Back

Application of long-read sequencing for genotyping, epigenetic profiling and surveillance of Yersinia pestis isolates from natural foci and disease outbreaks in Central Asia

Abdirassilova, A. A.; Yessimseit, D. T.; Rysbekova, A. K.; Kassenova, A. K.; Abdeliyev, B. Z.; Zhumadilova, Z. B.; Tokmurziyeva, G. Z.; Dikhanbayev, A. S.; Sanzhar, A. D.; Motin, V. L.; Reva, O. N.

2025-10-14 microbiology
10.1101/2025.10.14.682323 bioRxiv
Show abstract

This study explores the application of long-read sequencing technologies for genotyping, epigenetic profiling, and epidemiological monitoring of Yersinia pestis isolates obtained from natural foci in Central Asia and previous zoonotic outbreaks. Computational tools for genome assembly and genotyping were developed, enabling high-precision identification of both chromosomal and plasmid sequences, including the small cryptic pCKF plasmid. SNP-based genotyping distinguished the major Y. pestis biovars (Antiqua, Medievalis and non-main) and revealed cluster-specific diversity among Medievalis (MED) isolates, identifying a group of strains particularly prone to transmission from rodents to domestic animals and humans, which can be facilitated by the plasmid pCKF. Specific genomic polymorphisms were identified in sub-clades of MED isolates, which allow their identification with high precision. Additionally, comparative epigenomic analysis uncovered strain-specific cytosine methylation patterns at cgGATCG motifs, which may be linked to genome function regulation and adaptation to different hosts and environments. These findings demonstrate the effectiveness of long-read sequencing technologies in revealing both genetic and epigenetic features of bacterial pathogens, contributing to our understanding of the evolutionary mechanisms underlying the emergence and spread of this especially dangerous infection.

Published in Vector-Borne and Zoonotic Diseases · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 5 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.