Hantavirus

One to three sequences of the entire open reading frame (ORF) were randomly chosen from each hantavirus species for phylogenetic analysis. The RDP3 program [53] (link) was used to examine potential intra-segment recombination in the viral sequences, although no recombinant sequences were identified (although we do find evidence for segment reassortment – see below). Identical sequences were excluded from this study.
Both animal mt-cyt b gene and viral genome sequences were aligned using the ClustalW method implemented in the Lasergene program, version 5 (DNASTAR, Inc., Madison, WI). Poorly aligned positions and divergent regions of the alignment, and which could negatively affect phylogenetic analysis, were removed using Gblocks [54] (link). The following data set sizes were used in the final analysis: hantavirus S segment = 103 sequences, 1201 bp; M segment = 71 sequences, 3024 bp; L segment = 30 sequences, 6519 bp, partial L segment = 32, 330 bp; mt-cyt b gene = 97 sequences, 1140 bp.

To compare RT-qPCR efficiency in different formats, a standard curve was constructed using either in vitro transcribed RNA or total RNA extracted from pool of serum/blood clinical samples from patients known to be positive for CASV, RIOMV, LNV and ANAJV hantavirus infection by RT-PCR and sequencing. Seven 1:10 dilutions of in vitro RNA were used for RT-qPCR amplification in two different formats: singleplex and duplex with MS2 EIC. In this study, we defined the LoD as the lowest amount of genome copies in a reaction that can be detected by the assays with 95% probability at a confidence interval (CI) of 95%. We also used endpoint sensitivity do determine LoD. Endpoint sensitivity is determined by the lowest amount of genome copies in a reaction where the target is not detected by the assay. First, we analyzed the LoD by defining the endpoint sensitivity of RT-qPCR and Semi-nested RT-PCR. For that, we tested 12 dilutions of viral RNA extracted from a pool made of samples from all four hantavirus strains previously quantified by RT-qPCR (genome copies ranging from 10², 10¹, 1 and 0.9 to 0.1 copies/μl in six replicates each). The LoD was thus defined as the lowest quantity that yield positive results in all six replicates. Next, we determined the 95% LoD for each strain individually. We used hantavirus RNA previously isolated from sylvatic rodent samples that were submitted for nucleotide sequencing. First, we quantified each stock RNA by RT-qPCR. Then we performed a serial 5-fold dilution starting at 100 copies/reaction down to 0.0062 copies/reaction per dilution for each strain. Each dilution series was tested six times in a single run and the results used to calculate the 95% LOD of the assay by probit regression analysis, using SPSS Statistics version 25 (IBM, USA). We then calculated the analytical sensitivity defined as amount of genome copies per reaction detected 95% of the time. In addition, we determined the LoD of RT-qPCR with Semi-nested RT-PCR by testing 12 dilutions of viral RNA quantified by RT-qPCR ranging from 10², 10¹, 1 and 0.9 to 0.1 copies/μl.

In addition to the patient’s first pet rat (KS19/1354) [23 (link)], three more pet rats from pet-rat owner 1 and three from pet-rat owner 2 were collected, euthanized, and sent to the National Reference Laboratory for Hantaviruses at the Friedrich-Loeffler-Institut (FLI), Greifswald-Insel Riems, Germany. In addition, we included materials from earlier studies comprising samples of rats from breeding facilities and wild populations across Germany (2007 to 2019 [24 (link),25 (link)]), from wild populations from rural and urban areas in the Netherlands (2013–2021), as well as from ongoing studies of commensal rodent pathogens in Germany (2020; see Figure 1 and Supplementary Table S1). The rats from breeding facilities in Germany comprised laboratory Norway rats, wild-trapped Norway and black rats, as well as feeder rats from a zoo. Rats captured in 2013–2014 in the Netherlands were live-trapped with methods described previously [26 (link)]; other rats were captured using snap-traps. All carcasses were stored frozen at −20 °C until dissection.

All statistical analyses were weighted. The weights used considered the sampling weight and were adjusted for non-response. Non-response was corrected by reweighting using the equal-quantile score method [26 ], based on socio-demographic and geographic data and the professional activity sector available in the sampling frame for all individuals. A calibration by raking ratio method was then applied, using the distributions by sex, age group, and professional activity sector in the target population, using the SAS macro Calmar [27 (link)].
We described population demographic characteristics with absolute and relative frequencies for categorical variables and with mean and standard deviation for continuous variables. We explored factors associated with hantavirus seropositivity by estimating seroprevalence ratios and 95% confidence intervals (CI) using weighted Poisson regression models with robust standard errors. The clustering of observations by geographic area was accounted for using a fixed area effect in all models. The outcome of interest was hantavirus seropositivity, and we used one model per factor of interest (the main exposure variable). Causal diagrams were made between hantavirus seropositivity and each factor of interest using DAGitty ([28 ,29 (link)]; diagrams not shown). Based on those causal diagrams, it was deemed reasonable to use unconditional models for the geographic area, age, and main profession (i.e., no important confounders were identified for those factors). Also, we identified age as a confounder of the association between seniority and hantavirus seropositivity; and main profession as a confounder of the association between average weekly exposure time in forest and hantavirus seropositivity. Nevertheless, the strong collinearity between age and seniority (Pearson correlation coefficient of 0.76), precluded a multivariable analysis testing both factors. Analyses were performed using Stata 14.