A Next-Generation Sequencing-Based Molecular Approach to Characterize a Tick Vector in Lyme Disease

Abstract

Next-generation sequencing approaches have revolutionized genomic medicine and enabled rapid diagnosis for several diseases. These approaches are widely used for pathogen detection in several infectious diseases. Lyme disease is a tick-borne infectious disease, which affects multiple organs. The causative organism is a spirochete, Borrelia burgdorferi, which is transmitted by ticks. Lyme disease can be treated easily if detected early, but its diagnosis is often delayed or is incorrect leading to a chronic debilitating condition. Current confirmatory diagnostic tests for Lyme disease rely on detection of antigens derived from B. burgdorferi, which are prone to both false positives and false negatives. Instead of focusing only on the human host for the diagnosis of Lyme disease, one could also attempt to identify the vector (tick) and the causative organism carried by the tick. Since all ticks do not transmit Lyme disease, it can be informative to accurately identify the tick from the site of bite, which is often observed by the patient and discarded. However, identifying ticks based on morphology alone requires a trained operator and can still be incorrect. Thus, we decided to take a molecular approach by sequencing DNA and RNA from a tick collected from an individual bitten by the tick. Using next-generation sequencing, we confirmed the identity of the tick as a dog tick, Dermacentor variabilis, and did not identify any pathogenic bacterial sequences, including Borrelia species. Despite the limited availability of nucleotide sequences for many types of ticks, our approach correctly identified the tick species. This proof-of-principle study demonstrates the potential of next-generation sequencing in the diagnosis of tick-borne infections, which can also be extended to other zoonotic diseases.

Keywords: Borrelia; Lyme disease; dog tick; molecular diagnosis; tick-borne diseases.

FIG. 1.

Workflow for analysis of next-generation sequencing data. Workflow employed for analysis of (A) whole-genome and (B) RNA-seq sequencing data. Genomic DNA and RNA sequences for Ixodida (taxonomy ID: txid6935) and bacterial genomes were downloaded from NCBI as indicated. NCBI, National Center for Biotechnology Information.

FIG. 2.

Summary of alignment data from WGS. (A) Histogram of the number of WGS reads that mapped to various tick species. The size of vertical bars and circles represent the number of reads that map to the indicated tick species. Reads mapping to tick genomes were aligned against individual draft genomes of various ticks using BLAST to identify the exact tick species based on sequence identity. (B) Distribution of the number of mapped reads and sequence identity across tick species. The number of mapped reads against Ixodes scapularis was higher than against Dermacentor variabilis because of overrepresentation of I. scapularis sequences in NCBI. WGS, whole-genome sequencing.

FIG. 3.

Conservation of subolesin gene across the tick species. Sequence alignment shows the conservation of subolesin gene identified from the sequenced tick against four related tick species along with sequence identity indicated in percent.

FIG. 4.

Summary of alignment data from RNA-seq experiments. Sizes of vertical bars and circles represent the number of reads that map to the indicated tick species. Reads mapping to tick transcripts were aligned against individual transcript assemblies of various ticks using BLAST to confirm the identity of tick species based on sequence identity. (A) Histogram of the number of RNA-seq reads that mapped across tick species. (B) Distribution of number of reads and sequence identity across tick species.

FIG. 5.

Conservation of beta-actin transcript across tick species. Sequence alignment shows conservation of beta-actin transcript from the sequenced tick against four related tick species along with sequence identity indicated in percent.