pubmed.ncbi.nlm.nih.gov

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research - PubMed

  • ️Sat Jan 01 2022

Review

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Ethan W Morgan et al. Toxicol Sci. 2022.

Abstract

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Keywords: gut microbiome; metabolism; metabolomics; microbiome.

© The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.

A, Exposure to xenobiotics may have a toxic effect on members of the microbiota, altering the community composition and affecting the host. B, Microbial metabolism of xenobiotics may mitigate the host’s response to the compound. This may provide protection to the host against toxicants or reduce the effectiveness of pharmaceutical treatments. C, Members of the microbiota may obscure the cause of host toxicity by generating toxins from xenobiotic compounds. D, 16S rRNA gene sequencing, shotgun metagenomics, and metatranscriptomics provide detailed profiles of the taxonomic composition and genetics of the gut microbiota. E, Metabolite profiling with LC-MS/MS is used to identify the metabolic contributions of different organisms to the microbiome and identify relationships between detected compounds. F, Statistics methods simplify datasets with hundreds or thousands of features to identify similarities between samples and predict the causes of feature variability. G, Integrating sequencing and metabolomics techniques can provide a means to generate testable hypotheses that can be assessed using gnotobiotic mice and synthetic communities. Created with BioRender.com.

Figure 2.
Figure 2.

Sequencing-based analysis of the microbiome begins with the extraction of nucleic acids, library preparation, and high-throughput sequencing. Sequencing data is processed using bioinformatics software or pipelines based on the sequencing technique. Popular and user-friendly software for taxonomic identification using the 16S rRNA gene include QIIME2, DADA2, and mothur. Whole metagenome sequencing, also called shotgun metagenomics, and metatranscript sequencing data is often processed using complete software pipelines like HUMAnN2 or SqueezeMeta. All three sequencing techniques provide taxonomic profiling of the microbiome, but shotgun metagenomics and metatranscriptomics also provide profiles of functional potential or expressed genes. Created with BioRender.com.

Similar articles

Cited by

References

    1. Afgan E., Baker D., Batut B., van den Beek M., Bouvier D., Čech M., Chilton J., Clements D., Coraor N., Grüning B. A., et al. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544. - PMC - PubMed
    1. Aitchison J. (1982). The statistical analysis of compositional data. J. R. Stat. Soc. Series B Stat. Methodol. 44, 139–160.
    1. Aitchison J., Barceló-Vidal C., Martín-Fernández J. A., Pawlowsky-Glahn V. (2000). Logratio analysis and compositional distance. Math. Geol. 32, 271–275.
    1. Allen D. R., McWhinney B. C. (2019). Quadrupole time-of-flight mass spectrometry: a paradigm shift in toxicology screening applications. Clin. Biochem. Rev. 40, 135–146. - PMC - PubMed
    1. Alonso A., Marsal S., Julià A. (2015). Analytical methods in untargeted metabolomics: state of the art in 2015. Front. Bioeng. Biotechnol. 3, 23. - PMC - PubMed

Publication types

MeSH terms

Substances