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Citation: Matoute, A.; Maestri, S.;
Saout, M.; Laghoe, L.; Simon, S.;
Blanquart, H.; Hernandez Martinez,
M.A.; Pierre Demar, M.
Meat-Borne-Parasite: A
Nanopore-Based Meta-Barcoding
Work-Flow for Parasitic
Microbiodiversity Assessment in the
Wild Fauna of French Guiana. Curr.
Issues Mol. Biol. 2024,46, 3810–3821.
https://doi.org/10.3390/cimb46050237
Academic Editor: Bruce S. Seal
Received: 9 February 2024
Revised: 6 March 2024
Accepted: 3 April 2024
Published: 24 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Communication
Meat-Borne-Parasite: A Nanopore-Based Meta-Barcoding
Work-Flow for Parasitic Microbiodiversity Assessment in the
Wild Fauna of French Guiana
Adria Matoute 1,2, Simone Maestri 1, Mona Saout 1,2, Laure Laghoe 1,2, Stéphane Simon 1,2, Hélène Blanquart 3,
Miguel Angel Hernandez Martinez 4and Magalie Pierre Demar 1,2,4,*
1Tropical Biome and Immunopathophysiology (TBIP), Universitéde Guyane, 97300 Cayenne, France;
adria.matoute@univ-guyane.fr (A.M.); simone.maestri@hotmail.it (S.M.); mona.saout@univ-guyane.fr (M.S.);
laure.laghoe@gmail.com (L.L.); stef240572@gmail.com (S.S.)
2U1019-UMR 9017-CIIL-Center for Infection and Immunity of Lille, Institut Pasteur de Lille, CHU Lille,
INSERM, CNRS, UniversitéLille, 59000 Lille, France
3GenoScreen, 59800 Lille, France
4Laboratoire Associédu CNR Leishmaniose, Laboratoire Hospitalo-Universitaire de Parasitologie et
Mycologie, Centre Hospitalier Andrée Rosemon, 97300 Cayenne, France; miangher@homail.com
*Correspondence: magalie.demar@ch-cayenne.fr
Abstract: French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic
pathogens that are of considerable medical or veterinary importance. Until now, there has been
limited data available on the description of parasites circulating in this area, especially on protozoan
belonging to the phylum Apicomplexa; conversely, the neighbouring countries describe a high
parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new poten-
tially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis.
However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed
Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based
on the Oxford Nanopore Technologies sequencing platform using the 18S gene of 14 Apicomplexa
positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTI-
IME pipeline. Thanks to a scoring rule, we were able to classify 10 samples out of 14 as Apicomplexa
positive and reveal the presence of co-carriages. The same samples were also sequenced with the
Illumina platform for validation purposes. For samples identified as Apicomplexa positive by both
platforms, a strong positive correlation at up to the genus level was reported. Overall, the presented
workflow represents a reliable method for Apicomplexa detection, which may pave the way for more
comprehensive biomonitoring of zoonotic pathogens.
Keywords: Apicomplexa; Amazon; NGS sequencing; meta-barcoding; wild mammals
1. Introduction
The Amazon biome stretches to the extensive Amazon basin, a territory delimited
by the Andean regions in the west and the Cerrado in the south. It covers part of Brazil
(49%), Bolivia (11%), Peru (16%), Ecuador, Colombia, Venezuela, Guyana, Suriname, and
French Guiana and hosts a wide range of wild fauna of different ecosystems. Mammals are
a potential reservoir for many zoonotic pathogens, including Apicomplexa taxa such as
Sarcocystis spp., Cryptosporidium spp., or Toxoplasma gondii [
1
,
2
]. This phylum is a diverse
group of protozoan parasites that are unicellular eukaryotes and obligate intracellularly
without flagella, and they were detected in different mammalian organs [
3
–
5
]. Using
their apical complex and their secretory organelle structure, they invade the host cell [
6
],
potentially causing chronic asymptomatic diseases or severe acute diseases [7].
In South America, many countries describe a high prevalence of these parasites
in animal meat, as Sarocystis spp. is in the tongue muscles of armadillos [
8
], in wild
Curr. Issues Mol. Biol. 2024,46, 3810–3821. https://doi.org/10.3390/cimb46050237 https://www.mdpi.com/journal/cimb
Curr. Issues Mol. Biol. 2024,46 3811
birds [
9
] and Toxoplasma gondii and Sarcocystis spp. are detected in the Alouatta guariba
clamitans [
5
]. Except for T. gondii [
3
,
10
], there are no data on the description of such
parasites circulating in French Guiana, strongly hampering the assessment of the risk of
human transmission. Indeed, humans can be infected through activities such as hunting,
fishing in or consumption of soiled water, raw or uncooked game meat [
11
–
13
], which are
greatly enjoyed by the population. Environmental changes induced by deforestation and
urbanization enable close exchanges between different ecosystems, causing a disruption in
the sylvatic cycle. The anthropization of natural environments exposes human population
to the risks of the emergence of new virulent strains from wildlife. There is, therefore, a
risk of the introduction, circulation, and emergence of new parasitic species, which are
usually not adapted to humans, potentially causing severe pathologies, as described for
Amazonian toxoplasmosis [14].
Currently, there is no standard tool for the detection of protozoa in wild animals.
Indeed, in the few studies about protozoa in meat, the authors perform conventional
PCR using 18S rRNA gene general primers, or also target Apicomplexa with specific
primers to amplify one parasitic species of the phylum [
5
,
15
,
16
]. Others also employed
the Indirect Fluorescent Antibody Test (IFAT), which relies on detecting antibodies against
Apicomplexa, and is known for its high risk of cross reactions [
17
]. All these conventional
serological or molecular methods are not suitable for the detection of co-infections. The
development of high-throughput sequencing technologies is an approach now widely
used in the environmental field to assess the diversity of microorganisms [
18
,
19
]. High-
throughput sequencing platforms allow us to comprehensively study and better reveal the
diversity of the species and co-infections present in a sample [20].
In this study, we developed a low-cost, portable, and fast workflow based on Nanopore
sequencing to target the 18S rRNA gene for Apicomplexa detection. The Nanopore reads
were analysed using the MetONTIIME pipeline, which fosters reproducibility and standard-
ization, through the underlying QIIME2 [
21
] and Nextflow [
22
] frameworks. Moreover,
thanks to the sequencing of matched samples on the Illumina platform, we validated the
workflow and showed high reliability of taxa abundances at the genus level.
2. Materials and Methods
2.1. Sampling and Samples
The animals studied were from the TBIP collection, which has been set up during
the PARALIM project, whose aim was to assess the dietary risks associated with the
consumption of wild animals; this is commonly practiced in French Guiana. Therefore, it
was possible to identify the potential food risks but also the ecological risks threatening the
survival of animal species. It is with this interest that a massive collection of animal organs
from French Guiana was built. This collection of the TBIP laboratory was constituted
on a voluntary basis and without financial compensation from hunters, slaughterhouses,
and veterinarians between 2015 and 2019. The animal species were identified using the
Ivanova et al. protocol [23].
For this study, the samples were selected according to (i) their geographical origin:
from the Cayenne Island region (from Cayenne, Dégrad-des-Cannes, Matoury, Larivot
and Stoupan), the eastern region (Cacao, Saint-Georges, National Road 2 and Bélizon),
the western region (from Sinnamary to Grand-Santi), the savannahs (from Montsinery to
Kourou); (ii) the type of organ selected according to knowledge of parasite cycles including
those consumed by the local population; and (iii) the species of the animals collected, and
the lifestyle and diet of the species that may influence the risk of their exposure to parasites.
Then, we selected 20 samples positive for Apicomplexa, according to a PCR test, for
the development of Meat-Borne-Parasite, a meta-barcoding workflow based on the Oxford
Nanopore Technologies sequencing platform.
Curr. Issues Mol. Biol. 2024,46 3812
2.2. Molecular Analysis
2.2.1. DNA Extraction
DNA purification was carried out using the QIAmp DNA mini kit (Qiagen, Paris,
France) according to the supplier’s recommendations. Lysis was performed overnight
by adding 180
µ
L of the ATL lysis buffer and 20
µ
L of proteinase K to the tissue sample
(<25 mg). Then, DNA was extracted from the lysate and the eluate was collected in
200
µ
L of elution buffer. A negative extraction control was included in the set by replacing
the biopsy with water.
2.2.2. DNA Amplification
Detection of apicomplexan parasites was performed using a conventional PCR with
the 800 bp DNA fragment encoding the universal eukaryote 18S ribosomal RNA (18S
rRNA) gene [
24
]. A set of primers ApiF18Sv1v5 (5
′
-GCC ATG CAT GTC TAA GTA TAA
GCT TT-3
′
) and ApiR18Sv1v5 (5
′
-CTT TAA CAA ATC TAA GAA TTT CACC TCT G-3
′
)
targeting V1 to V5 regions of the 18S rRNA gene were designed by the TBIP laboratory.
The mixture reaction was run in a final volume of 50 µL containing 10 µL of Hot Fire
Polymerase (HFP) enzyme (Solis Biodyne, Tartu, Estonia), 2
µ
L of primer sense and anti-
sense (5
µ
M), 33
µ
L of water, and 5
µ
L of the DNA sample. Using the Arktik thermocycler
®
(Thermo Fisher Scientific, Waltham, MA, USA), the following PCR conditions were used:
an initial denaturation at 95
◦
C for 15 min, followed by 40 cycles of denaturation at 95
◦
C
for 30 s, annealing at 54
◦
C for 45 s, and an extension at 72
◦
C for 90 s. The last stage was
final elongation at 72
◦
C for 5 min. A positive and a negative control containing known
genomic DNA and water, respectively, were included. The amplification products were
visualized in a transilluminator after migration of the samples using electrophoresis in 1.2%
agarose TBE gel. These 20 positive PCR amplicons were purified in solution according to
the FastGene kit (NIPPON Genetics EUROPE, Düren, Germany), and were then quantified
using the Qubit®(Qiagen, Paris, France).
2.2.3. Nanopore Library Preparation and Sequencing
A Nanopore sequencing library was built following the ligation sequencing kit and
native barcoding kit (SQK-LSK109 with EXP-NBD196) protocol (Oxford Nanopore Tech-
nologies, London, UK) according to the manufacturer’s instructions. The library was
then loaded on a R9.4.1 flow-cell
®
(FLO-MIN106D, Oxford Nanopore Technologies, Lon-
don, UK), and sequencing was carried out on a MinION
®
Nanopore sequencer (Oxford
Nanopore Technologies, London, UK) for 8 h using MinKNOW v22.10.7.
2.2.4. Illumina Library Preparation and Sequencing
DNA extraction was performed from genomic DNA of matched samples. Then, the
PCR amplification was carried out using the primer pair P1-TAReuk454FWd1-18S (5
′
-CCA
GCA SCY GCG GTA ATT CC-3
′
) and P1-TAReuk454REV3-18S (5
′
-ACT TTC GTT CTT GAT
YRA-3
′
), targeting the V4 region of the 18S rRNA gene [
25
]. A Diatomea strain was included
as a positive control together with two negative controls, which were, respectively, the
tissue extraction control and the PCR background of the total library preparation process.
Sequencing of the amplicon libraries was performed in a single Illumina MiSeq
®
paired-end run with 2
×
250 bp read chemistry according to the Metabiote
®
protocol for
18S gene sequencing (GenoScreen, Lille, France).
2.3. Bioinformatics Processing
2.3.1. Meta-Barcoding Pipeline for Nanopore Data Analysis
Nanopore reads were base-called using Guppy v6.3.9 integrated into MinKNOW
v22.10.7 with the “hac” model, and demultiplexing was performed requiring the presence of
barcodes at both ends. Reads were then analysed with a novel bioinformatic pipeline, called
MetONTIIME, based on the Nextflow workflow manager and QIIME2 environment [
21
,
22
].
In particular, reads with quality > 7 were filtered with NanoFilt [
26
] and compressed to the
Curr. Issues Mol. Biol. 2024,46 3813
fastq.gz format. Reads were then imported in qiime2 v.2022.8.0 using “qiime tools import”
and dereplicated using “qiime vsearch dereplicate-sequences” and “qiime vsearch cluster-
features-de-novo”, to obtain a set of representative sequences and the corresponding table
with read counts. Representative sequences were then aligned to the Silva_132_99_18S
database (accessed on 5 January 2023) using “qiime feature-classifier classify-consensus-
vsearch” requiring a minimum alignment identity of 90%, a minimum query coverage of
80%, and performing consensus taxonomy assignment among the top three hits. Taxonomy
tables were then filtered, retaining only taxa belonging to Apicomplexa phylum. A scoring
rule was developed for classifying a sample as Apicomplexa positive in case the number of
reads of the sample assigned to Apicomplexa represents at least a 5-fold increase compared
to the average number of reads from negative controls assigned to Apicomplexa in the same
sequencing run. Accordingly, samples with less than 10 reads assigned to Apicomplexa
were classified as Apicomplexa negative and were dropped from the analysis. Taxonomy
tables and barplots describing the taxonomic classification at each taxonomic level were
generated with “qiime taxa collapse” and “qiime taxa barplot”. All scripts for running
the MetONTIIME pipeline are reported in https://github.com/MaestSi/MetONTIIME
repository (accessed on 5 January 2023).
2.3.2. Meta-Barcoding Pipeline for Illumina Data Analysis
Reads were analysed with the QIIME2_Illumina pipeline. In particular, fastq.gz
files were imported in qiime2 v2022.8.0 using “qiime tools import” after generation of
manifest.txt file, and PCR primers were trimmed with “qiime cutadapt trim-paired”. Over-
lapping mates were then merged with “qiime dada2 denoise-paired”. A set of amplicon
sequence variants (ASVs) was obtained, together with a feature table, describing the oc-
currence of ASVs in each sample. The database Silva_132_99_18S (accessed on 5 January
2023) was then imported with “qiime tools import”, and then “qiime feature- classifier
extract-reads” and “qiime feature-classifier fit-classifier-naive-bayes” were used to train
a naïve Bayes classifier on the region of the 18S gene amplified using TAReuk454FWD1-
TAReukREV3 primers. ASVs were then classified with “qiime feature-classifier classify-
sklearn”. Taxonomy tables were then filtered retaining only taxa belonging to Apicom-
plexa phylum, and samples with no reads assigned to Apicomplexa were dropped from
the analysis. Taxonomy tables and barplots were generated with “qiime taxa collapse”
and “qiime taxa barplot”. All scripts for running the pipeline are reported in the https:
//github.com/MaestSi/QIIME2_Illumina repository (accessed on 5 January 2023).
2.3.3. Comparison of Nanopore and Illumina Meta-Barcoding Results
Bioinformatic analysis was carried out to obtain taxonomic assignments for both
platforms using Silva [
27
] as a reference database (accessed on 5 January 2023). Feature
tables reporting the relative frequencies of taxa for Apicomplexa positive samples, ob-
tained for both Nanopore and Illumina platforms, were merged into a single feature table.
Relative frequencies for all the taxa collapsed at different taxonomic levels were then com-
pared between the two platforms, and scatterplots were produced with ggplot2 [
28
]. The
Pearson correlation was then computed between relative frequencies obtained with the
two platforms, and a t-test was performed to estimate the probability of the association
being null.
3. Results
3.1. Sampling and Apicomplexa Positive Samples Identification
In order to set-up and validate the Meat-Borne-Parasite workflow, a total of 20 samples
from 15 individuals were first included in the study. These samples were from six different
animal species (Table 1) and were obtained from lung (5), heart (7), tongue (7), and brain (1)
tissues. They were collected in four main regions of the country: one in the Cayenne Island
region, seven in the eastern region, one in the western region, and nine in the savannahs
Curr. Issues Mol. Biol. 2024,46 3814
(Figure 1). Only 14 of them showed a clear amplicon in the gel; the remaining samples
were, therefore, dropped and excluded from the following analyses.
Table 1. Distribution of host species studied.
Order Family Species Common Name Individual
Distribution
Rodentia Agoutidae Cuniculus paca Lowland paca, Paca 1
Rodentia Dasyproctidae Dasyprocta leporina Red-rumped agouti 1
Cingulata Dasypodidae Dasypus sp. nov.
Nine-banded armadillo
7
Rodentia Caviidae Hydrochoerus hydrochaeris Capybara 3
Cetartiodactyla Cervidae Mazama americana Red brocket deer 2
Perissodactyla Tapiridae Tapirus terrestris Tapir 1
Total 15
Curr. Issues Mol. Biol. 2024, 46, FOR PEER REVIEW 5
3. Results
3.1. Sampling and Apicomplexa Positive Samples Identification
In order to set-up and validate the Meat-Borne-Parasite workflow, a total of 20 sam-
ples from 15 individuals were first included in the study. These samples were from six
different animal species (Table 1) and were obtained from lung (5), heart (7), tongue (7),
and brain (1) tissues. They were collected in four main regions of the country: one in the
Cayenne Island region, seven in the eastern region, one in the western region, and nine in
the savannahs (Figure 1). Only 14 of them showed a clear amplicon in the gel; the remain-
ing samples were, therefore, dropped and excluded from the following analyses.
Table 1. Distribution of host species studied.
Order Family Species Common Name Individual Distribution
Rodentia Agoutidae Cuniculus paca Lowland paca, Paca 1
Rodentia Dasyproctidae Dasyprocta leporina Red-rumped agouti 1
Cingulata Dasypodidae Dasypus sp. nov. Nine-banded armadillo 7
Rodentia Caviidae Hydrochoerus hydrochaeris Capybara 3
Cetartiodactyla Cervidae Mazama americana Red brocket deer 2
Perissodactyla Tapiridae Tapirus terrestris Tapir 1
Total 15
Figure 1. Geographical origin distribution of the individuals analysed.
3.2. Matched Samples Sequencing with Nanopore and Illumina Platforms
The Apicomplexa positive samples were then processed and sequenced in parallel
with Nanopore and Illumina platforms. In particular, samples for Nanopore sequencing
were PCR amplified with ApiF18Sv1v5 and ApiR18Sv1v5 primers, and sequencing was
carried out for 8 h on a MinION device, producing 233,676 reads in total (Table 2), while
samples for Illumina sequencing were PCR amplified with P1-TAReuk454FWd1-18S and
P1-TAReuk454REV3-18S primers, and sequencing was carried out on a MiSeq instrument
with a 2 × 250 paired-end mode, producing 1,569,910 reads in total (Table 2).
Figure 1. Geographical origin distribution of the individuals analysed.
3.2. Matched Samples Sequencing with Nanopore and Illumina Platforms
The Apicomplexa positive samples were then processed and sequenced in parallel
with Nanopore and Illumina platforms. In particular, samples for Nanopore sequencing
were PCR amplified with ApiF18Sv1v5 and ApiR18Sv1v5 primers, and sequencing was
carried out for 8 h on a MinION device, producing 233,676 reads in total (Table 2), while
samples for Illumina sequencing were PCR amplified with P1-TAReuk454FWd1-18S and
P1-TAReuk454REV3-18S primers, and sequencing was carried out on a MiSeq instrument
with a 2 ×250 paired-end mode, producing 1,569,910 reads in total (Table 2).
Table 2. Demultiplexed sequence counts summary.
Illumina Reads Nanopore Reads
Minimum 71,565 18
Median 78,510 5821
Mean 78,495.5 11,127.4
Maximum 91,032 42,449
Total 1,569,910 233,676
Curr. Issues Mol. Biol. 2024,46 3815
3.3. Taxonomy Assignment and Platforms Comparison
Bioinformatic analysis of sequencing reads was carried out with MetONTIIME and
QIIME2_Illumina pipelines, respectively, to obtain taxonomic assignment. The negative
controls showed no reads assigned to Apicomplexa for the Illumina platform, while up
to two reads from the negative controls were assigned to Apicomplexa for the Nanopore
platform, possibly due to demultiplexing errors. Reads from positive controls were assigned
to the expected taxa.
Only reads assigned to Apicomplexa were retained for further analyses (Figure 2).
Nanopore platform classified 10 out of 14 samples as Apicomplexa positive, according
to a scoring rule we developed, which classifies a sample as Apicomplexa positive in case
the number of reads assigned to Apicomplexa is at least 5-fold the average number of reads
assigned to Apicomplexa for negative controls. This scoring rule was adapted from previous
works describing the adoption of Nanopore sequencing for pathogen detection [
29
,
30
], while
the Illumina platform classified all 14 samples as Apicomplexa positive. In particular, the
four samples classified as Apicomplexa negative by the Nanopore platform had a very
low percentage of reads assigned to Apicomplexa also in the Illumina analysis, namely
0.22%, 0.09%, 0.04%, and 0.02% for the G0159P, G0173CR, G0173L, and G0225CR1 samples,
respectively. The host species corresponding to the identification codes are reported in
Table 3.
Curr. Issues Mol. Biol. 2024, 46, FOR PEER REVIEW 6
Tab le 2. Demultiplexed sequence counts summary.
Illumina Reads Nanopore Reads
Minimum 71,565 18
Median 78,510 5821
Mean 78,495.5 11,127.4
Maximum 91,032 42,449
Total 1,569,910 233,676
3.3. Taxonomy Assignment and Platforms Comparison
Bioinformatic analysis of sequencing reads was carried out with MetONTIIME and
QIIME2_Illumina pipelines, respectively, to obtain taxonomic assignment. The negative
controls showed no reads assigned to Apicomplexa for the Illumina platform, while up to
two reads from the negative controls were assigned to Apicomplexa for the Nanopore
platform, possibly due to demultiplexing errors. Reads from positive controls were as-
signed to the expected taxa.
Only reads assigned to Apicomplexa were retained for further analyses (Figure 2).
Nanopore platform classified 10 out of 14 samples as Apicomplexa positive, according to
a scoring rule we developed, which classifies a sample as Apicomplexa positive in case
the number of reads assigned to Apicomplexa is at least 5-fold the average number of
reads assigned to Apicomplexa for negative controls. This scoring rule was adapted from
previous works describing the adoption of Nanopore sequencing for pathogen detection
[29,30], while the Illumina platform classified all 14 samples as Apicomplexa positive. In
particular, the four samples classified as Apicomplexa negative by the Nanopore platform
had a very low percentage of reads assigned to Apicomplexa also in the Illumina analysis,
namely 0.22%, 0.09%, 0.04%, and 0.02% for the G0159P, G0173CR, G0173L, and G0225CR1
samples, respectively. The host species corresponding to the identification codes are re-
ported in Table 3.
Figure 2. Apicomplexa relative frequency in wildlife samples. For each sample, the relative fre-
quency of reads assigned to Apicomplexa for Illumina and Nanopore matched samples at level 6
(i.e., up to genus) is reported. G0***L1 indicates the host ID, the first four elements indicate its code,
followed by the leer representing the organ studied (P: lung; L or LD: tongue; CR: heart; R: spleen),
and then the number representing the organ number.
Figure 2. Apicomplexa relative frequency in wildlife samples. For each sample, the relative frequency
of reads assigned to Apicomplexa for Illumina and Nanopore matched samples at level 6 (i.e., up to
genus) is reported. G0***L1 indicates the host ID, the first four elements indicate its code, followed by
the letter representing the organ studied (P: lung; L or LD: tongue; CR: heart; R: spleen), and then the
number representing the organ number.
Table 3. Host species corresponding to the identification codes.
Host Identification Code Host Species
G0068 Hydrochoerus hydrochaeris
G0113 Cuniculus paca
G0125 Mazama americana
G0130 Tapirus terrestris
G0149 Dasypus sp. nov.
G0173 Dasyprocta leporina
G0225 Dasypus sp. nov.
G0233 Mazama americana
Curr. Issues Mol. Biol. 2024,46 3816
Interestingly, we reported some cases of co-carriage. For example, in sample G0130CR2,
we detected only Theileria spp. with Illumina, while Nanopore detected Theileria spp.,
Babesia spp., and Isospora spp. Conversely, in sample G0233CR2, Nanopore detected only
Theileiria spp., while Illumina detected both Theileria spp. and Sarcocystis spp.
Co-carriers were found in 28.5% (4/14) of samples studied with Illumina sequencing
and 90% (9/10) of samples studied with Nanopore technology. Among the organs studied,
we found certain parasites in a single organ, such as T. gondii in the heart. On the other
hand, parasites such as Babesia spp. and Theileria spp. were found in all the organs analysed:
heart, lung, and tongue.
Four protozoan families were identified through sequencing analysis: Piroplasmorida,
Eimeriorina,Coccidia,Adeleorina, and Eugregarinorida. Among these families, four main
genera (Sarcocystis spp., Theileria spp., Babesia spp., Toxoplasma spp.) were identified using
both platforms, and five other genera (Hepatozoon spp., Frenkelia spp., Isospora spp., Besnoitia
spp. Eimeria spp.) were identified with Nanopore sequencing only. At the species level,
both Nanopore and Illumina platforms identified with more than 10 reads Babesia spp.,
Sarcocystis neurona, and Theileria cervi; moreover, the Nanopore platform identified with
more than 10 reads Toxoplasma gondii,Theileria spp. Theileria equi,Theileria ovis,Hepatozoon
spp., Frenkelia glareoli,Sarcocystis dispersa, and Frenkelia microti, while, the Illumina platform
identified with more than one read, Sarcocystis spp., Sarcocystis Scandinavica, and Sarcocystis
miescheriana (Table S1).
We then focused on the 10 samples classified as Apicomplexa positive by both plat-
forms and evaluated their relative taxa abundance at level 6 (genus level). This resulted in
a strong positive correlation (r Pearson = 0.86; t-test p-value = 2.6
×
10
−11
), confirming the
soundness of the proposed approach (Figure 3).
Curr. Issues Mol. Biol. 2024, 46, FOR PEER REVIEW 8
Figure 3. Nanopore and Illumina relative frequency at level 6. For each sample, the relative fre-
quency of genera identified using Nanopore and Illumina platforms was reported and labelled with
the genus name. Dots are coloured according to sample.
4. Discussion
In this study, we describe Meat-Borne-Parasite, a Nanopore sequencing-based work-
flow for Apicomplexa detection in wildlife samples. According to the literature, this is the
first meta-barcoding study on meat targeting protozoa. An innovative matrix was used to
highlight the biodiversity of microorganisms, especially from an Amazonian Forest envi-
ronment. Numerous research teams are increasingly using meta-barcoding for the biodi-
versity analysis of microbial ecology in various matrices (fish, faeces, soil, water). These
innovative and powerful technologies are used in different fields for the detection of food
frauds, identification of fish species or inspection of the dietary diversity of animals. How-
ever, bacteria are more frequently the target of the study, compared to parasites [19,31–
34]. For example, Ludwig, A. et al., and Howells et al. described the presence of microor-
ganisms such as parasites in meat, but it was not possible for them to establish potential
co-carriage, due to the technologies used, hence the interest of our study [5,8].
Illumina MiSeq is the gold-standard and most frequently used platform in microbial
ecology studies to describe biodiversity, but in recent years Oxford Nanopore Technolo-
gies has raised a lot of interest, thanks to the low price, portability, real-time features, and
capability to sequence long reads on the MinION device. Despite many studies showing
the reliability of Nanopore sequencing for a variety of applications [35,36], the higher error
rate compared to Illumina (modal accuracy with R9.4.1 chemistry is at about 96%, Figure
S1) requires ad hoc bioinformatic pipelines and thorough validation studies. Moreover,
advancements both in the base-calling algorithms and in the sequencing chemistry have
greatly improved Nanopore sequencing accuracy. The latest “Q20+” chemistry, which
was recently released on the market, allows the production of sequencing reads with se-
quencing accuracy higher than 99% [37,38].
Multiple studies have already focused on comparing taxa abundances provided by
Nanopore and Illumina-based workflows, showing a positive correlation, although some
others showed poor correlation [39–42]. In general, multiple factors concur in determining
taxa abundances, such as the reads sequencing accuracy, bioinformatic pipeline, reference
sample_name
a
a
a
a
a
a
a
a
a
a
G0068P1
G0113L
G0125P1.2
G0130CR1
G0130CR2
G0130P
G0149LD
G0150CR1
G0233CR2
G0233L2
Figure 3. Nanopore and Illumina relative frequency at level 6. For each sample, the relative frequency
of genera identified using Nanopore and Illumina platforms was reported, and those showing a
marked difference between the two platforms were labelled with the genus name. Dots are coloured
according to sample.
4. Discussion
In this study, we describe Meat-Borne-Parasite, a Nanopore sequencing-based work-
flow for Apicomplexa detection in wildlife samples. According to the literature, this is the
first meta-barcoding study on meat targeting protozoa. An innovative matrix was used to
highlight the biodiversity of microorganisms, especially from an Amazonian Forest environ-
ment. Numerous research teams are increasingly using meta-barcoding for the biodiversity
analysis of microbial ecology in various matrices (fish, faeces, soil, water). These innovative
and powerful technologies are used in different fields for the detection of food frauds,
Curr. Issues Mol. Biol. 2024,46 3817
identification of fish species or inspection of the dietary diversity of animals. However,
bacteria are more frequently the target of the study, compared to parasites [
19
,
31
–
34
]. For
example, Ludwig, A. et al., and Howells et al. described the presence of microorganisms
such as parasites in meat, but it was not possible for them to establish potential co-carriage,
due to the technologies used, hence the interest of our study [5,8].
Illumina MiSeq is the gold-standard and most frequently used platform in microbial
ecology studies to describe biodiversity, but in recent years Oxford Nanopore Technologies
has raised a lot of interest, thanks to the low price, portability, real-time features, and
capability to sequence long reads on the MinION device. Despite many studies showing
the reliability of Nanopore sequencing for a variety of applications [
35
,
36
], the higher
error rate compared to Illumina (modal accuracy with R9.4.1 chemistry is at about 96%,
Figure S1) requires ad hoc bioinformatic pipelines and thorough validation studies. More-
over, advancements both in the base-calling algorithms and in the sequencing chemistry
have greatly improved Nanopore sequencing accuracy. The latest “Q20+” chemistry, which
was recently released on the market, allows the production of sequencing reads with
sequencing accuracy higher than 99% [37,38].
Multiple studies have already focused on comparing taxa abundances provided by
Nanopore and Illumina-based workflows, showing a positive correlation, although some
others showed poor correlation [
39
–
42
]. In general, multiple factors concur in determining
taxa abundances, such as the reads sequencing accuracy, bioinformatic pipeline, reference
database (accessed on 5 January 2023), and PCR primers used for amplification. In fact,
due to an Illumina fragment length limited to about 600–800 bp, different PCR primer pairs
are frequently used with the two platforms, greatly reducing the reciprocal overlap [
43
,
44
].
In this work, we validated our novel Meat-Borne-Parasite workflow, using Illumina
platform as a gold-standard reference. In our workflow, the bioinformatic analysis is carried
out using MetONTIIME, a novel QIIME2 pipeline based on Nextflow workflow manager,
which exploits containerized technology for running all the steps consequentially in a
resource-optimized way, enabling the streamlined analysis of multiple samples.
At the genus level, we obtained a Pearson correlation value of 0.86 between the two
platforms. Overall, we find a good agreement between the two platforms at up to the
genus level, further reinforcing the accuracy of the proposed approach. Conversely, a
Pearson correlation value of 0.31 was obtained at the species level, suggesting caution
should be used for species-level assignments with meta-barcoding workflows. Indeed,
higher confidence in species-level assignments could be obtained using whole-genome
sequencing or, at least, by combining information of multiple marker genes.
While the Illumina platform allowed the detection of the presence of Apicomplexa in
14/14 samples, our Meat-Borne-Parasite workflow allowed us to classify as Apicomplexa
positive 10/14 samples, based on a conservative scoring rule which requires strong evidence
of reads of Apicomplexa origin, to classify a sample as positive. This rule accounts for
residual demultiplexing errors, which may occur due to sequencing errors in the barcode
regions. The four discordant samples were characterized by low relative abundance in
Illumina analysis, namely 0.22%, 0.09%, 0.04%, and 0.02%, respectively, in samples G0159P,
G0173CR, G0173L, and G0225CR1. Therefore, the reasons why they were missed with
Meat-Borne-Parasite workflow may be ascribed to a lower sequencing throughput (about
15% of Illumina reads were sequenced with Nanopore), or to differential PCR primers
efficiency. The lower sensitivity issue should be largely mitigated, both increasing the
sequencing run time and using the newest R10.4.1 chemistry, which will enable higher
sequencing accuracy and, in turn, further reduce demultiplexing errors. Moreover, using
smaller Flongle flow-cells for sequencing one sample at a time may represent an effective—
although slightly more expensive—solution for higher sensitivity assays, by removing the
need for demultiplexing.
Both platforms detected multiple co-occurring Apicomplexa genera, with a single
species, Dasyprocta leoprina, carriing a single parasite, Theileria spp.; however, this can be
explained by the fact that we only studied a single individual for this animal species, and
Curr. Issues Mol. Biol. 2024,46 3818
more samples from this species should be analysed to have a better representation. Some
parasites, such as Toxoplasma gondii, are known to show tropism: accordingly, we detected it
only in the heart (one sample out of five). Conversely, Babesia spp. was found to be spread
across all organs analysed in the present study. This could also be explained by the choice
of analytical methods, in particular extraction, which could influence parasite detection.
Indeed, Toxoplasma gondii requires specific isolation, as the parasite forms cysts in cell tissue,
as described by Dubey et al. [
45
]. For this reason, other parasites, such as Babesia spp.,
are more frequently observed in various samples [
46
]. By exploiting our novel analysis
workflow, a large-scale study will have to be carried out, taking into account analytical
specificities as is the case for Toxoplasma gondii.
Finally, the Nanopore platform appears to be the most efficient one for global biodi-
versity parasites in wildlife species monitoring, and it can be used as a relevant tool for
epidemiological or ecological surveillance on potentially human and animal pathogenic
parasites, such as Sarcocystis spp., Babesia spp., and Toxoplasma gondii [
3
,
12
,
47
]. Further or
complementary analyses using specific qPCR should be carried out to specifically determine
the prevalence of species potentially pathogenic to animals and humans.
5. Conclusions
In this study, we showed the set-up of a novel workflow for Nanopore-based meta-
barcoding aimed at detecting Apicomplexa infections in animal tissues. This protocol is
simple and does not require extensive knowledge, such as morphological taxonomic identi-
fication skills. The strong correlation between the two sequencing platforms guarantees
highly accurate genus-level results.
Moreover, this study shows a parasitic abundance in the wild fauna of French Guiana
and allows the evaluation of several potential risks for humans and animals: the ecological
risk, with predominant new or emerging parasitic strains, that could decimate animal
species and the food risk for the population, since wild meat consumption is widespread.
Thanks to low-cost, portability, and real-time sequencing, Nanopore-based meta-barcoding
could enable local authorities to set up a surveillance system, thus contributing effectively
to environmental biomonitoring tasks.
Supplementary Materials: The following supporting information can be downloaded at https://
www.mdpi.com/article/10.3390/cimb46050237/s1, Figure S1. Nanopore sequencing reads alignment
identity distribution; Table S1. Nanopore and Illumina absolute frequency of Apicomplexa-assigned
reads at taxonomic level 7.
Author Contributions: Conceptualization M.P.D.; collection L.L. and M.S.; methodology A.M., S.M.,
M.A.H.M., M.S., S.S., H.B. and M.P.D.; analysis A.M., S.M. and M.P.D.; writing—original draft A.M.
and S.M.; writing—review and editing M.A.H.M. and M.P.D.; and funding acquisition M.P.D. and
L.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research benefited from European funding (ERDF/FEDER) within the framework of
the PARALIM project (Synergie n
◦
GY0012553) and from Investissement d’Avenir grants of the the
Agence Nationale de la Recherche (CEBA: ANR-10-LABEX-25-01). This study was supported by the
University of French Guiana.
Institutional Review Board Statement: The animal research protocol was approved by the Ethics
Committee of Regional Scientific Council for Natural Heritage in French Guiana for studies involving
animals and received ad hoc authorisation from local authorities (n◦R03-2024-03-06-00002).
Informed Consent Statement: Not applicable.
Data Availability Statement: Raw sequence reads generated in this study have been submitted to
the SRA database (BioProject PRJNA1009124) (accessed on July 2023).
Curr. Issues Mol. Biol. 2024,46 3819
Acknowledgments: We acknowledge the contribution of the company Genoscreen especially Maïté
LELIEVRE for performing the experiment with the Illumina technology and the Institut Pasteur
de Guyane for the advice provided. We acknowledge the company Oxford Nanopore Technolo-
gies for the travel fellowship awarded to A. MATOUTE to attend the “Congres délocalisédes
sociétés françaises de parasitologie et mycologie médicale” once the study was completed to promote
research work.
Conflicts of Interest: The authors declare no conflicts of interest.
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