Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

Abstract

MotivationMetagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as {beta}-lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. ResultsWe introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PO_SCPLOWEC_SCPLOWTRO_SCPLOWIC_SCPLOW, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to {beta}-lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome. AvailabilityThe pipeline for calculation of bacterial replication rates using the high performance computing framework Slurm is available from https://biorg.cs.fiu.edu/petri/.