Evolutionary Dynamics of Prrs Virus in Italian Pig Farms: a Retrospective Study

doi:10.21203/rs.3.rs-4860203/v1

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Evolutionary Dynamics of Prrs Virus in Italian Pig Farms: a Retrospective Study

https://doi.org/10.21203/rs.3.rs-4860203/v1

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Porcine Reproductive and Respiratory Syndrome (PRRS) causes huge economic losses to pig farms worldwide. Currently available vaccines do not always offer complete protection, due to the extreme variability of the virus. Therefore, good farming practices must be improved to prevent the disease from spreading across the pig production system. In this study, we inferred the dynamics of PRRSV population in Italy by applying bayesian methods on our ORF7 sequence dataset collected during a 15-year period. Random subsets from the overall dataset were built to reduce analysis runtime. Calculated evolutionary rate was consistent between subsets and with other findings on PRRSV and other RNA viruses (4-7 x10^-3 substitution/site/year) while Time to the Most Recent Common Ancestor was less consistent (from 1980 to 1990). Despite this, in all population dynamic reconstruction a massive increase in size calculated in early 2000s lasting until aroung 2010 was inferred. This spike is followed by very heterougenous dynamics with some differences between subsets, probably due to the random sampling. Geographical origin was inferred in Emilia-Romagna region despite Lombardy being the region with the hightest number of farmed animals and farm size. These findings reflect the choices regurading farm management and biosecurity taken in the last two decades, and not strictly related to PRRS. Phylogeny and phylogeography are powerful tools to better understand microorganisms population dynamics and make appropriate choices for disease control.

PRRSV

phylogeny

phylogeography

Bayesian inference

Since its discovery in the early 90s, Porcine Reproductive and Respiratory Syndrome (PRRS) represents a major issue in pig farming and pork meat production, being responsible for consistent economic losses due to reproductive failure in sows and respiratory signs especially in piglets [1, 2], although signs can be observed in infected pigs of all ages [3]. Different studies have described losses in the breeding or growing-pig herds ranging from 12–45% of the total economic cost of PRRSV [4, 5]. The disease is caused by two viral species, showing a consistent geographical clustering: Betaarterivirus suid 1 (formerly known as PRRSV type 1), typical of the European continent, and Betaarterivirus suid 2 (formerly PRRSV type 2), spread in North America and Asia. Even if they belong to the same species, they share only 50–70% of their genome [6]. This genetic distance is likely due to their ancestor evolving in different ecological or geographical contexts over an extended period of time, and maybe originating from animal species other than swine [7]. PRRSV 1 is classified into three subtypes, from 1 to 3 [8], while type 2 can be divided into nine lineages, from 1 to 9 [9]. The genome is approximately 15 kb long, with at least 9 Open Reading Frames (ORFs) coding for both non-structural (ORF1a and 1b) and structural proteins (ORF 2–7). In particular, ORF5 and ORF7, which encode for glycoprotein 5 (GP5) and nucleocapsid (N) protein, respectively, have been the commonly used for genotyping and phylogenetic analysis due to their genetic variability, especially ORF5 [8–11]. Being a positive single-strand RNA virus, PRRSV is characterized by high genetic variability, due to high mutation rate and frequent recombination events [12, 13]. This variability has made disease control challenging, as preventive measures are often ineffective in field conditions. Modified Live Vaccine (MLV), which currently is the most widely used strategy for PRRS control, is poorly cross-protective against heterologous strains. Moreover, reversion to virulence of MLV, leading to disease outbreaks, has been reported for PRRSV [14]. In addition, groups of animals within the same farm can be found positive to different PRRSV strains [15–16]. Exploiting sequence data and metadata, phylodynamic models can provide a picture of viral evolution and transmission by reconstructing the geographic spread patterns and mapping the dynamycs of virus population [17]. The aim of this study was to (i) evaluate the variability of PRRSV-1 in Italy over a 15-years period and (ii) estimate its diffusion across the peninsula.

Samples collection and molecular screening

Overall, 7491 PRRSV positive samples collected by the Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna from 2008 to 2022 were included in the study. A variety of biological matrixes such as organs, swabs, serum, blood and semen, was tested to confirm PRRSV presence in diseased animals or for monitoring purposes. Over the considered period the diagnostic approach changed with the adoption of newer, faster and more accurate molecular protocols. Initially, from 2008 to 2011, manual RNA extraction with guanidine, chloroform and phenol was performed followed by One-Step RT-PCR reaction using a mixture of 3 primers, two type-specific and a one common primer: 5’-ATGGCCAGCCAGTCAATC-3’ (PRRSV type 1), 5’-GATTGCAAGCAGAGGGAGCGTTC-3’(PRRSV type 2), 5’- GGCGCACAGTATGATGCGTAG-3’ (common primer). The thermal protocol was as follows: 1 cycle of reverse transcription at 50°C for 30 min, 1 cycle of denaturation at 95°C for 15 min, 40 cycles of denaturation at 95°C for 30 sec, annealing at 58°C for 30 sec and extension at 72°C for 30 sec, and 1 cycle of final extension at 72°C for 7 min. Positive samples were identified through agarose gel electrophoresis and the typing was based on the molecular weight of the product: 180 bp for PRRSV-1 and 280 bp for PRRSV-2. After 2011 the molecular screening was updated by replacing both the extraction method and the PCR assay. Manual extraction was replaced with a semi-automatic method based on magnetic beads, using KingFisher Flex (Thermofisher Scientific) with the NucleoMag™ 96Virus Kit (Macherey-Nagel). A commercial qRT-PCR (i.e. XenoRNA (AgPath-ID™ NA and EU PRRSV Multiplex Reagent kit, Applied Biosystems™)) took over from end-point RT-PCR. After 2020 the diagnostic method was updated once again by replacing the previous extraction kit with the MagMAX™ CORE Nucleic Acid Purification Kit (Applied Biosystems™), and the Real Time RT-PCR kit with Virotype® PRRSV RT-PCR kit (Qiagen/Indical), allowing the European and American PRRSV strain identification together with a third Highly Pathogenic American PRRSV strain in the same reaction. Furthermore, the Virotype® kit includes specific primers and probes for the detection of an endogenous gene as internal control.

ORF7 sequencing

A 510 nucleotides (nt) region, including ORF7 (387 ± 6 nt) was the target for Sanger sequencing. A One step RT-PCR Kit (Qiagen) was set up with the specific primers: 5'CTTCGGAGCCTCGTGYTGGGCGGCAA-3’(forward) and 5'-TCGCCCTAATTGAATAGGTGA3' (reverse) according to the the following protocol: 1 cycle of reverse transcription at 50°C for 30 min, 1 cycle of initial denaturation at 95°C for 15 min, 42 cycles of denaturation at 95°C for 30 sec, annealing at 60°C for 40 sec and extension at 72°C for 50 sec, 1 cycle of final extension at 72°C for 7 min. Purification of PCR product was carried out with Exonuclease I to remove the amplification primers and Thermosens Phosphatase alkaline (FastAp) to remove unincorporated dNTPs. Sanger sequencing was performed in both directions with the Big Dye® terminator ready reaction v1.1 kit (Applied Biosystems™) using the same PCR primers according to the following protocol: 1 cycle of initial denaturation at 96°C for 90 sec, 25 cycles of denaturation at 96°C for 10 sec and annealing at 55°C for 5 sec and a final extension at 60°C for 4 min. Sequence purification was performed with BigDye® Xterminator Purification kit (Applied Biosystems™) according to the manufacturer's instructions. Electropherograms quality analysis was performed on forward and reverse sequences by Sequencing analysis v5.4 software (Applied Biosystems™). Raw data (.ab1 format file) were analyzed by Lasergene software SeqMan module (DNAStar, Madison USA versions from 7 to 17).

Phylogenetic analysis

Sequences were univocally identified using the farm code, which is unique for every farm in the Country, and the sampling date. Incomplete sequences, with ambiguous nucleotide or those with missing metadata were excluded from the analysis. Obtained sequences were aligned using MAFFT algorithm implemented in Lasergene v17 (DNASTAR) and a Maximum Likelihood (ML) tree was built with iqtree2 [18]. In both cases, vaccine sequences were included. These strains were excluded from the dataset for the subsequent analysis. The presence of a temporal signal was initially assessed using TempEST v1.5.3. [19]. For phylodynamic analysis, the complete dataset was sub-sampled 10 times. Each subset contained a random 10% of the whole dataset and underwent the same analysis. Subsequently, each subset was aligned once more and analyzed with BEAST v1.10.4. Each analysis was run twice, and the resulting files were combined through the LogCombiner tool included in the BEAST package, removing 10% of each run as burn-in. The substitution model (GTR + G + I) was selected based on the BIC score, automatically calculated with iqtree2 before the computation of the phylogenetic tree. The relaxed lognormal molecular clock [20] and Bayesian Skyline [21] were selected, as clock and tree models, respectively. The Brownian random walk migration model was implemented. Lastly, Markov Chain Monte Carlo (MCMC) was set at 300 million, with a sample frequency of 30 thousand steps. The resulting log file was evaluated with Tracer 1.6. and maximum clade credibility (MCC) trees were obtained through the TreeAnnotator software included in the BEAST package. Visualization of viral population diffusion was performed with SPREAD3 software [22].

A total of 4609 ORF7 sequences out of 7491 (61.8%) were included in the final dataset, originating from 15 Italian Regions and 50 Provinces (Additiona File 1), between 2008 and 2022. Overall, 1062 farms were sampled at least once throughout the considered period. The ML tree showed that all sequences belong to subtype 1, as none clustered with Lena subtype 2 or 3 (data not shown). Preliminary analysis of the phylogenetic tree in TempEst showed the presence of a proper temporal signal consisting of an evolutionary rate of 2.69*10^-3 substitutions/site/year (data not shown).

In the phylodynamic analysis, the mean estimated evolutionary rate was comparable among all runs and ranged from 4.12x10^-3 (subset 6, 95% HPD Interval [3.5739e-3, 4.7131e-3]) to 5.76 x10^-3 (subset 5, 95% HPD Interval [5.0155E-3, 6.5472E-3]). Mean Time to the Most Recent Common Ancestor was less consistent throughout datasets and ranged from 1960.24 in run 7 (95% HPD Interval [1938.3982, 1980.286]) to 1990.18 (95% HPD Interval [1983.249, 1996.0503]) in subset 1. (Figs. 2 and 3).

Bayesian Skyline reconstruction showed a population increase between 2003 and 2010, with slight differences in every run but consistent nonetheless (Figure 4). A contraction was observed thereafter until around 2012. This was followed by a stabilization period marked by minor fluctuations in timing and magnitude, which varied depending on the dataset considered.

The spatial analysis performed with SPREAD3 placed the origin of PRRSV in the province of Parma, Emilia-Romagna region, with the exception of subset 7, which also showed the lowest evolutionary rate. From this area, across all runs, the common pattern was the spread northwards and southwards from their respective origin. Initially concentrating in Lombardy and Emilia Romagna regions for few years, as expected given the origin sequences included in the dataset, a subsequent radial expansion was observed, especially eastwards and westward (Figure 5).

In this study, we applied a phylodynamic approach to evaluate the evolutionary dynamics of PRRSV-1 in Italy from 2008 to 2022. Over 15 years, PRRSV population remained composed exclusively by subtype 1 strains. BEAST analysis revealed a substitution rate consistent to that already found for PRRSV [23] and for other positive-sense single-stranded RNA viruses as well [24]. As shown in the Skyline reconstruction (Fig. 3), in some cases the tMRCA was inferred in the early 90s, as commonly accepted, while in other cases, the root dated in the early 80s, well before the spread of PRRSV. This inconsistency reflects the in silico random sampling, suggesting a certain sampling effect, that might slightly bias the overall estimations. Considering that the oldest sequence obtained through our diagnostic activity is from January 2nd, 2008, it is plausible to suggest that the disease remained undetected or misdiagnosed for several years. When older sequences (from early 2000 to late 90s) could be analyzed, the tMRCA was inferred even before 1982, as demonstrated previously outside Italy [25]. Therefore, the estimated TMRCA might have been underestimated due to limited sample availability. The Bayesian Skyline reconstruction showed a peak around 2010, with several fluctuations observed in the subsequent years. This result is likely due to the transition from closed-farm to multi-site production system, that took place during the early 2000s in Italy. This transition significantly increased animal movements over longer distances, leading to a massive spread of the virus among different farms and/or among different production sites of the same farms. Then, improvements in farming practices and the implementation of biosecurity procedures may have reduced PRRSV circulation in Italy right after 2010. These measures, initially applied for the eradication of Aujesky disease and Swine Vesicular Disease (SVD) around 2010 and still in place, could have had positive, indirect effects in limiting PRRS diffusion also. From 2015 to nowadays, the viral population has shown a “roller-coaster” dynamic, emphasizing the difficulties in control and/or eradication of PRRS. This dynamic may be due to prolonged viremia, persistent infection in lymphoid tissue [26], and the seasonal introduction of new animals in farms to sustain the national pig meat demand [27]. In addition, PRRSV can spread through various routes in farms, not necessarly via animals. Non-porous common materials and feed ingredients have also been shown to transmit PRRSV, with higher transmission rates at lower temperatures [28]. It has been also demonstrated that certain PRRSV strains can be airborne transmitted for several kilometers [29], which is critical in high density farm areas like the Pianura Padana in our study. A further analysis in SPREAD3 reconstructed PRRSV movements within the Italian peninsula. The diffusion appears to have originated in north-central Italy, in mid-to-late century, specifically in the province of Parma in Emilia-Romagna, despite Lombardy being the Italian region with the highest number of animals and farm density. From this origin, after initially migrating northward for a few kilometers, Lombardy became the main geographic area from where the virus spread in multiple directions, including southern Italy, where pigs are less intensively raised. Indeed, it has been demonstrated that while larger farms, even if in low numbers, play a major potential role in the diffusion of infectious diseases in pigs, acting as super-spreaders also small facilities can be relevant in PRRSV epidemiology when featured by low biosecurity levels, as often occurs in rural southern settings [27, 30]. On a side note, Modified Live Vaccines could have played a minor but still significant role both in the initial spread of PRRSV and in the most recent years, since they were constantly introduced in Europe starting from early 2000s [14]. However, there are risks associated with these vaccines, such as reversion to virulence and recombination between vaccine strains, potentially leading to severe outcomes [31]. Phylodynamic and phylogeographic analysis play an important role in studying the epidemiology of diseases that are challenging to eradicate and/or manage once introduced into farms or groups of farms. These information can support animal health decisions to contain virus spread. Moreover, by combining genetic and geographic data, these approaches provide a dynamic view of viral movements, which is even more critical for zoonotic viruses. Finally, applying these approaches on a whole genome scale, combined with comprehensive metadata, complex statistical methods and computing capabilities, can increase exponentially their potential to provide accurate viral population dynamics estimations.

AUTHOR CONTRIBUTIONS

Conceived and designed the experiments: G. P., T. S., I. B., G. L. A., M. B. B.. Performed laboratory experiments: V. C., S. M., D. C. Analyzed the data: G. P., V. C., S. M., D. C., G. F.. Writing—original draft preparation: G. P., G. F., G. V., M. B. B.. Writing—review and editing: G. P., S. F., G. F., T. S., I. B., M. B. B.. Supervision: I. B., G. L. A., M.B.B.. All authors read and approved the final manuscript.

ETHICS STATEMENT

Ethical approval was not required for this work, as samples were part of routine disease surveillance activities carried out by Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna (IZSLER). All the activities were performed in compliance with state and local regulations.

CONSENT FOR PUBLICATION

Not applicable.

DATA AVAILABILITY

The dataset supporting the conclusions of this article has been deposited in the NCBI GenBank archive with the temporary submission ID 2855995

COMPETING INTERESTS

The authors declare that they have not competing interests.

FUNDING

This research received no external funding.

ACKNOWLEDGEMENTS

We thank Matteo Tonni, Cristian Salogni, Alberto Tiraboschi and Gloria Garbin for their help with the project activities.

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No competing interests reported.

Additionalfile1.docx
The Additional File (.docx) contains: Suppl. Table 1: Origin of dataset sequences. ORF7 sequences included in the overall dataset divide by regions and provinces within the regions.

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You are reading this latest preprint version

Evolutionary Dynamics of Prrs Virus in Italian Pig Farms: a Retrospective Study

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Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Samples collection and molecular screening

ORF7 sequencing

Phylogenetic analysis

RESULTS

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1