Back

Fast and efficient Borrelia genome recovery from tick samples using Whole-Genome Amplification

Melis, S.; Bunduc, C. C.; Mottarlini, L.; Vumbaca, M.; Mileto, I.; Hepner, S.; Margos, G.; Fingerle, V.; Sing, A.; Prati, P.; Bandi, C.; Sambri, V.; Baldanti, F.; Gaiarsa, S.; Bellinzona, G.; Sassera, D.

2025-12-04 genomics
10.64898/2025.12.02.691817 bioRxiv
Show abstract

Borrelia burgdorferi sensu lato bacteria are the causative agents of Lyme borreliosis, a multisystemic illness with an expanding epidemiology in temperate areas. Genomic studies on Borrelia are hindered by the difficulty of culturing procedures: standard protocols require extended incubation, substantial technical expertise, and are prone to failure, limiting timely recovery of isolates. This contributes to a low number of complete Borrelia genomes available in public repositories, particularly for species other than B. burgdorferi sensu stricto. Here we introduce a novel approach that overcomes the necessity for extended culture by utilizing Whole Genome Amplification (WGA) directly on freshly collected ticks, and that can be performed in parallel to classical culturing. The protocol is paired with a tailored bioinformatic pipeline designed to ensure accurate assembly and reliable downstream analyses. Benchmarking on multiple control isolates demonstrated that the method yields high-quality chromosomal assemblies. To demonstrate practical applicability, we applied the protocol to freshly collected ticks, successfully generating five high-quality Borrelia chromosomes (two B. lusitaniae, two B. afzelii and one B. garinii). By generating sequencing-ready DNA in five days rather than months, our protocol greatly streamlines the process and minimizes the effort associated with traditional culture-based methods. This workflow will facilitate broader representation of understudied Borrelia species and support future epidemiological, ecological, and evolutionary investigations on this pathogen.

Matching journals

The top 6 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.