Back

Core genome sequencing and genotyping of Leptospira interrogans in clinical samples by target capture sequencing

Grillova, L.; Cokelaer, T.; Mariet, J.-F.; Pipoli da Fonseca, J.; PICARDEAU, M.

2022-04-29 microbiology
10.1101/2022.04.29.490004 bioRxiv
Show abstract

The life-threatening pathogen Leptospira interrogans is the most common agent of leptospirosis, an emerging zoonotic disease. However, little is known about the strains that are circulating worldwide due to the fastidious nature of the bacteria and its difficulty to be culture isolated. In addition, the paucity of bacteria in blood and other clinical samples has proven to be a considerable challenge for directly genotyping the agent of leptospirosis directly from patient material. Here, to elucidate the genomic diversity of Leptospira circulating strains, hybridization capture followed by Illumina sequencing of the core genome was performed directly from 20 biological samples that were PCR positive for pathogenic Leptospira. A set of samples subjected to capture with RNA probes covering the L. interrogans core genome resulted in 72 to 13,000-fold increase in pathogen reads when compared to standard sequencing without capture. A SNP analysis of the genomes sequenced from the biological samples using 273 Leptospira reference genome was then performed in order to determine the genotype of the infecting strain. For samples with sufficent coverage (19/20 samples with coverage >8X), we could unambigously identify L. interrogans sv Icterohaemorrhagiae (14 samples), L. kirschneri sv Grippotyphosa (4 samples) and L. interrogans sv Pyrogenes (1 sample) as the infecting strain. In conclusion, we obtained for most of our biological samples high quality genomic data at suitable coverage for confident core genome genotyping of the agent of leptospirosis. The ability to generate culture-free genomic data opens new opportunities to better understand the epidemiology and evolution of this fastidious pathogen.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.