1887

Microbial Genomics logo

Volume 8, Issue 12

A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis Open Access

Abstract

The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community’s response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely and . These organisms often contribute to the harmful long-term sequelae seen in these young infants.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antibacterial resistance , causal Bayesian network , multi-omics , origin of replication and peak-to-trough ratio (PTR)
Funding
This study was supported by the:
  • National Institute of Health (Award 1R15AI128714-01)
    • Principle Award Recipient: GiriNarasimhan
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2022-12-20
2024-04-25
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http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000899
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A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis
M Gen 8, 000899 (2022); https://doi.org/10.1099/mgen.0.000899
/content/journal/mgen/10.1099/mgen.0.000899
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