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. 2019 Apr 25;9(1):6575.
doi: 10.1038/s41598-019-42978-1.

Environmental DNA metabarcoding to detect pathogenic Leptospira and associated organisms in leptospirosis-endemic areas of Japan

Affiliations

Environmental DNA metabarcoding to detect pathogenic Leptospira and associated organisms in leptospirosis-endemic areas of Japan

Yukuto Sato et al. Sci Rep. .

Abstract

Leptospires, which cause the zoonotic disease leptospirosis, persist in soil and aqueous environments. Several factors, including rainfall, the presence of reservoir animals, and various abiotic and biotic components interact to influence leptospiral survival, persistence, and pathogenicity in the environment. However, how these factors modulate the risk of infection is poorly understood. Here we developed an approach using environmental DNA (eDNA) metabarcoding for detecting the microbiome, vertebrates, and pathogenic Leptospira in aquatic samples. Specifically, we combined 4 sets of primers to generate PCR products for high-throughput sequencing of multiple amplicons through next-generation sequencing. Using our method to analyze the eDNA of leptospirosis-endemic areas in northern Okinawa, Japan, we found that the microbiota in each river shifted over time. Operating taxonomic units corresponding to pathogenic L. alstonii, L. kmetyi, and L. interrogans were detected in association with 12 nonpathogenic bacterial species. In addition, the frequencies of 11 of these species correlated with the amount of rainfall. Furthermore, 10 vertebrate species, including Sus scrofa, Pteropus dasymallus, and Cynops ensicauda, showed high correlation with leptospiral eDNA detection. Our eDNA metabarcoding method is a powerful tool for understanding the environmental phase of Leptospira and predicting human infection risk.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Sampling locations of river water. O1, Okuma-1 sampling site; and G1, G2, and G3, Genka-1, -2, and -3 sampling sites, respectively. Satellite imagery were obtained from Google Maps (https://www.google.com/maps/); data providers of the satellite imagery are Google, Data SIO, NOAA, U.S. Navy, NGA, GEBCO, Landsat/Copernicus, Data LDEO-Columbia, and NSF. Adobe Illustrator CS6 was used to create the map with satellite imagery.
Figure 2
Figure 2
Schematic view of the procedure of library preparation for metabarcoding sequencing based on a two-step tailed PCR. Multiplex PCR was applied in the first step reaction for Leptospira and bacterial detection. The procedure for vertebrate mitochondrial 12S rRNA sequencing was basically the same but slightly modified from that of Miya et al..
Figure 3
Figure 3
Environmental detection of pathogenic leptospiral 16S rRNA gene. Number of sequence reads detected in each sample are shown with colored matrices in pink shading. Pink bars indicate the total number of Leptospira reads per sample summed across species. Blue bars show the amount of rainfall (mm) on the sampling day (left column) and that comprising two days before sampling, the day before the sampling, and the day of sampling (right column). O1, G1, G2, and G3 indicates sampling locations Okuma-1, Genka-1, -2, and -3, respectively. G-11–G-50 and O-11–O-50 denote sample names.
Figure 4
Figure 4
Correlation of bacterial 16S rRNA gene with rainfall and Leptospira. Histograms show distributions of Pearson’s correlation coefficient scores between the detected read numbers of 355 bacterial species and (A) rainfall amount on sampling day (mm) and (B) detected read number of leptospiral 16S rRNA gene. (C) Class, family, species, and operational taxonomic unit (OTU) numbers from GreenGenes database of 18 bacterial species that show significant correlation r (indicated by gray shading) with both or either rainfall amount and Leptospira detection (P < 0.05 and Benjamini–Hochberg-corrected false discovery rate < 0.05). The rightmost column indicates the partial correlations between each bacterium and Leptospira detection when controlled for rainfall; asterisks and gray shading denote correlations that remain significant at a level of P < 0.05.
Figure 5
Figure 5
Neighbor-joining clustering according to a series of correlation coefficients between Leptospira and vertebrate eDNA detection. The clustering tree was generated based on Euclidean distances among a series of Pearson’s correlation coefficients between detected read numbers of Leptospira (results from 16S rRNA and lipL32 genes) and those of the vertebrate mitochondrial 12S rRNA gene (results from KAPA-HiFi and PrimeStar-HS polymerases) or rainfall amount (mm; Supplementary Table S3). The black bar indicates 10 vertebrate species that showed significant correlation with leptospiral 16S rRNA gene detection (P < 0.05 and Benjamini–Hochberg-corrected false discovery rate < 0.05). Numbers in parentheses above scientific names denote the number of appearances of the species (maximum, 16 [i.e., 2 rivers multiplied by 4 months multiplied by 2 PCR-enzyme results]). Numbers on the tree indicate support values for the nodes estimated from 100 bootstrap replications.

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