ABSTRACT
Despite the proven value of applying genomic data for epidemiological purposes, commonly used high-throughput sequencing formats are not adapted to the response times required to intervene and finally control outbreaks. In this study, we propose a fast alternative to whole-genome sequencing (WGS) to track relevant microbiological strains: nanopore sequencing of multiple amplicons including strain marker single nucleotide polymorphisms (SNPs). As a proof a concept, we evaluated the performance of our approach to offer a rapid response to the most recent public health global alarm, the monkeypox virus (MPXV) global outbreak. Through a multisequence alignment, a list of 42 SNPs were extracted as signature makers for this outbreak. Twenty primer pairs were designed to amplify in a multiplex PCR the regions including 22 of these SNPs. Amplicon pools were sequenced in a MinION device, and SNPs were called in real time by an in-house bioinformatic pipeline. A total of 120 specimens (95 MPXV-PCR positive, Ct values from 14 to 39) were selected. In 67.37% of the positive subset, all 22 SNPs were called. After excluding low viral load specimens, in 92% of samples ≥11 outbreak SNPs were called. No false positives were observed in any of the 25 negative specimens. The total turnaround time required for this strategy was 5 hours, and the cost per sample was 14 euros. Nanopore sequencing of multiple amplicons harboring signature SNPs escapes the targeting limitations of strain-specific PCRs and offers a powerful alternative to systematic WGS, paving the way to real-time genomic epidemiology and making immediate intervention possible to finally optimize transmission control.
IMPORTANCE Nanopore sequencing of multiple amplicons harboring signature single nucleotide polymorphisms (SNPs) escapes the targeting limitations of strain-specific PCRs and offers a powerful alternative to systematic whole-genome analysis, paving the way to real-time genomic epidemiology and making immediate intervention possible to finally optimize transmission control.
KEYWORDS: MPXV, monkeypox virus, nanopore, amplicons, sequencing
OBSERVATION
The COVID-19 pandemic has demonstrated how valuable systematically obtained genomic data are to track the global dispersion of emerging SARS-CoV-2 variants (1). Similarly, rapid genomic analysis after the first cases of the monkeypox virus (MPXV) outbreak were diagnosed in Europe allowed us to determine the most likely source in Africa from whence the cases were initially exported before spreading across Europe. The exploitation of subsequent data identified the genomic signatures shared by the cases involved in this outbreak variant, including 42 mutations not present in its most recent ancestor. Genomic epidemiological analysis has proven to be equally valuable to track the transmission of many other pathogens, both at a population context level and in a nosocomial setting (2–4).
Despite the proven value of applying genomic data to epidemiological purposes, we must acknowledge that the analysis times associated with commonly used high-throughput formats are not adapted to the real-time identification of epidemiologically relevant strains that would be required to pursue maximum efficiency to intervene and finally control outbreaks.
To accelerate response times and reduce costs, we previously proposed an alternative to systematic genomic analysis to track the transmission of Mycobacterium tuberculosis high-risk strains (5). Our strategy was based on (i) identifying the more active transmission clusters based on molecular analysis (VNTR [Variable Number of Tandem Repeats]), (ii) WGS analysis of several representatives of the clusters to be preferentially surveyed, (iii) identifying specific single nucleotide polymorphism (SNP) markers for the strains involved, and (iv) designing multiplex allele-specific PCRs to target strain-specific markers. Based on the prospective application of these PCRs, we succeeded in optimizing the surveillance of tuberculosis transmission hot spots in Spain, France, Panama, Argentina, Costa Rica, and Morocco (6–8).
In this study, we aimed to maintain the same philosophy while improving its performance by markedly increasing the number of specific SNPs simultaneously targeted, which constituted the bottleneck of our previous multiplex-PCR-based approach. To address this, we transferred our initial strategy to multiple-amplicon rapid sequencing in nanopores using the MinION system (Oxford Nanopore Technologies, Oxford, UK). As a proof of concept, we evaluated the performance of our approach in offering a rapid response to the most recent current public health global alarm, the MPXV global outbreak, during which Spain has accumulated the third highest number of cases in the world, according to the Centers for Disease Control and Prevention.
First, we downloaded the 54 current outbreak genomes available at the NCBI and Virological.org up to 2 June 2022, as well as two references from the 2019 outbreak and two representative sequences from clades I and II. Using MAFFT (9) with an FFT-NS-2 strategy, we performed a multiple sequence alignment of these genomes, keeping the SNPs shared among the 2022 outbreak sequences but absent in their most recent ancestor strains, preceding the outbreak. The final list of 42 SNPs considered by our analysis to be the signature markers for this outbreak also corresponded to those proposed elsewhere (2).
Second, we designed 20 primer sets to obtain 320- to 340-bp amplicons, which included 22 of the 33 signature SNPs (9 SNPs were discarded for being part of tandem repeat sequences located at the ends of the viral genome); each amplicon included one marker SNP, except two that included two markers (Table 1). Primer sequences were designed to share the same PCR conditions so as to multiplex them in the same reaction. The PCR conditions were as follows: 2.5 mM MgCl2, 0.2 μM each primer, 200 μM dNTPs, and 0.4 μL AmpliTaq Gold (Applied Biosystems, Foster City, CA, USA) in a final reaction volume of 25 μL. The PCR was run at 95°C for 5 min, followed by 30 cycles (95°C for 1 min, 60°C for 1 min, and 72°C for 1 min), and a final extension step at 72°C for 10 min. A pool of 20 amplicons from each positive MPXV specimen, as per the LightMix modular monkeypox virus kit (Roche, Basel, Switzerland), was used to prepare the sequencing library using a rapid barcoding kit (SQK-RBK110-96, Oxford Nanopore Technologies, Oxford, UK), which was loaded in a Minion device (Flow cell r9.4.1). A pipeline (https://github.com/MG-IiSGM/virION) was prepared for real-time analysis of the sequences obtained from the nanopore sequencing. The pipeline first preprocessed the fast5 raw files into FastQ files; then aligned them against the MPXV-UK_P2 (MT903344.1), an ancestral strain for the outbreak; and finally called the 22 SNPs selected as specific for the outbreak strain.
TABLE 1.
Primer sequences for the multiplex PCR and SNP associated with each amplicon
| SNPa ID | SNP positionb | Primer ID | Primer sequence |
|---|---|---|---|
| SNP6 | C7780T | SNP6_MPXV_F | TCTGATGTTGTTGTTCGCTGC |
| SNP6_MPXV_R | TGCCTACTAATACAAGCACAATACC | ||
| SNP10 | G30376A | SNP10_MPXV_F | CGGCATATTAACCCAAGCAGC |
| SNP10_MPXV_R | ACAACCACGCAAATCATACAAGT | ||
| SNP11 | G31062A | SNP11_MPXV_F | AGAAGTTAGACTGTGAATGTCAAGG |
| SNP11_MPXV_R | CGAAAAACGTGTGGGTGAATACC | ||
| SNP12 | G34468A | SNP12_MPXV_F | AACCACAATTTGCTTCCGCC |
| SNP12_MPXV_R | TAAAAATTGCGCGCTCCGAA | ||
| SNP13 | G38369A | SNP13_MPXV_F | CATCCACAGTTATTGGGCCAG |
| SNP13_MPXV_R | GCGTCCCGAGATTGATGTGT | ||
| SNP15 | C39148T | SNP15_MPXV_F | GCTGTTTTCATCAAGGTTTGTATCC |
| SNP15_MPXV_R | CGCCAGAAGTCTAGATGCGT | ||
| SNP17 | G54126A | SNP17_MPXV_F | TGTCAAATATTGCTCGTCCAACG |
| SNP17_MPXV_R | GTCGAAATGGCGAGACCGTA | ||
| SNP18 | G54644A | SNP18_MPXV_F | AAACATTGGGGAAGAGCCGT |
| SNP18_MPXV_R | ACGCGGTTATTTCAGTCGTG | ||
| SNP19 | G64306A | SNP19_MPXV_F | TCCACTTCGGATCCATAAGCAA |
| SNP19_MPXV_R | GCACCTGAGCAGACCCAAT | ||
| SNPs20/21 | C73075T/G73248A | SNP20_21_MPXV_F | AATCGATAAATTGCGCCAAATTGTG |
| SNP20_21_MPXV_R | TGTCCTAATAGGCATCAGTTCCTTG | ||
| SNP22 | G74214A | SNP22_MPXV_F | ACCCAATTGCTGTCGCAC |
| SNP22_MPXV_R | TGTGCGCGTAAATGATGCAA | ||
| SNP23 | G77392A | SNP23_MPXV_F | CTTCCGTCAAGGATCATCACCTA |
| SNP23_MPXV_R | AACACATTCCAATAGTCCCAGAAAA | ||
| SNP25/26 | C82382T/G82460A | SNP25_26_MPXV_F | TTGCCTGGTGATTGGGTAGAA |
| SNP25_26_MPXV_R | AAACAATGAATACGCTGCTACGA | ||
| SNP27 | G95043A | SNP27_MPXV_F | CTGGAAGACAAGTTAGATGGTGT |
| SNP27_MPXV_R | TTCTTCGGTATCCTTGTATCTAACT | ||
| SNP28 | G124139A | SNP28_MPXV_F | ATGAAGACGGCGATTTCGTAGA |
| SNP28_MPXV_R | TTTTCTGCCGTGTGTGGTCA | ||
| SNP29 | G124683A | SNP29_MPXV_F | TCCGGATGTATTAATCGTAGTCAGT |
| SNP29_MPXV_R | GGTCCATCTAGTCGTTTTACCA | ||
| SNP30 | C128707T | SNP30_MPXV_F | ATGTACTCATGGCTACGGCATT |
| SNP30_MPXV_R | GACGATCTACCGATTCCGTTT | ||
| SNP32 | A151472C | SNP32_MPXV_F | ATGATGTTACCCGATCCTCTCTT |
| SNP32_MPXV_R | CTGTGGATCGCTGAGGAAAGT | ||
| SNP36 | G181995A | SNP36_MPXV_F | CAGCAGTTCCTTATGATCAACGAT |
| SNP36_MPXV_R | TGGTGGAAGATATGAAGGTGTCG | ||
| SNP37 | C183534T | SNP37_MPXV_F | ACGCCCTACAAATGATGGTCT |
| SNP37_MPXV_R | ACCAACCTCCACATATTCTGGT |
SNP, single nucleotide polymorphism.
Based on MT903344.1 reference.
For our evaluation, we selected PCR-positive and -negative specimens from cases with an MPXV infection suspicion received in our institution for reverse transcription (RT)-PCR testing. Between June 21 and September 16, 2022, a total of 120 specimens (104 cutaneous lesions, 5 rectal exudates, 3 rectal/pharyngeal pools, 3 plasma, 2 pharyngeal exudates, 2 abscesses, and 1 cerebrospinal fluid [CSF]) were selected. Among these, 95 samples from 87 different patients were RT-PCR-positive (Ct values ranging from 14 to 39). The specimens were sequenced and run in 12 independent MinION runs (10 specimens per run were loaded, using a different set of indexes per run).
In 64 specimens (67.37% of the positive subset), all 22 SNPs were called at ≥30X depth (average coverage (477.72 times) after 60 min of running (to ensure a response time similar to that required for an RT-PCR assay). If we lowered the threshold for the number of SNPs properly called to 12 (≥20X depth), we increased the number of MPXV identifications to 73 (77%). Among the remaining specimens, after having eliminated 10 with low viral loads (Ct value >30), with 5 called ≥11 SNPs (≥12X depth). In summary, after excluding low viral load specimens, 92% of samples assayed called a sufficiently high set of specific MPXV outbreak SNPs. In all but 2 of the 25 PCR-negative specimens, no SNPs were called; a single SNP, with very low coverage (>5X depth), was called in two specimens.
It could be considered a limitation that not all targeted SNPs were called in some of the specimens with low viral loads. We must consider that, first, 80% of the total 644 specimens with a positive MPXV PCR in our laboratory presented Ct values <29; therefore, specimens with lower viral loads were rather infrequent, most corresponding to plasma samples. Second, we evaluated whether the reduction in the number of SNPs called in low viral load specimens could lead to misidentifications of the MXPV outbreak strain. To this end, we performed an in-silico evaluation of the number of targeted SNPs that would be called in other monkeypox viruses. We downloaded 20 FastQ files from monkeypox virus (4 from 2019 and 16 from 2020). None of the targeted SNPs were called, except for one SNP in one case. When proceeding similarly with FastQ files from other orthopoxviruses (camelpox, cowpox, vaccinia, variola, etc.), marked differences were found in the sequences flanked by the 20 primer sets used in our approach, and therefore, potential misidentifications were fully ruled out. All these data together signify that, even when supporting our identification on the calling of a subset of the total SNPs targeted, the identification of the MPXV outbreak strain is highly specific.
In five specimens, we identified one or two private SNPs in the regions analyzed in our strategy. This indicated that with just 3.3% of the genome sequenced, we could capture some of the diversity acquired by MPXV during this outbreak (Public Health England (PHE) technical briefing 7).
The systematic application of RT-PCR in COVID-19 diagnosis has taught us the usefulness of having an indirect inference of viral load, based on Ct values (10). Similarly, we aimed to evaluate whether we could extract some inference regarding MPXV viral load in the specimen from our nanopore sequencing data. For each specimen, we extracted several parameters from the sequencing run: final average coverage, number of SNPs called at ≥30X depth, and number of active pores/sequencing pores in the flow cell. From these values, we defined a model of multiple linear regression. When we compared the experimental Ct values obtained by RT-PCR for each specimen with the values inferred from our model, we observed a deviation of ±3 (Table 2). This inference was far from being precise, but it could still be useful to categorize each specimen into a range of low/intermediate/high viral load.
TABLE 2.
Results obtained in the projecta
| Sample | ENA | Sample | SNPs |
Mean stats |
Ct |
|||||
|---|---|---|---|---|---|---|---|---|---|---|
| Called | Outbreak | >30X depth | Coverage | Frequency | PCR | Inferred | Difference | |||
| 6617233 | ERS13545281 | Cutaneous lesion | 22 | 22 | 22 | 974 | 0.96 | 20 | 20.00 | 0.00 |
| 6617225 | ERS13545282 | Cutaneous lesion | 22 | 22 | 16 | 43.35 | 0.97 | 24 | 25.38 | 1.38 |
| 6617224 | ERS13545283 | Cutaneous lesion | 22 | 22 | 22 | 114.35 | 0.97 | 23 | 23.74 | 0.74 |
| 6617273 | ERS13545284 | Cutaneous lesion | 22 | 22 | 21 | 81.7 | 0.96 | 25 | 24.10 | −0.90 |
| 6617344 | ERS13545285 | Cutaneous lesion | 22 | 22 | 4 | 19.48 | 0.98 | 26 | 28.15 | 2.15 |
| 6624173 | ERS13545286 | Cutaneous lesion | 22 | 22 | 22 | 1,142.04 | 0.96 | 16 | 19.27 | 3.27 |
| 6627502 | ERS13545287 | Cutaneous lesion | 22 | 22 | 22 | 727.78 | 0.96 | 21 | 21.07 | 0.07 |
| 6617279 | ERS13545288 | Cutaneous lesion | 22 | 22 | 22 | 1,392.57 | 0.96 | 19 | 18.18 | −0.82 |
| 6624186 | ERS13545289 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6617222 | ERS13545290 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628559 | ERS13545291 | Cutaneous lesion | 22 | 22 | 22 | 527.35 | 0.97 | 22 | 21.62 | −0.38 |
| 6610604 | ERS13545292 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628537 | ERS13545293 | Abscesses | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6617380 | ERS13545294 | Cutaneous lesion | 22 | 22 | 22 | 1,381.22 | 0.96 | 16 | 17.90 | 1.90 |
| 6617361 | ERS13545295 | Cutaneous lesion | 22 | 22 | 22 | 797.26 | 0.96 | 19 | 20.44 | 1.44 |
| 6617362 | ERS13545296 | Cutaneous lesion | 22 | 22 | 22 | 713.39 | 0.97 | 20 | 20.81 | 0.81 |
| 6617363 | ERS13545297 | Cutaneous lesion | 22 | 22 | 20 | 60.83 | 0.97 | 24 | 24.09 | 0.09 |
| 6628926 | ERS13545298 | Cutaneous lesion | 22 | 22 | 22 | 1,157.09 | 0.96 | 17 | 18.88 | 1.88 |
| 6628925 | ERS13545299 | Rectal/pharyngeal | 14 | 14 | 0 | 9.07 | 1 | 29 | 28.76 | −0.24 |
| 6628927 | ERS13545300 | Rectal/pharyngeal | 0 | 0 | 0 | 0 | 0 | 36 | ||
| 6628164 | ERS13545301 | Cutaneous lesion | 1 | 1 | 0 | 6 | 0.83 | 39 | ||
| 6617307 | ERS13545302 | Cutaneous lesion | 1 | 1 | 0 | 5 | 1 | NEG | ||
| 6617269 | ERS13545303 | Plasma | 0 | 0 | 0 | 0 | 0 | 32 | ||
| 6617395 | ERS13545304 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6617314 | ERS13545305 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628217 | ERS13545306 | Cutaneous lesion | 22 | 22 | 22 | 1,019.39 | 0.96 | 19 | 19.15 | 0.15 |
| 6628164-5 | ERS13545307 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | 39 | ||
| 6617307-5 | ERS13545308 | Cutaneous lesion | 1 | 1 | 0 | 5 | 1 | NEG | ||
| 6617269-5 | ERS13545309 | Plasma | 3 | 3 | 0 | 7.67 | 0.91 | 32 | ||
| 6628217-5 | ERS13545310 | Cutaneous lesion | 21 | 21 | 20 | 1427 | 0.96 | 19 | 17.83 | −1.17 |
| 6621312 | ERS13545311 | Rectal Exudates | 22 | 22 | 22 | 655.09 | 0.97 | 29,73 | 20.42 | −9.31 |
| 6622760 | ERS13545312 | Rectal exudates | 22 | 22 | 22 | 361.36 | 0.96 | 18,4 | 21.69 | 3.29 |
| 6628226 | ERS13545313 | Cutaneous lesion | 6 | 6 | 0 | 6.83 | 1 | 27 | 28.13 | 1.13 |
| 6628227 | ERS13545314 | Cutaneous lesion | 16 | 16 | 1 | 15.25 | 0.98 | 31 | ||
| 6628228 | ERS13545315 | Cutaneous lesion | 22 | 22 | 22 | 559.64 | 0.96 | 19 | 20.83 | 1.83 |
| 6628235 | ERS13545316 | Cutaneous lesion | 22 | 22 | 22 | 469.77 | 0.96 | 18 | 21.22 | 3.22 |
| 6617282 | ERS13545317 | Cutaneous lesion | 22 | 22 | 22 | 164.95 | 0.96 | 24 | 22.55 | −1.45 |
| 6617283 | ERS13545318 | Cutaneous lesion | 22 | 22 | 22 | 443.86 | 0.97 | 19 | 21.33 | 2.33 |
| 6628236 | ERS13545319 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628237 | ERS13545320 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628235-5 | ERS13545321 | Cutaneous lesion | 22 | 22 | 22 | 1,002.05 | 0.96 | 18 | 18.58 | 0.58 |
| 6628236-5 | ERS13545322 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6628237-5 | ERS13545323 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6617207 | ERS13545324 | Cutaneous lesion | 22 | 22 | 22 | 1,071.09 | 0.96 | 19 | 18.28 | −0.72 |
| 6617270 | ERS13545325 | Cutaneous lesion | 22 | 22 | 22 | 697.59 | 0.96 | 22 | 19.91 | −2.09 |
| 6617271 | ERS13545326 | Cutaneous lesion | 22 | 22 | 22 | 1,320.27 | 0.96 | 23 | 17.20 | −5.80 |
| 6617113 | ERS13545327 | Cutaneous lesion | 22 | 22 | 22 | 738.55 | 0.96 | 21 | 19.73 | −1.27 |
| 6613821 | ERS13545328 | Cutaneous lesion | 20 | 20 | 3 | 19.5 | 0.97 | 26 | 27.08 | 1.08 |
| 6613834 | ERS13545329 | Cutaneous lesion | 22 | 22 | 22 | 542.68 | 0.96 | 25 | 20.58 | −4.42 |
| 6613837 | ERS13545330 | Cutaneous lesion | 22 | 22 | 22 | 625.27 | 0.97 | 20 | 20.22 | 0.22 |
| 6628316 | ERS13545331 | Cutaneous lesion | 22 | 22 | 6 | 23.27 | 0.96 | 28 | 26.08 | −1.92 |
| 6637422 | ERS13545332 | Cerebrospinal fluid | 0 | 0 | 0 | 0 | 0 | 29 | 27.51 | −1.49 |
| 6613834-5 | ERS13545333 | Cutaneous lesion | 22 | 22 | 22 | 151.14 | 0.96 | 25 | 21.96 | −3.04 |
| 6613837-5 | ERS13545334 | Cutaneous lesion | 22 | 22 | 22 | 312.64 | 0.96 | 20 | 21.26 | 1.26 |
| 6610169 | ERS13545335 | Cutaneous lesion | 22 | 22 | 22 | 295.5 | 0.96 | 21 | 21.34 | 0.34 |
| 6610160 | ERS13545336 | Cutaneous lesion | 22 | 22 | 22 | 242.05 | 0.96 | 22 | 21.57 | −0.43 |
| 6617250 | ERS13545337 | Cutaneous lesion | 22 | 22 | 16 | 36.68 | 0.96 | 24.31 | 23.80 | −0.51 |
| 6617251 | ERS13545338 | Cutaneous lesion | 22 | 22 | 22 | 218.82 | 0.96 | 20.65 | 21.67 | 1.02 |
| 6610676 | ERS13545339 | Abscesses | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6617265 | ERS13545340 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6613672 | ERS13545341 | Cutaneous lesion | 22 | 22 | 22 | 238.59 | 0.96 | 25 | 21.26 | −3.74 |
| 6613821-5 | ERS13545342 | Cutaneous lesion | 12 | 12 | 0 | 7.92 | 0.97 | 26 | 27.16 | 1.16 |
| 6628333 | ERS13545343 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6629444 | ERS13545344 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6630612 | ERS13545345 | Cutaneous lesion | 22 | 22 | 19 | 46.18 | 0.96 | 26 | 22.76 | −3.24 |
| 6618985 | ERS13545346 | Cutaneous lesion | 22 | 22 | 22 | 508.82 | 0.96 | 17 | 20.08 | 3.08 |
| 6617131 | ERS13545347 | Cutaneous lesion | 20 | 20 | 0 | 13.8 | 0.97 | 28 | 27.13 | −0.87 |
| 6608350 | ERS13545348 | Cutaneous lesion | 22 | 22 | 22 | 163.77 | 0.96 | 22 | 21.59 | −0.41 |
| 6628073 | ERS13545349 | Cutaneous lesion | 22 | 22 | 20 | 49.45 | 0.95 | 24 | 22.53 | −1.47 |
| 6618540 | ERS13545350 | Cutaneous lesion | 23 | 22 | 22 | 943.3 | 0.96 | 16 | 18.19 | 2.19 |
| 6633875 | ERS13545351 | Cutaneous lesion | 22 | 22 | 22 | 401.73 | 0.96 | 21 | 20.23 | −0.77 |
| 6630369 | ERS13545352 | Cutaneous lesion | 22 | 22 | 22 | 648.77 | 0.96 | 19 | 19.15 | 0.15 |
| 6630704 | ERS13545353 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6630737 | ERS13545354 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6633875-5 | ERS13545355 | Cutaneous lesion | 22 | 22 | 6 | 24.59 | 0.96 | 24 | 25.43 | 1.43 |
| 6630743 | ERS13545356 | Cutaneous lesion | 22 | 22 | 22 | 656.55 | 0.96 | 20 | 19.12 | −0.88 |
| 6630976 | ERS13545357 | Cutaneous lesion | 22 | 22 | 22 | 577.73 | 0.96 | 19 | 19.46 | 0.46 |
| 6630701 | ERS13545358 | Cutaneous lesion | 8 | 8 | 0 | 9.88 | 0.96 | 27 | 26.82 | −0.18 |
| 6630703 | ERS13545359 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | 32 | ||
| 6636100 | ERS13545360 | Cutaneous lesion | 22 | 22 | 22 | 259.09 | 0.96 | 23 | 20.85 | −2.15 |
| 6747961 | ERS13545361 | Cutaneous lesion | 22 | 22 | 22 | 345.05 | 0.96 | 20 | 20.15 | 0.15 |
| 6601275 | ERS13545362 | Cutaneous lesion | 22 | 22 | 22 | 288.18 | 0.96 | 20 | 20.40 | 0.40 |
| 6601974 | ERS13545363 | Cutaneous lesion | 22 | 22 | 22 | 280.45 | 0.95 | 30 | ||
| 6747386 | ERS13545364 | Cutaneous lesion | 22 | 22 | 22 | 543.77 | 0.96 | 15 | 19.29 | 4.29 |
| 6747387 | ERS13545365 | Rectal exudates | 22 | 22 | 22 | 399.32 | 0.96 | 17 | 19.92 | 2.92 |
| 6740475 | ERS13545366 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6630855 | ERS13545367 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | 38 | ||
| 6699957 | ERS13545368 | Cutaneous lesion | 22 | 22 | 22 | 150.32 | 0.96 | 22 | 21.00 | −1.00 |
| 6699898 | ERS13545369 | Cutaneous lesion | 22 | 22 | 22 | 80.41 | 0.96 | 23 | 21.30 | −1.70 |
| 6601077 | ERS13545370 | Pharyngeal exudates | 22 | 22 | 22 | 105.91 | 0.95 | 21 | 21.19 | 0.19 |
| 6630855-5 | ERS13545371 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6630893 | ERS13545372 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6747799 | ERS13545373 | Cutaneous lesion | 24 | 22 | 22 | 124.58 | 0.96 | 18 | 20.79 | 2.79 |
| 6747765 | ERS13545374 | Cutaneous lesion | 22 | 22 | 22 | 125.05 | 0.97 | 23 | 20.79 | −2.21 |
| 6747766 | ERS13545375 | Cutaneous lesion | 22 | 22 | 22 | 323.55 | 0.96 | 18 | 19.92 | 1.92 |
| 6740166 | ERS13545376 | Cutaneous lesion | 3 | 3 | 0 | 6.33 | 0.86 | 27 | 26.19 | −0.81 |
| 6747358 | ERS13545377 | Cutaneous lesion | 22 | 22 | 22 | 247.55 | 0.96 | 18 | 20.25 | 2.25 |
| 6682926 | ERS13545378 | Cutaneous lesion | 1 | 1 | 0 | 7 | 1 | 31 | ||
| 6670223 | ERS13545379 | Cutaneous lesion | 22 | 22 | 22 | 665.05 | 0.96 | 20 | 18.44 | −1.56 |
| 6687073 | ERS13545380 | Cutaneous lesion | 23 | 22 | 12 | 34.35 | 0.97 | 24 | 23.40 | −0.60 |
| 6664375 | ERS13545381 | Cutaneous lesion | 22 | 22 | 22 | 392.59 | 0.96 | 21 | 19.30 | −1.70 |
| 6666575 | ERS13545382 | Cutaneous lesion | 22 | 22 | 22 | 173.09 | 0.96 | 21 | 20.25 | −0.75 |
| 6642566 | ERS13545383 | Cutaneous lesion | 22 | 22 | 22 | 294.91 | 0.96 | 15 | 19.72 | 4.72 |
| 6642564 | ERS13545384 | Cutaneous lesion | 22 | 22 | 22 | 90.91 | 0.96 | 21 | 20.61 | −0.39 |
| 6662714 | ERS13545385 | Cutaneous lesion | 22 | 22 | 22 | 277.64 | 0.96 | 18 | 19.80 | 1.80 |
| 6653234 | ERS13545386 | Plasma | 0 | 0 | 0 | 0 | 0 | 37 | ||
| 6669393 | ERS13545387 | Cutaneous lesion | 22 | 22 | 22 | 456.73 | 0.96 | 21 | 19.02 | −1.98 |
| 6665712 | ERS13545388 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | 26 | 25.90 | −0.10 |
| 6747356 | ERS13545389 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6747790 | ERS13545390 | Rectal/pharyngeal | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6651064 | ERS13545391 | Cutaneous lesion | 22 | 22 | 22 | 116 | 0.97 | 22 | 20.18 | −1.82 |
| 6650976 | ERS13545392 | Rectal exudates | 22 | 22 | 22 | 170.27 | 0.96 | 14 | 19.94 | 5.94 |
| 6669392 | ERS13545393 | Cutaneous lesion | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6642550 | ERS13545394 | Cutaneous lesion | 23 | 22 | 22 | 264.26 | 0.95 | 16 | 19.54 | 3.54 |
| 6656789 | ERS13545395 | Rectal exudates | 0 | 0 | 0 | 0 | 0 | NEG | ||
| 6669307 | ERS13545396 | Cutaneous lesion | 22 | 22 | 20 | 53.59 | 0.97 | 23 | 20.90 | −2.10 |
| 6655994 | ERS13545397 | Cutaneous lesion | 22 | 22 | 22 | 112.5 | 0.96 | 23 | 20.20 | −2.80 |
| 6651585 | ERS13545398 | Pharyngeal exudates | 22 | 22 | 22 | 85.23 | 0.97 | 21 | 20.31 | −0.69 |
| 6669306 | ERS13545399 | Cutaneous lesion | 22 | 22 | 22 | 139.5 | 0.96 | 21 | 20.08 | −0.92 |
| 6655713 | ERS13545400 | Cutaneous lesion | 22 | 22 | 22 | 104.36 | 0.96 | 19 | 20.23 | 1.23 |
The table shows the code sample number with its corresponding ENA access, the area from which the sample is taken, the total SNPs that have been called, SNPs markers of the outbreak, and SNPs markers >30X depth, mean frequency, and coverage statistics, as well as Ct results, both experimental and statistically inferred with difference. Negative samples (NEG) or above Ct 30 were excluded from this Ct prediction. The 10 samples included in each run are split by shading.
The total turnaround time required by our multiple-amplicon nanopore sequencing method (multiplex PCR, library preparation, sequencing run, and flow cell wash to be reused) was 5 h. Cost per specimen was adjusted to 14 euros due to the decision to reuse one single flow cell up to 12 times. Costs for standard diagnostic MPXV RT-PCR were 18 euros/specimen. We must acknowledge that our analysis times did not accelerate the response time offered by the RT-PCR-based scheme applied to MPXV diagnosis in our institution. However, we must bear in mind that a positive result from the commercial PCRs just indicates the presence of MPXV, without being able to differentiate whether it corresponds to the outbreak strain or any other strain. It is true that in the current epidemiological scenario, with most cases out of Africa corresponding to the same clade B1, the generic PCR result may be sufficient to infer that we are detecting the outbreak strain. However, some sequences different from clade B1, belonging to clade A1, have also been recently identified in Europe and the United States (PHE technical briefing 5), corresponding to other independent exportation events. We should therefore be prepared to more precisely discriminate the strains from forthcoming cases. Moreover, our study intended to select the current MPXV outbreak just as a proof of concept to evaluate a strategy that may offer an optimized fast track of relevant public health strains in a wider spectrum of epidemiological alarms involving other viruses/bacteria.
Nanopore sequencing of multiple amplicons harboring signature SNPs escapes the targeting limitations of strain-specific PCRs and offers a powerful alternative to systematic whole-genome analysis, paving the way to real-time genomic epidemiology and making immediate intervention possible to finally optimize transmission control.
Data availability.
The data supporting the findings of this study (FastQ files) were deposited at ENA (https://www.ebi.ac.uk) under the project accession number PRJEB56588; the accession numbers of the sequenced strains used in the study can be found in Table 2.
ACKNOWLEDGMENTS
This work was supported by Instituto de Investigación Sanitaria Gregorio Marañón through intramural contracts 2021-II-PI-01 and 2021-II-PREDOC-IA-01 (for S.B.S). Fondo de Investigaciones Sanitarias (ISCIII [Instituto de Salud Carlos III]) also funded Miguel Servet Contract CPII20/00001 (L.P.-L.).
We are grateful to Helena Kruyer for editing and proofreading assistance.
Contributor Information
Laura Pérez-Lago, Email: lperezg00@gmail.com.
Darío García de Viedma, Email: dgviedma2@gmail.com.
Wen Chang, Institute of Molecular Biology, Academia Sinica.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study (FastQ files) were deposited at ENA (https://www.ebi.ac.uk) under the project accession number PRJEB56588; the accession numbers of the sequenced strains used in the study can be found in Table 2.