Dataset Information

A systematic sequencing-based approach for microbial contaminant detection and functional inference.

ABSTRACT: BACKGROUND:Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. RESULTS:We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000-100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. CONCLUSIONS:We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

SUBMITTER: Park SJ

PROVIDER: S-EPMC6743104 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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A systematic sequencing-based approach for microbial contaminant detection and functional inference.

Park Sung-Joon SJ Onizuka Satoru S Seki Masahide M Suzuki Yutaka Y Iwata Takanori T Nakai Kenta K

BMC biology 20190913 1

<h4>Background</h4>Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed.<h4>Results</h4> ...[more]

PMID: 31519179

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Project description:UnlabelledGeobacter sulfurreducens strain KN400 was recovered in previous studies in which a culture of the DL1 strain of G. sulfurreducens served as the inoculum in investigations of microbial current production at low anode potentials (-400 mV versus Ag/AgCl). Differences in the genome sequences of KN400 and DL1 were too great to have arisen from adaptive evolution during growth on the anode. Previous deep sequencing (80-fold coverage) of the DL1 culture failed to detect sequences specific to KN400, suggesting that KN400 was an external contaminant inadvertently introduced into the anode culturing system. In order to evaluate this further, a portion of the gene for OmcS, a c-type cytochrome that both KN400 and DL1 possess, was amplified from the DL1 culture. HiSeq-2000 Illumina sequencing of the PCR product detected the KN400 sequence, which differs from the DL1 sequence at 14 bp, at a frequency of ca. 1 in 10(5) copies of the DL1 sequence. A similar low frequency of KN400 was detected with quantitative PCR of a KN400-specific gene. KN400 persisted at this frequency after intensive restreaking of isolated colonies from the DL1 culture. However, a culture in which KN400 could no longer be detected was obtained by serial dilution to extinction in liquid medium. The KN400-free culture could not grow on an anode poised at -400 mV. Thus, KN400 cryptically persisted in the culture dominated by DL1 for more than a decade, undetected by even deep whole-genome sequencing, and was only fortuitously uncovered by the unnatural selection pressure of growth on a low-potential electrode.ImportanceRepeated streaking of isolated colonies on solidified medium remains a common strategy for obtaining pure cultures, especially of difficult-to-cultivate microorganisms such as strict anaerobes. The results presented here demonstrate that verifying the purity of cultures obtained in this manner may be difficult because extremely rare variants can persist, undetectable with even deep genomic DNA sequencing. The only way to ensure that a culture is pure is to cultivate it from an initial single cell, which may be technically difficult for many environmentally significant microbes.

Project description:Motivation:Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project. Results:To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies. Availability and implementation:TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html. Contact:yijuan.hu@emory.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

A systematic sequencing-based approach for microbial contaminant detection and functional inference.

Publications

A systematic sequencing-based approach for microbial contaminant detection and functional inference.

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