Characterization of Puumala hantavirus in bank voles from two regions in the Netherlands where human cases occurred

Vries, Ankje de; Vennema, Harry; Bekker, Dick; Maas, Miriam; Adema, J.P.H.M.; Opsteegh, Marieke; Giessen, J.W.B. van der; Reusken, Chantal

doi:10.1099/jgv.0.000481

Cited by 8 publications

(14 citation statements)

References 27 publications

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“…Details of the procedure are supplied in [12]. By dividing the number of positive voles by the total number of voles tested, we obtained the prevalence of PUUV infection.…”

Section: Methodsmentioning

confidence: 99%

Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands

Swart

Bekker²,

Maas

et al. 2017

Infection Ecology & Epidemiology

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This paper deals with modelling the relationship between human Puumala hantavirus (PUUV) infection, the abundance and prevalence of infection of the host (the bank vole), mast, and temperature. These data were used to build and parametrise generalised regression models, and parametrise them using datasets on these factors pertaining to the Netherlands. The performance of the models was assessed by considering their predictive power. Models including mast and monthly temperature performed well, and showed that mast intensity influences vole abundance and hence human exposure for the following year. Thus, the model can aid in forecasting of human illness cases, since (1) mast intensity influences the vole abundance and hence human exposure for the following year and (2) monitoring of mast is much more feasible than determining bank vole abundance.

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“…Details of the procedure are supplied in [12]. By dividing the number of positive voles by the total number of voles tested, we obtained the prevalence of PUUV infection.…”

Section: Methodsmentioning

confidence: 99%

Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands

Swart

Bekker²,

Maas

et al. 2017

Infection Ecology & Epidemiology

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“…Collectively, the genome (S, M, and L segment) nucleotide sequence diversity of PUUV between different genetic lineages is in the range of 11–19% [ 22 ]. This diversity suggests that PUUV could be the most variable virus within the orthohantavirus genus [ 123 ], although this could be resultant of sampling bias.…”

Section: Genetic Diversity Of Orthohantavirusesmentioning

confidence: 99%

“…Mutations can generate many distinct PUUV genome variants and lead to the formation of new strains. Currently, eight PUUV genetic lineages are identified in the bank voles: Central European (CE), Alpe-Adrian (ALAD), Danish (DAN), South Scandinavian (S-SCAN), North Scandinavian (N-SCAN), Finnish (FIN), Russian (RUS) and the newly identified Latvian (LAT) [18][19][20][21][22][23] (Table 1). It was demonstrated that the nucleotide genetic diversity between PUUV lineages for the S (small) segment is 15-19% [24,25], while the differences in nucleotide sequences between PUUV strains within the same lineage could range from 0 to 9% [20,26].…”

Section: Introductionmentioning

confidence: 99%

“…It was demonstrated that the nucleotide genetic diversity between PUUV lineages for the S (small) segment is 15-19% [24,25], while the differences in nucleotide sequences between PUUV strains within the same lineage could range from 0 to 9% [20,26]. [18,22] The PUUV genetic diversity results from both the accumulation of the point mutations and rearrangement within the viral genome, such as recombination and reassortment [23,30]. While the molecular evolution of orthohantaviruses can lead to the emergence of strains that are more adapted to the rodent host, it is believed that the strain's genome variations could lead to the substantial changes in the virus pathogenicity in humans [30].…”

Section: Introductionmentioning

confidence: 99%

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Orthohantaviruses, Emerging Zoonotic Pathogens

et al. 2020

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Orthohantaviruses give rise to the emerging infections such as of hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) in Eurasia and the Americas, respectively. In this review we will provide a comprehensive analysis of orthohantaviruses distribution and circulation in Eurasia and address the genetic diversity and evolution of Puumala orthohantavirus (PUUV), which causes HFRS in this region. Current data indicate that the geographical location and migration of the natural hosts can lead to the orthohantaviruses genetic diversity as the rodents adapt to the new environmental conditions. The data shows that a high level of diversity characterizes the genome of orthohantaviruses, and the PUUV genome is the most divergent. The reasons for the high genome diversity are mainly caused by point mutations and reassortment, which occur in the genome segments. However, it still remains unclear whether this diversity is linked to the disease’s severity. We anticipate that the information provided in this review will be useful for optimizing and developing preventive strategies of HFRS, an emerging zoonosis with potentially very high mortality rates.

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“…We also analyzed the nucleic acid extractions for the presence of four viruses: tick-borne encephalitis virus, Tula hantavirus, Puumala hantavirus, and Eyach virus. For the first three, we performed reverse transcription real-time PCRs as previously described [31][32][33][34]. Positive (plasmid) controls and negative (water) controls were used on every plate.…”

Section: Screening For Microparasitesmentioning

confidence: 99%

Parasite Load and Site-Specific Parasite Pressure as Determinants of Immune Indices in Two Sympatric Rodent Species

Hofmeester

Bügel

Hendrikx

et al. 2019

Animals

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Wildlife is exposed to parasites from the environment. This parasite pressure, which differs among areas, likely shapes the immunological strategies of animals. Individuals differ in the number of parasites they encounter and host, and this parasite load also influences the immune system. The relative impact of parasite pressure vs. parasite load on different host species, particularly those implicated as important reservoirs of zoonotic pathogens, is poorly understood. We captured bank voles (Myodes glareolus) and wood mice (Apodemus sylvaticus) at four sites in the Netherlands. We sampled sub-adult males to quantify their immune function, infestation load for ecto- and gastrointestinal parasites, and infection status for vector-borne microparasites. We then used regression trees to test if variation in immune indices could be explained by among-site differences (parasite pressure), among-individual differences in infestation intensity and infection status (parasite load), or other intrinsic factors. Regression trees revealed splits among sites for haptoglobin, hemagglutination, and body-mass corrected spleen size. We also found splits based on infection/infestation for haptoglobin, hemolysis, and neutrophil to lymphocyte ratio. Furthermore, we found a split between species for hemolysis and splits based on body mass for haptoglobin, hemagglutination, hematocrit, and body-mass corrected spleen size. Our results suggest that both parasite pressure and parasite load influence the immune system of wild rodents. Additional studies linking disease ecology and ecological immunology are needed to understand better the complexities of host–parasite interactions and how these interactions shape zoonotic disease risk.

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Characterization of Puumala hantavirus in bank voles from two regions in the Netherlands where human cases occurred

Cited by 8 publications

References 27 publications

Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands

Modelling human Puumala hantavirus infection in relation to bank vole abundance and masting intensity in the Netherlands

Orthohantaviruses, Emerging Zoonotic Pathogens

Parasite Load and Site-Specific Parasite Pressure as Determinants of Immune Indices in Two Sympatric Rodent Species

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