to taxa within Obitools. Constructed using
 EMBL database through ObiTools. Filename: multi.vs.single.reworked.csv
 Description: Already processed data, ready to be used for producing the
 figures of the manuscript. Column names and explanation listed below.
  Filename: mosquito_metadata_manuscript Description: Metadata file for the
 sampled mosquitoes. Includes location and time of sampling among others.
 Column names and explanation listed below.  **Filenames:**
 A1-1_S1_L001_R1_001.fastq.gz A1-1_S1_L001_R2_001.fastq.gz
 A1-2_S2_L001_R1_001.fastq.gz A1-2_S2_L001_R2_001.fastq.gz
 A1-3_S3_L001_R1_001.fastq.gz A1-3_S3_L001_R2_001.fastq.gz
 A2-1_S4_L001_R1_001.fastq.gz A2-1_S4_L001_R2_001.fastq.gz
 A2-2_S5_L001_R1_001.fastq.gz A2-2_S5_L001_R2_001.fastq.gz
 A2-3_S6_L001_R1_001.fastq.gz A2-3_S6_L001_R2_001.fastq.gz
  P1_S13_L001_R1_001.fastq.gz P1_S13_L001_R2_001.fastq.gz
 P2_S14_L001_R1_001.fastq.gz P2_S14_L001_R2_001.fastq.gz
  Culi-1_S1_L001_R1_001.fastq.gz Culi-1_S1_L001_R2_001.fastq.gz
  single.Vic-Culi01_S2_L001_R1_001.fastq.gz
 single.Vic-Culi01_S2_L001_R2_001.fastq.gz
 single.Vic-Culi02_S3_L001_R1_001.fastq.gz
 single.Vic-Culi02_S3_L001_R2_001.fastq.gz
  single.Vic-Plas01_S4_L001_R1_001.fastq.gz
 single.Vic-Plas01_S4_L001_R2_001.fastq.gz
 single.Vic-Plas02_S5_L001_R1_001.fastq.gz
 single.Vic-Plas02_S5_L001_R2_001.fastq.gz
  MultiplexVictoirPlate1_S1_L001_R2_001.fastq.gz
 MultiplexVictoirPlate2_S2_L001_R2_001.fastq.gz
 MultiplexVictoirPlate3_S3_L001_R2_001.fastq.gz
 MultiplexVictoirPlate1_S1_L001_R1_001.fastq.gz
 MultiplexVictoirPlate2_S2_L001_R1_001.fastq.gz
 MultiplexVictoirPlate3_S3_L001_R1_001.fastq.gz  Description: General File
 name description for all sequence raw files which end in “.fastq.gz”
 -            R1 = Forward reads -            R2 = Reverse reads
 -            S1-S14 = Library index -            Multiplex = Multiplex
 sequences (combination of Aves, Plas and Culi primers) -            A1-1
 to A2-3 = Singleplex sequences obtained using Aves02 primer pairs
 -            P1 to P2 = Singleplex sequences obtained using Plas01 primer
 pair. -            Culi-1 = Singleplex sequences obtained using Culi01
 primer pair -            Single.Vic-Culi01 and Single.Vic-Culi02 =
 Singleplex sequences obtained using     Culi01 primer pair (to complement
 Culi-1 and Culi-2 sequences).          Complementary to Culi-1 sequences.
 -            Single.Vic-Plas01 and Single.Vic-Plas02 = Singleplex
 sequences obtained using      Plas01 primer pair (to complement Plas-1 and
 Plas-2 sequences)  File name: ngs.multiplex.culi.txt Description: file to
 associate multiplex sequences (from all MultiplexVictoirPlate*.fastq.gz
 files) to each individual sample using the forward and reverse unique tag
 combinations for the Culi01 primer pair.  File name:
 ngs.multiplex.aves.txt Description: file to associate multiplex sequences
 (from all MultiplexVictoirPlate*.fastq.gz files) to each individual sample
 using the forward and reverse unique tag combinations for the Aves02
 primer pairs.  File name: ngs.multiplex.plas.txt Description: file to
 associate multiplex sequences (from all MultiplexVictoirPlate*.fastq.gz
 files) to each individual sample using the forward and reverse unique tag
 combinations for the Plas01 primer pair.  File name: ngs_plas01.txt
 Description: file to associate singleplex sequences from
 single.Vic-Plas01_S4_L001_R1+R2 to each individual sample using the
 forward and reverse unique tag combinations for the Plas01 primer pair.
  File name: ngs_plas02.txt Description: file to associate singleplex
 sequences from single.Vic-Plas02_S5_L001_R1+R2 to each individual sample
 using the forward and reverse unique tag combinations for the Plas01
 primer pair.  File name: ngs_culi01.txt Description: file to associate
 singleplex sequences from single.Vic-Culi01_S2_L001_R1+R2 to each
 individual sample using the forward and reverse unique tag combinations
 for the Culi01 primer pair.  File name: ngs_culi02.txt Description: file
 to associate singleplex sequences from single.Vic-Culi02_S3_L001_R1+R2 to
 each individual sample using the forward and reverse unique tag
 combinations for the Culi01 primer pair.  File name: ngs_plasmo.txt
 Description: file to associate singleplex sequences from P1_S13_L001_R1+R2
 and P2_S14_L001_R1+R2 to each individual sample using the forward and
 reverse unique tag combinations for the Plas01 primer pair.  File name:
 ngs_aves1.txt Description: file to associate singleplex sequences from all
 A1-*.fastq.gz files to each individual sample using the forward and
 reverse unique tag combinations for the Aves02 primer pairs.  File name:
 ngs_aves2.txt Description: file to associate singleplex sequences from all
 A2-*.fastq.gz files to each individual sample using the forward and
 reverse unique tag combinations for the Aves02 primer pairs.  File name:
 ngs_culi01.singleplex.txt Description: file to associate singleplex
 sequences from Culi-1_S1_L001_R1+R2 to each individual sample using the
 forward and reverse unique tag combinations for the Culi01 primer pair.
  DATA-SPECIFIC INFORMATION FOR: multi.vs.single.reworked.csv 1\. Number of
 variables: 11 2\. Number of cases/rows: 172 3\. Variable List: -      
 Sample: mosquito Sample ID -       Primer: Primer pair used -      
 Sequence: Unique sequence -       scientific_name2.Singleplex: Consensus
 scientific name obtained after taxonomic assignation (first through
 obiTools, then double checked using BLAST) for the singleplex approach.
 -       rra.singleplex: Relative read abundance (RRA) for each sequence in
 each sample per primer pair for the singleplex approach. -      
 reads.singleplex: raw amount of reads for each sequence in each sample per
 primer pair for the singleplex approach. -      
 scientific_name2.Multiplex: Consensus scientific name obtained after
 taxonomic assignation (first through obiTools, then double checked using
 BLAST) for the multiplex approach. -       rra.multiplex: Relative read
 abundance (RRA) for each sequence in each sample per primer pair for the
 multiplex approach. -       reads.multiplex: raw amount of reads for each
 sequence in each sample per primer pair for the multiplex approach.
 -       taxonomy: 3 levels (Different, Same, NA). “Same” when both
 taxonomic assignations reached the same taxon for the two approaches.
 “Different” when the assignation differed. “NA” when only one of the two
 approaches produced a sequence for a particular sample and primer pair.
 -       Sequence.multiplex: In case the previous column is “Different”,
 the “sequence.multiplex” column shows the different sequence produced
 using the multiplex approach compared to singleplex approach, for each
 primer pair and sample.  4. Missing data codes:             NA, “Not
 applicable” DATA-SPECIFIC INFORMATION FOR: mosquito_metadata_manuscript
 1\. Number of variables: 6 2\. Number of cases/rows: 36  3. Variable List:
 -       Sample: mosquito sample ID -       Mosquito species (visual
 identification): mosquito species or genus based on a preliminary visual
 identification. -       Sampling date: Days within which the mosquito was
 captured. -       Sampling month: Month of sampling -       Sampling year:
 Year of sampling -       Location: Sampling location within Switzerland.
 Two locations sampled. GPS coordinates provided in the main manuscript.
  4. Missing data codes:             None  DATA-SPECIFIC INFORMATION FOR:
 ALL ngs_* files. 1\. Number of variables: 7 2\. Number of rows: 87-96 3\.
 Variable list:  Column1: Experiment name Column2: Replicate name Column3:
 Forward tag : Reverse tag Column4: Forward primer Column5: Reverse primer
 Column6: "F" - Needed to run Obitools Column7: Position in the
 plate  **INSTRUCTIONS TO PROCESS RAW SEQUENCING OUTPUT**  32 sequencing
 files, depending if they were multiplex or singleplex, they contain
 sequences from one or the three primer pairs together. For all files, the
 procedure is the same.  1. Unzip raw files 2\. Join paired ends to have
 aligned dataset 3\. Use the correct ngs_files accordingly for the
 appropiate sequencing file to assign sequences to each individual sample.