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Various animal species have been evaluated in depth for their potential as Tick-borne encephalitis virus (TBEV) sentinel species, although evidence for equine capacity is incomplete. Therefore, a comprehensive cross-sectional stratified serosurvey and PCR analysis of selected horses (n = 301) were performed in TBEV endemic localities in Lithuania. Attached and moving ticks (n = 241) have been collected from aforementioned hosts to evaluate natural infectivity of TBEV vectors (Ixodes spp.) in the recreational environments surrounding equestrian centers. All samples were screened for TBEV IgG and positive samples were confirmed by virus neutralization test (VNT). 113 (37.5%) horses from all counties of Lithuania tested positive for TBEV IgG, revealing age and sex indifferent results of equine seroprevalence that were significantly dependent on pedigree: horses of mixed breed were more susceptible to infection possibly due to their management practices. TBEV prevalence in equine species corresponded to TBEV-confirmed human cases in the precedent year. As much as 3.9% of horses were viraemic with TBEV-RNA with subsequent confirmation of TBEV European subtype. 4/38 of tested tick pools were positive for TBEV-RNA (Minimal infectious rate 1.2%). Several unknown microfoci were revealed during the study indicating areas of extreme risk close to popular human entertainment sites. The study provides important evidence in favor of horses&rsquo, usage as sentinel species, as equines could provide more detailed epidemiological mapping of TBEV, as well as more efficient collection of ticks for surveillance studies.

The first cases of African swine fever (ASF) were detected in the Lithuanian wild boar population in 2014. Since then, the disease spread slowly through the whole country, affecting both, wild boar and domestic pigs. In the other Baltic states, which both are also affected by ASF since 2014, the recent course of ASF prevalence suggests that the countries might be well under way of disease elimination. In contrast, in Lithuania the epidemic seems to be still in full progress. In the present study, we aimed to extend a previous prevalence study in Lithuania. Looking at ASF virus (ASFV) and seroprevalence estimates of wild boar in all months of 2018 and in all affected municipalities in Lithuania, the course of ASF was evaluated on a temporal and spatial scale. A non-spatial beta-binomial model was used to correct for under- or overestimation of the average prevalence estimates. Within 2018 no big differences between the prevalence estimates were seen over time. Despite of the lower sample size, highest ASFV prevalence estimates were found in dead wild boar, suggesting higher detection rates through passive surveillance than through active surveillance. Accordingly, with the maximum prevalence of 87.5% in May 2018, the ASFV prevalence estimates were very high in wild boar found dead. The number of samples originating from hunted animals (active surveillance) predominated clearly. However, the ASFV prevalence in those animals was lower with a maximum value of 2.1%, emphasizing the high value of passive surveillance. A slight increase of the seroprevalence in hunted wild boar could be seen over time. In the center of Lithuania, a cluster of municipalities with high ASFV and seroprevalence estimates was found. The results of the study indicate that ASFV is still circulating within the Lithuanian wild boar population, constituting a permanent risk of disease transmission into domestic pig holdings. However, additional, more recent data analyses are necessary to re-evaluate the course of ASF in Lithuania and thus, to be able to make a statement about the stage of the ASF epidemic in the country. This is of huge importance for Lithuania for evaluating control measures and their efficacy, but also for neighbouring countries to assess the risk of disease spread from Lithuania.

Additional file1. Table S1: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a seropositive test results for ASF and a negative PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each month of 2018. Table S2: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a seropositive test results for ASF and a positive PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each month of 2018. Table S3: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a positive PCR but a negative ELISA test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each month of 2018. Table S4: Number of wild boar samples, which were investigated only by PCR to detect ASF virus genome. Number of samples from passive surveillance, which resulted in a positive PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each month of 2018. Table S5: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a seropositive test results for ASF and a negative PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each ASF-affected municipality of Lithuania. Table S6: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a seropositive test results for ASF and a positive PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each ASF-affected municipality of Lithuania. Table S7: Number of wild boar samples, which were investigated by ELISA to detected ASF specific antibodies and by PCR to detect ASF virus genome. Number of samples from active surveillance, which resulted in a positive PCR but a negative ELISA test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each ASF-affected municipality of Lithuania. Table S8: Number of wild boar samples, which were investigated only by PCR to detect ASF virus genome. Number of samples from passive surveillance, which resulted in a positive PCR test result and the estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) including the 95% confidence intervals for each ASF-affected municipality of Lithuania. Figure S1: Estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) of hunted wild boar showing an ELISA and a PCR-positive test result for each month of 2018. The whiskers indicate 95% confidence intervals. Figure S2: Estimated raw and corrected prevalence (calculated using a non-spatial beta-binomial model) of hunted wild boar showing an ELISA-negative but a PCR-positive test result for each month of 2018. The whiskers indicate 95% confidence intervals. Figure S3: Estimated raw and corrected prevalence with 95% confidence intervals (calculated using a non-spatial beta-binomial model) of hunted wild boar showing a sero- and PCR-positive ASF sample result for each ASF-affected municipality of Lithuania. Figure S4: Estimated raw and corrected prevalence with 95% confidence intervals (calculated using a non-spatial beta-binomial model) of hunted wild boar showing a PCR-positive and a seronegative ASF sample result for each ASF-affected municipality of Lithuania. Figure S5: Estimated raw and corrected prevalence with 95% confidence intervals (calculated using a non-spatial beta-binomial model) of wild boar found dead showing a PCR-positive ASF sample result for each ASF-affected municipality of Lithuania.

Background Hepatitis E virus (HEV) is one of the major causes of acute viral hepatitis worldwide. In Europe, food-borne zoonotic transmission of HEV genotype 3 has been associated with domestic pigs and wild boar. Controversial data are available on the circulation of the virus in animals that are used for human consumption, and to date, no gold standard has yet been defined for the diagnosis of HEV-associated hepatitis. To investigate the current HEV infection status in Lithuanian pigs and wild ungulates, the presence of viral RNA was analyzed by nested reverse transcription polymerase chain reaction (RT-nPCR) in randomly selected samples, and the viral RNA was subsequently genotyped. Results In total, 32.98 and 22.55% of the domestic pig samples were HEV-positive using RT-nPCR targeting the ORF1 and ORF2 fragments, respectively. Among ungulates, 25.94% of the wild boar samples, 22.58% of the roe deer samples, 6.67% of the red deer samples and 7.69% of the moose samples were positive for HEV RNA using primers targeting the ORF1 fragment. Using primers targeting the ORF2 fragment of the HEV genome, viral RNA was only detected in 17.03% of the wild boar samples and 12.90% of the roe deer samples. Phylogenetic analysis based on a 348-nucleotide-long region of the HEV ORF2 showed that all obtained sequences detected in Lithuanian domestic pigs and wildlife belonged to genotype 3. In this study, the sequences identified from pigs, wild boars and roe deer clustered within the 3i subtype reference sequences from the GenBank database. The sequences obtained from pig farms located in two different counties of Lithuania were of the HEV 3f subtype. The wild boar sequences clustered within subtypes 3i and 3h, clearly indicating that wild boars can harbor additional subtypes of HEV. For the first time, the ORF2 nucleotide sequences obtained from roe deer proved that HEV subtype 3i can be found in a novel host. Conclusion The results of the viral prevalence and phylogenetic analyses clearly demonstrated viral infection in Lithuanian pigs and wild ungulates, thus highlighting a significant concern for zoonotic virus transmission through both the food chain and direct contact with animals. Unexpected HEV genotype 3 subtype diversity in Lithuania and neighboring countries revealed that further studies are necessary to understand the mode of HEV transmission between animals and humans in the Baltic States region.

Hepatitis E is an important public health concern disease that causes acute hepatitis in humans with different hepatitis E virus strains reported among domestic and wild animals. In Lithuania, seroprevalence studies on animals considered to be possible virus reservoirs, particularly in wildlife species, had never been investigated before. In order to assess the actual distribution of the virus in the domestic pig and wildlife species, serum samples originating from different geographic areas of Lithuania were tested for the presence of antibodies to the virus using enzyme-linked immunosorbent assay. The study results proved that hepatitis E virus infection is prevalent among domestic pigs, wild boar, moose, and roe deer in Lithuania, with the overall prevalence of IgG antibodies against hepatitis E virus being 43.75%, 57.05%, 11.76%, and 1.20%, respectively. Significantly higher (P< 0.01) seroprevalence values of 53.66% and 80% were observed in weaned pigs and adult wild boar. Herds with 15 001–30 000 pigs were 2.4 times more likely (P< 0.01) to test positive for hepatitis E virus antibodies (70.18%), than herds with ≤ 500 pigs (21.11%). The differences in seroprevalence rates between domestic pigs (from 25 to 66.67%) and wild boar (from 42.86 to 73.33% ) in all 10 counties were not significant (P> 0.05). Estimated low seroprevalence rate in moose, roe deer and absence of antibodies in red deer suggest that such wildlife species are accidental hosts of hepatitis E virus or at least hepatitis E virus is not present in Lithuanian cervides.

Hepatitis E virus (HEV) is one of the major causes of acute viral hepatitis worldwide. In Europe, food-borne zoonotic transmission of HEV genotype 3 has been associated with domestic pigs and wild boar. Controversial data are available on the circulation of the virus in animals that are used for human consumption, and to date, no gold standard has yet been defined for the diagnosis of HEV-associated hepatitis. To investigate the current HEV infection status in Lithuanian pigs and wild ungulates, the presence of viral RNA was analyzed by nested reverse transcription polymerase chain reaction (RT-nPCR) in randomly selected samples, and the viral RNA was subsequently genotyped.In total, 32.98 and 22.55% of the domestic pig samples were HEV-positive using RT-nPCR targeting the ORF1 and ORF2 fragments, respectively. Among ungulates, 25.94% of the wild boar samples, 22.58% of the roe deer samples, 6.67% of the red deer samples and 7.69% of the moose samples were positive for HEV RNA using primers targeting the ORF1 fragment. Using primers targeting the ORF2 fragment of the HEV genome, viral RNA was only detected in 17.03% of the wild boar samples and 12.90% of the roe deer samples. Phylogenetic analysis based on a 348-nucleotide-long region of the HEV ORF2 showed that all obtained sequences detected in Lithuanian domestic pigs and wildlife belonged to genotype 3. In this study, the sequences identified from pigs, wild boars and roe deer clustered within the 3i subtype reference sequences from the GenBank database. The sequences obtained from pig farms located in two different counties of Lithuania were of the HEV 3f subtype. The wild boar sequences clustered within subtypes 3i and 3h, clearly indicating that wild boars can harbor additional subtypes of HEV. For the first time, the ORF2 nucleotide sequences obtained from roe deer proved that HEV subtype 3i can be found in a novel host.The results of the viral prevalence and phylogenetic analyses clearly demonstrated viral infection in Lithuanian pigs and wild ungulates, thus highlighting a significant concern for zoonotic virus transmission through both the food chain and direct contact with animals. Unexpected HEV genotype 3 subtype diversity in Lithuania and neighboring countries revealed that further studies are necessary to understand the mode of HEV transmission between animals and humans in the Baltic States region.

Additional file 1. Primers and probe used for the conventional and real-time PCRs to detect PRRSV [10, 26, 30].

Background Porcine reproductive and respiratory syndrome virus (PRRSV) is recognized worldwide as an important and economically devastating pathogen in pig production. Although PRRSV is widespread in domestic swine, there is a lack of information regarding PRRSV infection in European wild boars (Sus scrofa). Currently available information does not provide conclusive evidence that wild boars are a reservoir of PRRSV. Nevertheless, wild boars may be likely to become infected by domestic swine through occasional direct or indirect contact. Furthermore, wild boars can act as a reservoir for infectious diseases of domestic pigs. Therefore, the objectives of the present study were to determine the virus prevalence and further explore the epidemiology and diversity of PRRSV strains present in Lithuanian wild boars over a 5-year period. A total of 1597 tissue and serum samples from wild boars inhabiting 44 districts and ten counties in Lithuania were analysed using conventional nested reverse transcription polymerase chain reaction (RT-PCR) and real-time Taqman RT-PCR for the detection of PRRSV-specific open reading frame (ORF) 1 and 6 sequences. Results PRRSV was highly prevalent in Lithuanian wild boar populations, with an average rate of 18.66 % using conventional RT-PCR and 19.54 % using real-time RT-PCR. PRRSV was detected in 36.71 and 41.77 % of 237 hunting grounds tested by conventional RT-nPCR and real-time RT-PCR, respectively. No statistically significant differences in PRRSV prevalence were observed by geographic area in the ten Lithuanian counties. Animals infected with PRRSV were identified in all age groups; however, significantly higher prevalence rates were identified in subadult and adult wild boars than in juveniles up to 12 months old. No positive results were obtained using conventional PCR with Type 2 specific primers. Phylogenetic analysis of the partial ORF5 region revealed that ten wild boars harboured virus sequences belonging to genetic subtypes 3 and 4 and may therefore pose a serious threat to Lithuanian pig farms in which only subtype two strains are circulating. Conclusions The results of virus prevalence and phylogenetic analyses strongly support the role of wild boars as a possible natural reservoir for PRRSV in Lithuania. Electronic supplementary material The online version of this article (doi:10.1186/s13028-016-0232-5) contains supplementary material, which is available to authorized users.