MBPD: A multiple bacterial pathogen detection pipeline for One Health practices

Abstract

Bacterial pathogens are one of the major threats to biosafety and environmental health, and advanced assessment is a prerequisite to combating bacterial pathogens. Currently, 16S rRNA gene sequencing is efficient in the open-view detection of bacterial pathogens. However, the taxonomic resolution and applicability of this method are limited by the domain-specific pathogen database, taxonomic profiling method, and sequencing target of 16S variable regions. Here, we present a pipeline of multiple bacterial pathogen detection (MBPD) to identify the animal, plant, and zoonotic pathogens. MBPD is based on a large, curated database of the full-length 16S genes of 1986 reported bacterial pathogen species covering 72,685 sequences. In silico comparison allowed MBPD to provide the appropriate similarity threshold for both full-length and variable-region sequencing platforms, while the subregion of V3-V4 (mean: 88.37%, accuracy rate compared to V1-V9) outperformed other variable regions in pathogen identification compared to full-length sequencing. Benchmarking on real data sets suggested the superiority of MBPD in a broader range of pathogen detections compared with other methods, including 16SPIP and MIP. Beyond detecting the known causal agent of animal, human, and plant diseases, MBPD is capable of identifying cocontaminating pathogens from biological and environmental samples. Overall, we provide a MBPD pipeline for agricultural, veterinary, medical, and environmental monitoring to achieve One Health.

Keywords: 16S rRNA gene sequencing; One Health; bacterial pathogen detection.

Figure 1

MBPD workflow for open‐view bacterial pathogen detection based on 16S rRNA‐encoding gene sequencing. Bacterial pathogens causing animal, plant, and zoonotic diseases were first collected and curated from publicly available literature, databases, and web resources to construct the multiple bacterial pathogen database (MBPD) with a total of 72,685 full‐length sequences of 16S. Then, the accuracy and appropriate threshold of each variable region of 16S were evaluated through an in silico experiment. The clean data from 16S sequencing of bio/eco samples were subjected to the DADA2 pipeline using the MBPD database to obtain the amplicon sequence variant (ASV) sequences. The feature table of the ASV sequences was assigned using the UCLUST algorithm with an in silico experiment to optimize the cutoffs of similarity for the different variable regions of 16S. MBPD, multiple bacterial pathogen detection.

Figure 2

The curated bacterial pathogen database of MBPD. (A) Phylogenetic tree based on the 16S rRNA gene sequences of 1986 reference pathogens. Colors of branches represent the corresponding phyla, and the outer ring denotes animal, plant, and zoonotic types of pathogens. (B) Basic taxonomic composition of the MBPD database at the phylum level. Only the top 10 phyla are shown in the figure, and the remaining phyla are merged as others. (C) Basic taxonomic composition of the MBPD database at the genus level. Only the top 10 genera are shown in the figure.

Figure 3

In silico comparison of 16S rRNA variable regions. (A) Common sequencing target of variable regions in Illumina and full‐length sequencing. The 16S full‐length database of MBPD was trimmed and generated in silico amplicons for different subregions based on the location of PCR primers commonly used in microbiome studies. Then, 10,000 sequences were extracted and repeated 30 times for pathogen alignment. (B) Missing rate of pathogens across bacterial genera under in silico experiments. Various facets denote the sequencing target of 16S variable regions (V). Colors denote the missing pathogen at the phylum level. C‐Phytoplasma and BCP refer to Candidatus Phytoplasma and Burkholderia‐Caballeronia‐Paraburkholderia, respectively. (C) Accuracy rate of taxonomy assignment for in silico amplicons of the MBPD database with varying similarity thresholds (80%, 90%, 95%, 97%, and 99%) for different variable regions of 16S. (D) Runtime of taxonomy assignment for in silico amplicons with varying similarity thresholds for different variable regions of 16S. (E) Accuracy rate of the subregion in pathogen detection compared to the V1−V9 region. Different lowercase letters indicate significant differences in disease incidence across varieties (HSD post hoc test: p < 0.05). HSD, honest significant difference.

Figure 4

Performance of MBPD, 16SPIP, and MIP in bacterial pathogen detection. (A) Venn diagram displaying the shared and specific species of pathogens detected by MBPD, 16SPIP, and MIP. (B) Comparison of runtime among MBPD, MIP, and 16SPIP. Runtime in min. The colors green, red, and brown denote MIP, MBPD, and 16SPIP, respectively.

Figure 5

Pathogen detection in the healthy and diseased samples. Differences in the relative abundance of causal agents of diseases in human, animal, and plant rhizosphere samples (A). Relative abundances of other potential genera were enriched in the diseased group between human (B), animal (C), and plant rhizosphere (D) samples. Pairwise Student's t‐test was used for statistical analyses (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).