Abstract

Author summary

Introduction

Campylobacter is a Gram-negative fastidious organism that requires specific atmospheric and nutritional requirements for successful culture and isolation. In high income countries, campylobacteriosis is one of the most common forms of bacterial enteritis and it is often stated that more than 95% of infections are caused by either Campylobacter jejuni or Campylobacter coli [1]. However, in low- and middle-income countries (LMICs), where campylobacteriosis is an endemic disease associated with gastroenteritis, environmental enteropathy, and growth stunting in children, disease has been shown to be associated with other Campylobacter species besides just C. jejuni and C. coli [2–6]. Due to challenges that limit culturing of Campylobacter isolates these types of infections can often be overlooked, particularly in LMICs where they are most prevalent [7].

To overcome inadequate culturing of pathogens, the Centers for Disease Control and Prevention (CDC) of the United States has highlighted the importance of culture independent diagnostic technologies (CIDTs) for the detection and quantification of foodborne bacterial illnesses [8–10]. Several CIDTs for Campylobacter species have been developed that are either antigen-based or nucleic acid amplification-based [11]. Indeed, large-scale epidemiologic studies have relied on CIDTs including quantitative polymerase chain reaction (qPCR), either in stand-alone assays or associated with array card technologies (BioFire, BD Diagnostics/ Enteric Bacterial Panel, Applied Biosystems), to determine infection and colonization in human samples [7,12–14]. Yet, these CIDTs target C. jejuni and/or C. coli rather than all Campylobacter species.

With the rapid expansion, increasing portability, and decreasing costs of next-generation sequencing (NGS), a potential sensitive alternative to PCR-based diagnostics is shotgun metagenomics. This CIDT enables the resolution of multiple Campylobacter species that are rarely cultured and for which specific probes have not been developed, as well as other pathogenic and non-pathogenic bacterial, viral, parasitic, and fungal populations present in a sample [15]. Additionally, it has the potential to overcome the major CIDT issue of not providing a pathogen genome for source traceback or outbreak investigations for public health agencies without reflex culturing [16,17]. Determining the genetic complexity and polyclonality of Campylobacter species in fecal samples from children living in highly endemic settings is a requisite for understanding transmission dynamics and attaining accurate source attribution.

Previous studies occurring in Peru have identified stool samples positive for Campylobacter by 16S rRNA gene-based PCR probes, and further differentiated for C. jejuni and C. coli via qPCR using the cadF gene. In many cases C. jejuni and C. coli were also successfully cultured from these samples. However, culturing unidentified Campylobacter species from stool samples positive for Campylobacter by 16S rRNA PCR but negative for C. jejuni and C. coli by cadF qPCR has yet to be consistently successful [5,13]. Thus, in this study, we utilize whole-genome shotgun metagenomic sequencing (WGMS), as a CIDT, to detect and identify the presence of other Campylobacter species besides C. jejuni and C. coli in fecal samples from children in Peru.

Methods

Results

Metagenomic detection of Campylobacter species

From the 50 archived fecal samples analyzed, we were able to successfully sequence and obtain data from 44 samples. Total DNA recovery from the 44 samples was quite variable and resulted in total reads per sample between 1,410 and 2,975,084 paired-reads, while overall 84% of samples had more than 150,000 reads (Table 1). The variability of DNA from the samples might be due to DNA degradation following long-term storage of the fecal samples. Nevertheless, we decided to analyze all samples with reads since previous WGMS results of infant stool samples had identified a target organism (C. infans) at >80% of all reads [36]. Mapping the sample reads against the genomes from 28 different Campylobacter species identified 41 out of 44 samples (93%) that were positive for Campylobacter reads. There were three samples in which no Campylobacter species was detected (97077, 118488, 130169). Despite negative qPCR results for C. jejuni and C. coli in these 44 samples, reads that matched C. jejuni (28/44 (63.6%)) and C. coli (6/44 (13.6%)) were identified in a majority of samples. Other Campylobacter species reads were identified in 28 samples with C. infans (26/44 (59.1%)), C. upsaliensis (4/44 (9.1%)), C. concisus (3/44 (6.8%)), C. helveticus (1/44 (2.3%)), and C. curvus (1/44 (2.3%) (Table 1). C. concisus, C. curvus, C. helveticus and C. upsaliensis were only identified in diarrheal fecal samples from children that were 10 months or more in age (Fig 1).

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Fig 1

Campylobacter species in symptomatic (diarrhea) and asymptomatic (monthly) fecal samples from children under two years of age.

Campylobacter concisus, Campylobacter curvus, Campylobacter helveticus, and Campylobacter upsaliensis are only found in diarrheal fecal samples from children over 10 months of age.

Table 1

The number of reads detected for Campylobacter infans, C. jejuni, C. coli, C. upsaliensis, and C. helveticus. C. concisus and C. curvus in fecal samples from 44 children under two years of age living in Iquitos, Peru that were positive for Campylobacter by a Genus specific 16S assay but negative for the cadF target.

Sample	Total reads	C. infans1	C. jejuni1	C. coli1	C. upsaliensis1	C. helveticus1	C. concisus1	C. curvus
13323	575,288	52 (8kb)	1,059 (281kb)	164 (27kb)			3
13812	925,310		15		8
15704	1,387,764	19	34		98 (10kb)	89 (6kb)	462 (83kb)	30
18102	2,975,084	72 (9kb)	66 (11kb)
18196	8,798	5	96*
19879	1,506,736	71,326 (1.6mb)
22997	252,136		121	12
25500	153,672	11,399 (488kb)	43		37
28671	496,196		317 (39kb)	598 (86kb)
31204	1,267,842	806 (96kb)	13		502 (63kb)
34891	221,308	77 (9kb)	12
37185	301,380		299 (58kb)	20
41480	517,620		5
52141	553,782		1,544 (258Kb)	125 (24kb)
57052	1,253,590		139 (10kb)
57847	1,714,028		21
63792	392,858	4	4
65146	14,002				8
76987	6,862	4
78740	18,192	4	41*
97077	950,608
97770	565,420	861 (154kb)					31
97915	948,314	2
101367	591,096		160
108338	1,047,414	685 (68kb)	1,734 (295kb)
118291	1,210,340	26	24
118488	711,132
124035	372,880		981 (179kb)
124473	214,924	3	31
130169	640,924
132208	205,794	6	4
147132	1,195,212	170 (4kb)	4166 (633kb)	404 (60kb)
147161	1,102,270	218*
147355	428,732	5	6
147585	1,838,554	94 (7kb)
148072	6,784	32
148216	2,454,082	18	471*
148541	2,658,540	1	446 (21kb)
150322	246,382	105 (19kb)
150354	1,412,860	32	104 (7kb)		6630 (350kb)	272 (16kb)
150410	1,281,136	1	1109 (54kb)	114 (9kb)
150583	1,410	2
150687	1,637,790	53,637 (1.5mb)			357 (41kb)		14
151303	11,598	12	7

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*Over representation of reads mapping at one locus, likely due to PCR amplification during library preparation.

¹Numbers in the paratheses represent total length along a Campylobacter genome with >50 mapped reads. These lengths do not consider the coverage of the assemblies.

Detection of C. infans using MetaWrap

Analysis of the shotgun metagenomic reads using the MetaWRAP pipeline resulted in the same identification of Campylobacter species reads for samples that had >500 reads mapped to a Campylobacter species. For samples 25500, 150687 and 19879, that possessed the most C. infans reads according to reference mapping, MetaWRAP binning modules binned (>50% completeness and <10% contamination) C. infans (Campylobacter sp. CGEMS) reads. According to Kraken2 module for sample 25500, 23% of all sequence reads and 68% of assembled reads were C. infans (Fig 2). For both samples 150687 and 19879, 6% of all reads were C. infans and 7% of assembled reads were C. infans, respectively (Fig 3). For other samples, MetaWRAP identified Campylobacter species specific reads from total reads in 36/37 (97.3%) of the samples, including C. infans as greater than 20% of the overall Campylobacter reads in samples 19879, 31204, 34891, 41480, 63792, 97770, 108338, 118291, 132208, 147585, and 150322, Overall, MetaWRAP analysis identified 11/37 (29.7%) of the samples had co-infections with multiple Campylobacter species based on two or more Campylobacter species associated with at least 10% of the Campylobacter reads each (Table 2).

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Fig 2

MetaWRAP analysis of sample #25500 that represents metagenomic determination of the presence of C. infans in the stool sample and provides a demonstration that a standard pipeline for analyzing whole-genome sequencing shotgun metagenomics data can identify multiple Campylobacter species present in the stool sample.

Additionally, those sequence reads can be assembled into partial or complete genomes that can be used for source tracking or studying transmission dynamics unlike other molecular tools. A. Kronogram used to represent/display that 23% of total sequencing reads and 98% of the Campylobacter specific sequencing reads are matching C. infans, and provides a breakdown of the number of sequence reads generated for that sample the match Campylobacter species at the different taxonomic levels. B. Kronogram used to represent/display that when present in high enough concentrations as for this sample, identified Campylobacter species can be partially or completely genome assembled using a standard analysis pipeline and provides a breakdown of the number of assembled reads generated for that sample the match Campylobacter species at the different taxonomic levels. For this sample, 68% of all the assembled reads and 100% of the Campylobacter contigs for this sample were identified as C. infans. C. Shotgun metagenomic analysis pipelines can also rely on software to bin reads/contigs based on various parameters such as GC content of the data. Blobplot of all the assembled contigs that were binned from the sample based on percentage of GC content at the Order level of taxonomic identification, which demonstrates Campylobacterales as the dominate Order in the assembled contigs and that the binning process of the shotgun metagenomic analysis can also identify Campylobacter species from the stool samples. The Campylobacter bin contained 15 contigs that equals 20,789 bp or 1.2% of the genome.

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Fig 3

MetaWRAP analysis of sample #150687 that had lower amount of sequence reads associated with Campylobacter, but had a higher overall number of sequence reads generated compared to other samples in the study.

Standard pipeline analysis was still able use shotgun metagenomics to determine the presence of C. infans in the stool sample and identify multiple Campylobacter species present in the stool sample. A. Kronogram used to represent/display that 6% of total sequencing reads and 97% of the Campylobacter specific sequencing reads are matching C. infans, and provides a breakdown of the number of sequence reads generated for that sample the match Campylobacter species at the different taxonomic levels. B. Kronogram used to represent/display that when present in high enough concentrations as for this sample, identified Campylobacter species can be partially or completely genome assembled using a standard analysis pipeline and provides a breakdown of the number of assembled reads generated for that sample the match Campylobacter species at the different taxonomic levels. For this sample, 7% of all the assembled reads and 98% of the Campylobacter contigs for this sample were identified as C. infans. C. Shotgun metagenomic analysis pipelines can also rely on software to bin reads/contigs based on various parameters such as GC content of the data. Blobplot of all the assembled contigs that were binned from the sample based on percentage of GC content at the Order level of taxonomic identification, which demonstrates Campylobacterales was binned during the process of the shotgun metagenomic analysis even with high levels of sequence reads not associated with Campylobacter and many additional bins generated from the stool sample, thus many different aspects of metagenomic analysis can identify Campylobacter species from stool samples. The Campylobacter bin contained 150 contigs that equals 1,676,492 bp or 95.6% of the C. infans genome. However, there were 171 contigs overall assembled to various Campylobacter species equaling 1,781,027 bp from the sample including 11 additional C. infans contigs that were not binned for a total C. infans genome of 1,740,218 bp or 99.2% of the complete genome.

Table 2

MetaWRAP analysis of stool samples from 44 fecal samples from children under two years of age living in Iquitos, Peru which were positive by PCR for the Campylobacter 16S assay but negative for the cadF gene.

Sample ID	Percentage of All Reads to Campylobacter	Percentage of All Campylobacter Reads				Number of Campylobacter contigs1	Number of C. infans contigs2	Number of C. jejuni contigs2
		C. infans	C. jejuni	C. coli	C. upsaliensis
13323	0.7%	3%	32%	3%	0.3%	1 (1,074 bp)	0	1 (1,074 bp)
13812	0.004%	6%	24%	0%	41%	0	0	0
15704	0.1%	3%	2%	0%	10%	4 (9,380 bp)	0	0
18102	0.03%	42%	24%	2%	1%	0	0	0
18196	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
19879	6.0%	98%	0.3%	0.05%	0.3%	99 (856,787 bp)	99 (856,787 bp)	0
22997	0.2%	3%	38%	0%	3%	0	0	0
25500	23%	98%	0.3%	0.09%	0.3%	15 (20,789 bp)	15 (20,789 bp)	0
28671	0.5%	0%	19%	59%	0.7%	0	0	0
31204	0.2%	56%	2%	0.2%	36%	3 (5,689)	1 (1,209 bp)	1 (3,198 bp)
34891	0.4%	23%	2%	4%	12%	0	0	0
37185	0.2%	0%	33%	4%	0%	0	0	0
41480	0.004%	20%	20%	0%	10%	0	0	0
52141	3%	0%	39%	4%	0.4%	2 (2,047 bp)	0	0
57052	0.04%	2%	14%	3%	0%	0	0	0
57847	0.009%	4%	7%	6%	0%	0	0	0
63792	0.002%	50%	25%	0%	0%	0	0	0
65146	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
76987	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
78740	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
97077	0.04%	2%	19%	7%	3%	1 (1,154 bp)	0	0
97770	0.4%	64%	6%	0.4%	0.1%	4 (9,603)	2 (6,546 bp)	0
97915	0.01%	4%	4%	4%	0%	0	0	0
101367	0.2%	0.3%	74%	1%	0%	3 (4,677 bp)	0	1 (1,391 bp)
108338	0.5%	80%	3%	1%	0.6%	3 (6,989 bp)	1 (1,129 bp)	1 (3,509 bp)
118291	0.008%	34%	12%	7%	7%	0	0	0
118488	0%	0%	0%	0%	0%	0	0	0
124035	8%	0%	49%	6%	0.3%	2 (3,381 bp)	0	0
124473	0.3%	4%	42%	4%	4%	0	0	0
130169	0.01%	0%	13%	10%	0%	0	0	0
132208	0.04%	25%	0%	0%	0%	1 (1,561 bp)	0	0
147132	0.7%	0.5%	33%	4%	0.2%	13 (17.707 bp)	0	6 (9,464 bp)
147161	0.9%	5%	4%	7%	12%	143 (220,649 bp)	6 (10,651 bp)	3 (5,303 bp)
147355	0.008%	15%	23%	8%	0%	0	0	0
147585	0.02%	42%	10%	8%	3%	1 (1,134 bp)	0	0
148072	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
148216	0.04%	4%	3%	0%	0%	0	0	0
148541	0.03%	0.4%	42%	0.4%	0.4%	0	0	0
150322	0.2%	63%	4%	2%	1%	0	0	0
150354	0.7%	0%	0.4%	0.1%	96%	0	0	0
150410	0.2%	0%	30%	5%	0%	0	0	0
150583	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A
150687	6.0%	97%	0.4%	0.1%	0.8%	171 (1,781,027 bp)	161 (1,740,218 bp)	0
151303	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A

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¹Numbers in the paratheses represent total amount of Campylobacter sequence data from the sample that are assembled into contigs.

²Numbers in the paratheses represent the total amount of base pairs of the genome assembled into contigs for those Campylobacter species from that sample.

MetaWRAP analysis also identified other organisms in each of these samples including potential diarrheal pathogens and intestinal flora. An advantage of shotgun metagenomics is the identification of the fecal microbiome in the samples from Peruvian children with Campylobacter species, thus allowing additional analysis of the fecal microbiome like the impact of high Campylobacter abundance or number of Campylobacter species on the alpha and/or beta diversity of the fecal microbiome (S2 Fig). For infant diarrheal sample 25500, 7% of the gut microbiome was identified as Escherichia coli and 32% was Bifidobacterium sp. For both samples 150687 and 19879, E. coli reads were the larger portion of the gut microbiome at 41% and 37% and Bifidobacterium sp. was 16% and 0%, respectively. Besides E. coli, 67% of reads for sample 19879 were from the family Enterobacteriaceae including 9% reads from Klebsiella sp. An additional advantage of shotgun metagenomics is that each of these dominate bacteria in the fecal microbiome from the different samples can be genome assembled and binned during the pipeline analysis, as was demonstrated in the majority of the fecal samples in this study (S3 Fig). Having these bins then allows for further studies such as the role these commensals or other enteric pathogens have in interacting with different Campylobacter species in the infants intestinal tract.

Discussion

We have previously demonstrated that stools from which Campylobacter is identified using qPCR, but C. jejuni and C. coli are not identified, have an elevated risk of watery diarrhea suggesting that at least some of these less well-known Campylobacter species result in clinical diarrhea in LMICs [5]. Herein, we demonstrated a method that identified Campylobacter species by mapping WSMS reads to the genomes of 27 different Campylobacter species using the reference assembler within Geneious Prime software, although, other assemblers could be used. Indeed, the metaWRAP pipeline retrieved similar results, identifying multiple Campylobacter species. The WSMS of stool samples that were previously identified as positive for Campylobacter by CIDT via 16S qPCR demonstrated how this second global CIDT process can broadly identify Campylobacter species and co-infections with multiple Campylobacters. We demonstrate that mapping the sample reads against the Campylobacter species genomes identified 41 out of 44 samples positive for Campylobacter. As previously observed [36], co-infections with multiple Campylobacter species, including C. infans and other atypical Campylobacter species including C. upsaliensis and C. concisus was demonstrated.

In this proof-of-principle study, we selected a group of archived samples from children who had tested positive for Campylobacter with a non-selective PCR probe for all Campylobacter species, but were negative for the cadF gene, one of the most common targets used to identify C. jejuni and C. coli from other Campylobacter species, particularly in LMICs. To our surprise, the majority of the samples contained C. jejuni and/or C. coli, despite the fact that the metagenomics and qPCR assays were run on the same extraction. This demonstrates that PCR using standard primers for the detection of the cadF gene is less sensitive than previously recognized.

Whole-genome shotgun metagenomic sequencing (WSMS) as a supportive, species defining CIDT strategy provides more than just confirmation of a Campylobacter species. First, our results demonstrate that WSMS can identify Campylobacter species even when the sample provides limited sequencing reads (as low as 1,400 reads), but the ability to identify the Campylobacter species depends on the amount of these organisms within the microbiome. When levels of Campylobacter were high, as seen for sample 150687 (S1B Fig), many distinct reads from around the genome were mapped. In fact, for samples with high read counts to a specific Campylobacter species, remapping the reads to diverse variable regions of the species from multiple strains, such as lipooligosaccharide and capsule biosynthesis loci [37–39], will provide additional details to the strains in the samples. At lower levels, where fewer reads were mapped, the reads discriminatory power needs to be addressed through BLASTn analysis and elimination of mapped reads that were associated with non-discriminatory regions like the rRNA loci or transposons. Species-non-confirmatory reads, such as reads mapping to rRNA loci, can map to more than one genome, and in some cases different types of bacteria. In cases where there are few reads that identify a species, species-non-confirmatory reads would require a BLASTn search, which would identify different species and reduce the level of identification accordingly. When there are species-confirmatory reads and non-confirmatory reads, the non-confirmatory reads may help validate one of species but should be used with caution to only confirm based on the species-confirmatory reads. Our study indicates that removal of bacterial conserved sequences such as rRNA loci is required to reduce false species calls. This has been described previously [40]. Finally, despite identifying Campylobacter species reads, samples with very few total sequence reads were not very useful for species confirmation or additional analysis since there are few confirmatory reads mapped to the pathogens being detected or to other organisms of the microbiome. Instead, samples that provide a few million reads would be more useful.

WSMS requires only a single sequencing method and given the breadth of species in the Campylobacter genus, a relatively efficient approach to unequivocal species identification. PCR and qPCR require distinct primer pairs for each organism to be detected and possible changes to conditions. Also, PCR/qPCR requires additional validation when altering the detectable species. With 27 possible Campylobacter species PCR/qPCR specific primer design quickly becomes impractical. Even designing a single 16S gene amplicon-based sequencing method requires determination and validation of new PCR conditions and primers to ensure each Campylobacter species may be distinguished. Using shotgun metagenomics, we utilize a method that works when there is DNA and can detect targeted organisms when there are enough reads. In this study, we were able to assemble a C. infans genome at over 95% complete for source tracking and/or transmission dynamics for one of our samples, which is useful given the nearly universal inability to culture this pathogen and lack of standardized culture protocols.

Furthermore, even validated primers like the cadF gene primers for C. jejuni and/or C. coli can produce false negatives, as demonstrated by the results of this study where over 60% of the samples were actually positive for C. jejuni and/or C. coli after a negative cadF qPCR. Third, WSMS allows multiple Campylobacter species, including different strains of the same species to be identified from a single sample. Many of the species within the Campylobacter genus are more than 10% different, so most reads only have the potential to map to the genome of one species. There are some regions that are more conserved (<10% different) between species and reads mapping to these regions are non-confirmatory. Finally, WSMS provides sequence information regarding other members of the microbiota, identify other enteric pathogens, and co-infections between Campylobacter and these other enteric bacterial organisms.

The most notable finding of this study was the number of samples which contained concurrent infections with multiple species of Campylobacter, which has recently been observed in LMIC settings [36,41]. This suggests that prolonged and persisting mixed infections are occurring in this population. The infant population under study is chronically undernourished with a high level of intestinal inflammation, which may facilitate prolonged carriage, such as the increased rate of isolation of C. concisus in patients with inflammatory bowel disease [42]. Chronic infection, in some cases over years, have been reported in immunocompromised hosts [43], and even in hosts with no evident compromised immune system as a result of microbial factors, such as a cell invasion protein [44]. Given that Campylobacter is closely related to Helicobacter, the ability of the microbe to persist in the host as a result of microbial factors is also plausible. Lastly, changes in the microbiome in the chronically infected population may be more permissive for the establishment and maintenance of Campylobacter. We have previously established that specific taxa in this cohort are associated with Campylobacter infection [44] and identified microbes that are associated with an increased risk of infection (Ruminococcus gnavus (ASV23), Dialister (ASV26), Prevotella (ASV204 and ASV275)) and taxa that are associated with a decreased risk of Campylobacter infection (Bacteroides ovatus (ASV40), Ruminococcus toraues (ASV242), Bacteroides (ASv27) and Lachnospiraceae.

C. infans was identifiable in 59.1% (26/44) fecal samples that were determined to have Campylobacter by a genus specific PCR assay, and that did not amplify the cadF gene. This potential new species of Campylobacter has only been isolated once from a patient in Europe, yet it was shown to be prevalent in fecal samples from children in Southeast Asia and Africa [36, 45, 46]. There is still limited information associated with the presence of this species and the occurrence of gastrointestinal diseases. Further studies are needed to inform its potential pathogenicity. Given the co-infection with C. infans and C. concisus in three infants, we hypothesize that C. infans presence may be associated with previously described decompartmentalization of the gastrointestinal tract with an increased presence of oropharyngeal flora in the stool of undernourished children [47].

Polyclonal pathogen populations facilitate the acquisition of antibiotic resistance in the infected host through the selection of resistant strains [48]. Our data suggest that co-infection of multiple Campylobacter species in settings of intense transmission is common and may facilitate the development of resistance and propagation of multidrug resistant Campylobacter in response to clinical therapeutics in LMIC at a higher rate than in the US or Europe where monoclonal Campylobacter infections are likely the rule. The emergence of multidrug resistant Campylobacter is often considered attributable principally to antibiotic usage in livestock and poultry production. However, our findings could suggest that in LMIC contexts, human antibiotic use may also play an important role but further investigation is needed to draw a definitive conclusion.

Conclusion

WSMS was able to efficiently identify Campylobacter species other than C. jejuni and C. coli. Other non-jejuni and non-coli Campylobacter species are prevalent among children in Peru, of which Candidatus “C. infans” was the most frequently identified Campylobacter species, although C. upsaliensis, C. concisus, C. helveticus and C. curvus were also identified. Children in Peru infected with Campylobacter often have multiple species present in the intestinal tract at one time.

Supporting information

Funding Statement

Funding for this study was provided by the Bill and Melinda Gates Foundation (OPP1066146 and OPP1152146 to MNK), the National Institutes of Health of the United States (R01AI158576 and R21AI163801 to MNK and CTP; D43TW010913 to MNK). This research was also supported in part by USDA-ARS CRIS project 2030-42000-055-00D to CTP. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

Sequence related files are available from the NCBI database (BioProject PRJNA837236). All other relevant data are available within the manuscript and its supporting information.

References