Dataset Information

Microbial community dissimilarity for source tracking with application in forensic studies.

ABSTRACT: Microbial source-tracking is a useful tool for trace evidence analysis in Forensics. Community-wide massively parallel sequencing profiles can bypass the need for satellite microbes or marker sets, which are unreliable when handling unstable samples. We propose a novel method utilizing Aitchison distance to select important suspects/sources, and then integrate it with existing algorithms in source tracking to estimate the proportions of microbial sample coming from important suspects/sources. A series of comprehensive simulation studies show that the proposed method is capable of accurate selection and therefore improves the performance of current methods such as Bayesian SourceTracker and FEAST in the presence of noise microbial sources.

SUBMITTER: Carter KM

PROVIDER: S-EPMC7377425 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Microbial community dissimilarity for source tracking with application in forensic studies.

Carter Kyle M KM Lu Meng M Luo Qianwen Q Jiang Hongmei H An Lingling L

PloS one 20200723 7

Microbial source-tracking is a useful tool for trace evidence analysis in Forensics. Community-wide massively parallel sequencing profiles can bypass the need for satellite microbes or marker sets, which are unreliable when handling unstable samples. We propose a novel method utilizing Aitchison distance to select important suspects/sources, and then integrate it with existing algorithms in source tracking to estimate the proportions of microbial sample coming from important suspects/sources. A ...[more]

PMID: 32702000

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Project description:It is important to track fecal sources from humans and animals that negatively influence the water quality of rural rivers and human health. In this study, microbial source tracking (MST) methods using molecular markers and the community-based FEAST (fast expectation-maximization microbial source tracking) program were synergistically applied to distinguish the fecal contributions of multiple sources in a rural river located in Beijing, China. The performance of eight markers were evaluated using 133 fecal samples based on real-time quantitative (qPCR) technique. Among them, six markers, including universal (BacUni), human-associated (HF183-1 and BacH), swine-associated (Pig-2-Bac), ruminant-associated (Rum-2-Bac), and avian-associated (AV4143) markers, performed well in the study. A total of 96 water samples from the river and outfalls showed a coordinated composition of fecal pollution, which revealed that outfall water might be a potential input of the Fsq River. In the FEAST program, bacterial 16S rRNA genes of 58 fecal and 12 water samples were sequenced to build the "source" library and "sink," respectively. The relative contribution (<4.01% of sequence reads) of each source (i.e., human, swine, bovine, or sheep) was calculated based on simultaneous screening of the operational taxonomic units (OTUs) of sources and sinks, which indicated that community-based MST methods could be promising tools for identifying fecal sources from a more comprehensive perspective. Results of the qPCR assays indicated that fecal contamination from human was dominant during dry weather and that fecal sources from swine and ruminant were more prevalent in samples during the wet season than in those during the dry season, which were consistent with the findings predicted by the FEAST program using a very small sample size. Information from the study could be valuable for the development of improved regulation policies to reduce the levels of fecal contamination in rural rivers.

Project description:BackgroundHigh-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.ResultsUsing amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.ConclusionsHill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package ( https://github.com/omvatten/qdiv ). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines. Video Abstract.

Dataset Information

Microbial community dissimilarity for source tracking with application in forensic studies.

Publications

Microbial community dissimilarity for source tracking with application in forensic studies.

Similar Datasets

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