Detecting and typing Chlamydia trachomatis strains in metagenomes using the MetaChlam pipeline

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Abstract The Gram negative bacteria Chlamydia trachomatis ( Ct ), an obligate intracellular human pathogen, is a predominant cause of sexually transmitted infections and ocular trachoma globally, exerting a significant impact on public health. Ct “strains” (major lineages within the species) are known to have different tissue tropisms and be associated with different disease outcomes. Metagenome samples from typical sites where Ct infects (e.g., endocervix, conjunctiva, rectum) rarely contain enough reads for traditional genotyping methods such as Multi-Locus Sequence Typing (MLST) or ompA genotyping. To overcome these limitations, we implemented an ensemble tool called MetaChlam that can accurately classify Ct strains with as few as 250 Ct reads. Using 109 publicly available Ct genomes from naturally circulating strains, we established that an ANI-based threshold of 99.75% was capable of distinguishing Ct strains from each other. We implemented metagenome-based typing using the previously developed LINtax, Strainscan, StrainGE, and Sourmash softwares. MetaChlam integrated the four tools along with custom databases into an automated nextflow pipeline. Using simulated metagenomic reads, we found that our pipeline accurately identified the correct strains in both single strain and multi-strain mixtures of samples. Finally, we showed that MetaChlam had higher specificity for the true presence of Ct reads in NCBI SRA metagenomic datasets than NCBI PebbleScout software. A surprising finding of these analyses was that reads from Ct , an obligate human intracellular pathogen, can be found as contaminants in samples from sites where the organism is almost certainly not present. Overall, our study enhances the characterization and classification of Ct strains and provides protocols for identification and typing of Ct in shotgun metagenome data. The MetaChlam pipeline is available on Github: https://github.com/parul-sharma/MetaChlam .

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