Torque teno sus virus k2a (TTSuVk2a) in wild boars from northeastern Patagonia, Argentina

Abstract

Torque teno sus virus k2a (TTSuVk2a) is a member of the family Anelloviridae that can establish persistent infections in both domestic pigs and wild boars. Its association with diseases has not been precisely elucidated, and it is often considered only as a commensal virus. This infectious agent has been reported in herds throughout the world. In this study, we investigated the detection rate and diversity of TTSuVk2a in free-living wild boars from northeastern Patagonia, Argentina. Total DNA was extracted from tonsil samples of 50 animals, nested PCR assays were carried out, and infection was verified in 60% of the cases. Sequence analysis of the viral non-coding region revealed distinct phylogenetic groups. These clusters showed contrasting patterns of spatial distribution, which presented statistically significant differences when evaluating spatial aggregation. In turn, the sequences were compared with those available in the database to find that the clusters were distinguished by having similarity with TTSuVk2a variants of different geographic origin. The results suggested that Patagonian wild boar populations are bearers of diverse viral strains of Asian, European, and South American provenance.

Introduction

Viruses of the family Anelloviridae have small circular genomes of negative-sense DNA, which are packaged in non-enveloped icosahedral particles [1, 2]. This family is characterized by its high rate of nucleotide substitution, similar to that of RNA viruses [3]. Extremely diverse, it currently encompasses 156 species that are classified in 30 distinct genera [4, 5]. Anelloviruses have been found in many vertebrate hosts (mammals and birds), and those infecting Sus scrofa are collectively known as torque teno sus viruses (TTSuVs) [2, 5, 6]. In a taxonomy subject to constant revision, at least four different TTSuVs have been described. TTSuV1a and TTSuV1b are members of the genus Iotatorquevirus (also named TTSuV1), while TTSuVk2a and TTSuVk2b of the genus Kappatorquevirus (also named TTSuV2) [5, 6]. Their genomes are approximately 2.8 kb, with three partially overlapping open reading frames (ORF1 to ORF3) and a non-coding or untranslated region (UTR) [1, 7]. TTSuVs are considered easy to transmit through the fecal–oral route and are also able to establish persistent infections. This is reflected in prevalence that, although highly variable, are often around 50% but can even approach 100% [1, 2, 6, 8]. Their association with disease is controversial and they are usually understood only as opportunistic or commensal viruses. However, there are studies that have linked them (especially TTSuV2) with an exacerbation of the clinical signs caused by circovirus infection, such as the post-weaning multisystemic wasting syndrome (PMWS) [1, 8]. Based on data obtained mainly from domestic pigs and, to a much lesser extent, wild boars, it has been concluded that TTSuVs have spread throughout the world [2, 9]. Particularly for South America, the presence of these viruses has been confirmed through several works in Brazil [10–14] and just a few more in the Southern Cone (Argentina, Chile, and Uruguay) [9, 15, 16]. In fact, as far as we know, there are only two reports about TTSuVs in Argentina. The first of them, based on a total of 19 positive samples, evaluated the diversity of TTSuV1 and TTSuV2 in domestic pigs [9]. The second detected TTSuVk2a at the population level, obtaining consensus sequences from pools of wild boar samples [15]. Therefore, this work was intended to improve the epidemiological knowledge of porcine anelloviruses in Argentina and South America, focusing on TTSuVk2a with the aim of studying its detection rate, diversity, distribution, and potential geographic origin in wild boars from northeastern Patagonia.

Materials and methods

Wild boar population studied and sampling area

The study was conducted on free-living wild boars from northeastern Patagonia (Buenos Aires and Río Negro provinces), Argentina. Animals were shot by authorized hunters between March 2016 and May 2019. Samples were obtained in collection points that were scattered over an area between − 39.09 to − 41.09° latitude and − 62.47 to − 65.88° longitude (Fig. 3a). Most of the covered land is part of private fields, where the native vegetation alternates with semi-intensive livestock production (cattle, sheep, and pigs) and agriculture. Data associated with each specimen in terms of coordinates and date of hunting, sex, size, and weight was recorded. Wild boars were classified as piglets (less than 6 months of age), juveniles (6–12 months of age), and subadults or adults (more than 12 months of age) according to body size and estimated weight [17].

Fig. 3.

TTSuVk2a spatial distribution and differences between phylogenetic clusters. a Map showing the collection points of positive samples disaggregated by cluster: A (blue dots), B (green dots), C (red dots), and D (purple dots). Negative samples are represented by gray dots (for overlapping positions, the number of cases is indicated). The inset details the area covered in northeastern Patagonia (box). b Histogram showing the distribution of spatial distances (kilometers) between the collection points of negative samples (gray bars) and between those of positive samples (black bars). Mann–Whitney U test p value is shown. c Dot and box plots showing the distribution of spatial distances in the group of negative samples and in each phylogenetic cluster. Mann–Whitney U test p values are shown only for statistically significant comparisons. ^BBonferroni corrected

Collected tissues and DNA extraction

Tonsils were separately recovered from 50 wild boars using clean and/or disposable elements and then stored at − 20 °C until further processing. After its thawing and mechanical disaggregation, 25 − 30 mg of tissue from each specimen was subjected to total DNA extraction by using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer’s instructions (proteinase K treatment was carried out for 18 h to ensure complete digestion). Purified DNA was kept at − 20 °C.

Viral detection, nucleotide sequencing, and phylogenetic analysis

TTSuVk2a detection was carried out through a nested PCR assay described by Kekarainen et al. [18], which amplifies a segment of the viral non-coding region. In the first round, the primers used were F1: 5′-AGTTACACATAACCACCAAACC-3′ and R1: 5′-ATTACCGCCTGCCCGATAGGC-3′. In the second round, the primers used were F2: 5′-CCAAACCACAGGAAACTGTGC-3′ and R2: 5′-CTTGACTCCGCTCTCAGGAG-3′. The expected size for the fragment is of about 230 bp. The PCR products were electrophoresed on a 2% agarose gel, stained with GelRed (Biotium) and visualized with UV light. Amplicons were submitted for capillary electrophoresis sequencing to Macrogen (South Korea). The obtained nucleotide sequences were compared with those in GenBank (RefSeq or Nucleotide collection (nr/nt) databases) by using the NCBI-BLAST program (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The MEGA v5.2.2 software (https://www.megasoftware.net/) package was used for sequence alignment (ClustalW), phylogenetic tree construction (neighbor-joining method, Jukes-Cantor model), and calculation of genetic distances (Jukes-Cantor model).

Temporal and spatial data

Temporal distances (days) between samples were calculated as implemented in Microsoft Excel v14.0.7268.5000 (2010) by subtracting dates. Spatial distances (kilometers) between samples were calculated from coordinates through the formula: 6371*ACOS(COS(RADIANS(90 − Latitude1))*COS(RADIANS(90 − Latitude2)) + SIN(RADIANS(90 − Longitude1))*SIN(RADIANS(90 − Longitude 2))*COS(RADIANS(Longitude1 − Longitude 2))). A map was created with R packages ggmap and ggplot2 using geolocalizations [19, 20].

Statistical analysis

Statistical tests were conducted by using the calculators available at the Statistics Kingdom website (http://www.statskingdom.com). Comparisons for sex, age, and seasonal variation in TTSuVk2a detection rate were made using the Fisher exact test. The association of genetic distance with temporal distance or spatial distance was assessed using the Spearman rank correlation test (Bonferroni correction for multiple comparisons). Differences in the distribution of spatial distances were evaluated with the Mann–Whitney U test (Bonferroni correction for multiple comparisons). Linear regressions were performed as implemented in Microsoft Excel v14.0.7268.5000 (2010).

Results

Detection rate

Total DNA samples from 50 wild boars were evaluated to determine the presence of TTSuVk2a. The infection was verified in 30 cases, which corresponded to an overall detection rate of 60%. Negative and positive cases (hereafter referred to as TTSuVk2a( −) and TTSuVk2a( +), respectively) were then analyzed in relation to the sex and age of animals. Females showed a TTSuVk2a( +) proportion of 60%, while in males, it was of 54%. The age group including piglets and juveniles exhibited the same detection rate as the group composed of subadults and adults (56%). An effect of sampling season was also considered. A 75% was calculated for TTSuVk2a( +) during spring and summer, but this value was of 57% during autumn and winter. No statistically significant differences were found for any of the above mentioned comparisons (Fisher exact test p > 0.05). Details are shown in Table 1.

Table 1.

TTSuVk2a detection rate in relation to sex, age, and sampling season

Category	Samples (n)
	TTSuVk2a( −)	TTSuVk2a( +)	Detection rate (%)	Fisher exact test p value
Total	20	30	60
Sexa
Female	8	12	60	0.7662
Male	11	13	54
Ageb
Piglet/juvenile	4	5	56	1.0000
Subadult/adult	15	19	56
Seasonc
Spring/summer	2	6	75	0.4501
Autumn/winter	18	24	57

^aData not available for 6 cases

^bData not available for 7 cases

^cSpring/summer: October to March, autumn/winter: April to September

Local diversity

The amplification products of the 30 TTSuVk2a( +) samples were subjected to direct nucleotide sequencing. To explore the viral diversity in northeastern Patagonia, a phylogenetic tree was constructed. This revealed the existence of four distinct clusters that were named A to D (Fig. 1a). Cluster A involved 12 sequences (OR594307–OR594318), cluster B 3 sequences (OR594319–OR594321), cluster C 4 sequences (OR594322–OR594325), and cluster D 11 sequences (OR594326–OR594336). Thus, major clusters A and D represented the 40% and 37% of sequences, respectively. In contrast, minor clusters B and C reached together only 23%. Next, each of the nucleotide sequences was compared to that of the isolate 2p, the TTSuVk2a reference genome (RefSeq database NC_014092.2). A percent identity ranging from 94.05 to 98.21% was observed. These values were 95.86–97.63% for cluster A, 97.02–98.21% for cluster B, 95.83–96.43% for cluster C, and 94.05–97.02% for cluster D. Data on phylogenetic clusters are summarized in Fig. 1b and Table 2.

Fig. 1.

TTSuVk2a diversity in wild boars from northeastern Patagonia. a Evolutionary tree highlighting the four distinct phylogenetic groups identified (clusters A to D). The samples associated with each of them are indicated. The tree was constructed through the neighbor-joining method, using the Jukes-Cantor model. The final dataset included 167 nucleotide positions from viral UTR (sequences OR594307–OR594336). Bootstrap values based on 1000 replicates. Only those > 50% are indicated above the branches. b Detail of the number of cases (n) and relative abundance (%) for each cluster. ^aPercent identity range of sequences compared to that of the TTSuVk2a reference genome (RefSeq database NC_014092.2)

Table 2.

TTSuVk2a sequences obtained in this study and the most similar sequences retrieved from GenBank

Sample	Sequence obtained in this work	Most similar sequence in databasea	Percent identity	Country
Cluster A
A110	OR594307	MK263690.1	97.02	China
A116	OR594308	MK263690.1	97.62	China
A117	OR594309	MK263690.1	98.21	China
A118	OR594310	MK263690.1	98.21	China
A122	OR594311	MK263690.1	97.62	China
A129	OR594312	MK263690.1	97.02	China
A133	OR594313	MK263690.1	97.62	China
A149	OR594314	MK263690.1	97.62	China
A150	OR594315	JF451684.1	98.81	Chile
A152	OR594316	MK263690.1	96.43	China
A158	OR594317	MK263690.1	97.62	China
A164	OR594318	MK263690.1	97.62	China
Cluster B
A127	OR594319	MH469980.1	98.81	Uruguay
A128	OR594320	MH469980.1	98.81	Uruguay
A163	OR594321	MH469980.1	100.00	Uruguay
Cluster C
A105	OR594322	MK263717.1	97.62	China
A141	OR594323	MK263717.1	98.21	China
A154	OR594324	MK263717.1	98.21	China
A166	OR594325	MK263717.1	98.21	China
Cluster D
A111	OR594326	JX449020.1	98.21	Romania
A113	OR594327	JF451814.1	98.21	Spain
A123	OR594328	JX449020.1	97.62	Romania
A130	OR594329	JF451819.1	97.02	South Korea
A132	OR594330	JX535335.1	97.62	China
A140	OR594331	JX449020.1	98.21	Romania
A147	OR594332	JX449020.1	98.21	Romania
A155	OR594333	JX449020.1	98.21	Romania
A156	OR594334	JX449020.1	97.02	Romania
A161	OR594335	JF451666.1	98.21	Spain
A162	OR594336	JF451814.1	97.62	Spain

^aGenBank accession numbers for sequences retrieved from the Nucleotide collection (nr/nt) database using NCBI-BLAST (accessed April 2023)

Factors linked to nucleotide divergence

To investigate whether nucleotide divergence was related to tempo-spatial changes, the association of genetic distance with both temporal distance (days between sampling dates) and spatial distance (kilometers between sampling coordinates) was studied (Fig. 2a and b). The latter correlation was found to be positive and statistically significant (Spearman rank correlation test p < 0.05, Bonferroni corrected), suggesting a potential interplay between spatial structure and virus diversity.

Fig. 2.

TTSuVk2a nucleotide divergence and its association with temporal and spatial factors. Scatter plots displaying genetic distances (base substitutions per site, Jukes-Cantor model) versus a temporal distances (days) or b spatial distances (kilometers). Linear regressions and Spearman rank correlation test p values are shown. ^BBonferroni corrected

Spatial distribution of phylogenetic clusters

To conduct a more detailed spatial analysis, a map indicating the sampling point of each TTSuVk2a( −) and TTSuVk2a( +) specimen was plotted. In turn, the dots representing TTSuVk2a( +) were differentiated according to the phylogenetic cluster to which the detected virus was assigned (Fig. 3a). On this map, an apparent aggregation of the infected wild boars was visualized. The spatial distances separating the TTSuVk2a( −) locations and those separating the TTSuVk2a( +) locations were compared to evaluate this possibility. A histogram showed that the values of the TTSuVk2a( +) series (median of 65.76 km) were centered to the left with respect to the corresponding data for TTSuVk2a( −) (median of 115.53 km) (Fig. 3b). This difference was found to be statistically significant (Mann–Whitney U test p < 0.05). Each phylogenetic cluster was then considered individually (Fig. 3c). It was observed that the major clusters A and D differed in their distribution patterns. While cluster D presented a dispersion similar to TTSuVk2a( −), cluster A was located in a more limited area (Mann–Whitney U test p < 0.05, Bonferroni corrected). For minor clusters B and C, the comparisons were inconclusive due to the small number of data.

Geographic origin of TTSuVk2a lineages found in northeastern Patagonia

To determine whether the different spatial distributions of phylogenetic clusters were linked to different origins in terms of geographic provenance, the sequences most similar to those obtained in this work were searched in GenBank (Nucleotide collection (nr/nt) database) through NCBI-BLAST (Table 2). For cluster A, sequences retrieved from the database were 92% (11 out of 12) from Asia (China), while the remaining was from South America (Chile). For cluster B, all sequences (3 out of 3) were from South America (Uruguay). For cluster C, all sequences (4 out of 4) were from Asia (China). For cluster D, 82% (9 out of 11) were from Europe (Spain and Romania) and the rest from Asia (South Korea and China). This geographic information is summarized in Fig. 4.

Fig. 4.

Country of origin of TTSuVk2a sequences retrieved from database. The sequences most similar to those obtained in this work were searched in GenBank. Their number (n) and relative abundance (%) are shown discriminated by continent and country of origin for each phylogenetic cluster

Discussion

There are a limited number of reports about the circulation of TTSuVs in wild boars from South American countries [21]. In Uruguay, an analysis performed on 31 animals estimated detection frequencies of 48% (15/31) and 3% (1/31) for TTSuV1 and TTSuVk2a, respectively [16]. In southern Brazil, a molecular survey based on 80 cases showed that 54% (43/80) were infected with TTSuV1a and 5% (4/80) with TTSuV1b, without looking for Kappatorquevirus [11]. In Argentina, an ecological study recently verified the occurrence of TTSuVk2a in free-ranging wild boars [15]. Therefore, the present work carried out in northeastern Patagonia is a significant contribution to the knowledge of the epidemiological situation of these viruses in South America. We observed a TTSuVK2a detection rate of 60%, a value similar to that reported for wild boar populations in Spain, Germany, and Romania, with a TTSuV2 prevalence ranging from 32 to 66% [3, 22, 23]. In contrast, only 2.5% of wild boars were infected with TTSuVk2a in northern Italy [24], a low frequency that could be comparable to that above mentioned for Uruguay [16]. Such differences in the proportion of infected animals are attributable (at least partially) to methodological issues, but could also be related to the epidemiological dynamics of these viruses. In fact, substantial changes (up to tenfold) have been recorded in the prevalence value from one hunting season to the next [3]. In this context, it is worth mentioning the possibility that TTSuVs may be hosted by species other than Sus scrofa. Righi et al. [24] have detected the presence of TTSuVk2a (but not TTSuV1a) in other ungulates, with a frequency of 9.4% in wild ruminants. The ecological relevance of this observation remains to be determined, although with a TTSuVk2a detection rate as high as that found in northeastern Patagonia, a viral circulation involving both domestic pigs and wild boars as well as other wildlife can be hypothesized.

The non-coding region of TTSuVs has been exploited in numerous studies aimed to investigate the genetic diversity, molecular epidemiology, phylogenetic relationships, and disease associations of these infectious agents [10, 12, 13, 15, 18, 23, 25–29]. The TTSuVk2a UTR sequences obtained in this work allowed us to identify phylogenetic clusters and discover spatial patterns, in addition to investigating the geographic origin of strains present in Argentina.

The diversity of anelloviruses in domestic pigs from numerous countries all over the world was extensively analyzed by Cortey et al. [9]. The study was carried out based on samples from Europe, Asia, Africa, Oceania, North America, and South America, to find that the distribution and genetic composition of TTSuV populations were mainly explained by livestock trade routes. Although such a situation appeared to be the case for both TTSuV1 and TTSuV2, it was the latter that showed the most significant associations. It should be noted that the high variability and transmissibility of porcine anelloviruses, combined with their ability to establish persistent infections, makes them particularly suitable as sentinels for changes in the composition of swine viral communities. Thus, under this premise, the authors verified the potential of TTSuVs as a model to track the effects of pig movements on the geographic range and characteristics of porcine infectious agents. In turn, the transmission of viruses from livestock to wild boars adds complexity to the epidemiological situation derived from the globalized exchange network [30]. Free-ranging animals contribute to the dispersal of infectious agents (identified or not) through movements outside of formal or informal pig trade routes, thus hindering epidemiological surveillance efforts. Wildlife migrations can even cross country borders, facilitating contact among viral communities of distant origins.

Considering the above, as an approach to trace the possible origins of the TTSuVK2a variants circulating in Patagonia, the most similar sequences to those we obtained from wild boars were searched in the database. Noticeably, despite the fact that TTSuVK2a sequences from domestic pigs in Argentina have been published [9], none of them was retrieved from the database through the previous analysis. Instead, the sequences found were from various countries around the world. Although the geographic provenances inferred in our study should be interpreted with caution and not be assumed as direct sources of the detected viruses (mainly due to the uneven availability of sequences by country in the database), the results strongly suggest that infectious agents reach northeastern Patagonia by more than one distinguishable pathway. This would be evidenced by the major clusters A and D, which seem to be an example of co-circulation of a TTSuVk2a phylogroup of Asian ancestry and another of European ancestry. Beyond the general spatial aggregation observed for the infections, the differences in distribution support the idea of distinct geographic origins for the aforementioned clusters, which probably arrived in our region by means of separate events. The minor cluster B, in contrast, would illustrate a different case, with viruses that could be related to strains circulating in the neighboring country of Uruguay, being easy to suppose a straight epidemiological connection with this nearby South American territory. Finally, the minor cluster C would indicate the presence of a second Asian lineage, although much less represented.

In conclusion, these silent viruses could be acting as markers revealing the encounter in northeastern Patagonia of swine viral communities of diverse origins, with the uncertain impact that such mixing and spread of virus populations might have on the incidence of emerging or re-emerging pathogens. To achieve a more complete picture of the prevalence and diversity of TTSuVs in South America, studies are needed covering larger geographic extensions and analyzing both domestic pigs and wild boars, as well as other ungulates from the region. Such a comprehensive approach to the epidemiology of these viruses could also shed some light on their possible ecological role.

Author contribution

All authors contributed to the conception and design of the study. M.W., S.A., and D.B. carried out the collection of samples and associated data. N.G.I. analyzed the data and contributed with new computational methods. D.A.B. contributed to the conceptualization of the study. F.A.D.M. and C.P.B. conducted the assays, analyzed the data, and wrote the preliminary manuscript. All authors read, discussed, and approved the final manuscript.

Funding

This study was partially supported by the Universidad Nacional de Río Negro (UNRN) (PI 40-C-717, 2018) and (PI 40-C-984, 2021).

Data availability

All data included in this study are available on request from the corresponding author.

Declarations

Ethical approval

Wild boars were hunted according to local regulations (Law 5786, decree 2578–1403/05, for Buenos Aires province; Law 2056, decree 633/86, for Río Negro province).

Conflict of interest

The authors declare no competing interests.