A Method for Tracking CRISPR/Plasmidome Dynamics in Complex Bacterial Communities
Final Report Abstract
This DFG project focused on studying the changes in community composition of a synthetic bacterial community (OMM12) during repeated rounds of antibiotic treatment in gnotobiotic mice. Taken together, we performed an animal experiment using 20 stably colonized OMM12 mice and generated a comprehensive collection of 307 fecal samples of which 112 were subjected to whole genome shotgun sequences plus a collection of bacterial strains collected via colony picking. While wet lab experiments were performed as planned, we identified several obstacles when analysing the NGS data for which we tested and developed tools to better analyse these datasets. Due to the availability of high quality reference genomes, variant calling was performed via direct mapping of reads rather than assembly, which is commonly done in similar studies on complex, undefined complex communities. In our case, we found that a high fraction of shared genomic regions within and between community members artificially inflate variant profiles when using GATK best practices variant calling pipelines and additional filtering needs to be performed in such a setting, which could be a valuable observation for similar studies. Furthermore, we demonstrated that absolute abundance profiles are highly valuable for interpretation of community dynamics and only there we detect clear and easy to interpret community dynamics responses to the treatment regimes demonstrating that the study was well designed. In our study we observed community response to the antibiotic treatment, in detail we found that: ● Multiple rounds of antibiotic-treatment leads to evolution of community resistance. ● This coincided with dynamic occurence of ABX resistant strains in response to antibiotic treatment for Tetracycline and Ciprofloxacin group. ● Using metagenomic sequencing, we were able to track ABX resistance to specific alleles in known ABX resistance genes. ● We developed an algorithm for defining “genomotypes'' from metagenomics data using Non-negative matrix factorization. This shows different genomotypes dynamics for individual bacteria and treatment groups. ● We re-isolated 2 of the bacterial strains and confirmed increased ABX resistance (MIC) of the challenged community in respect to the wildtype. We are in the process of performing the last evaluations of the NMF procedure and putting the pieces together to finish a manuscript describing this study. We believe that our insights gained will be valuable for both, our understanding of the impact of antibiotics on the microbiome and refinement of tools used for the evolutionary analysis of metagenomic datasets in general.
Publications
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2019. DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection. Bioinformatics, 35(14), pp.2498-2500
Asgari, E., Münch, P.C., Lesker, T.R., McHardy, A.C. and Mofrad, M.R.
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2019. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nature microbiology, 4(3), pp.470-479
Vatanen, T., Plichta, D.R., Somani, J., Münch, P.C., Arthur, T.D., Hall, A.B., Rudolf, S., Oakeley, E.J., Ke, X., Young, R.A. and Haiser, H.J.
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2021. Identification of Natural CRISPR Systems and Targets in the Human Microbiome. Cell Host & Microbe, 29(1), pp.94-106
Münch, P.C., Franzosa, E.A., Stecher, B., McHardy, A.C. and Huttenhower, C.
