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  • Title: Analysis of environmental dust in goat and sheep farms to assess Coxiella burnetii infection in a Q fever endemic area: Geographical distribution, relationship with human cases and genotypes.
    Author: Zendoia II, Barandika JF, Hurtado A, López CM, Alonso E, Beraza X, Ocabo B, García-Pérez AL.
    Journal: Zoonoses Public Health; 2021 Sep; 68(6):666-676. PubMed ID: 34240552.
    Abstract:
    Real-time PCR analysis of environmental samples (dust and aerosols) is an easy tool to investigate the presence of Coxiella burnetii in the farm environment. The aim of this study was to assess the distribution of C. burnetii DNA in dust collected inside animal premises from 272 small ruminant farms in Bizkaia (northern Spain), a region with recent reports of human Q fever cases and outbreaks. Within each farm, 5 samples of dust were collected from difference surfaces, and data on animal census, management procedures, characteristics of the premises and geographic location were collected. Real-time PCR analysis of the dust samples detected presence of C. burnetii DNA in 98 farms (36.0%), flock-prevalence being higher in sheep (38.9%) or mixed ovine-caprine production systems (36.8%), compared to goats (25.0%). Larger bacterial burdens were observed in mixed farms, compared to sheep (p < .05). Single nucleotide polymorphism (SNP) analysis identified 5 different genotypes, with SNP8 being the predominant genotype (73%), followed by SNP6 (11%), SNP2 (9%), SNP4 (5%) and SNP1 (2%). Proportion of farms where C. burnetii DNA was detected differed among the different agricultural counties, and a higher proportion of C. burnetii DNA positive farms was associated with the occurrence of recent human Q fever outbreaks at several geographical locations. Dust sampling in domestic ruminant farms coupled with real-time PCR to screen for the presence of C. burnetii and estimate bacterial load can be a useful tool to identify herds and regions with high prevalence, define priority actions and monitor the effect of control measures. If combined with molecular genotyping and spatial distribution maps, it can help to identify farm contamination sources and trace the origin of human outbreaks.
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