Evidence map›Paper›PMID 30106352›Full record

ArticleOmics : a journal of integrative biology2018

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

Anil K Madugundu, Babylakshmi Muthusamy, Sreelakshmi K Sreenivasamurthy, Chandra Bhavani, Jyoti Sharma, Bankatesh Kumar, Krishna R Murthy, Raju Ravikumar, Akhilesh Pandey

Abstract read
In one paragraph

Article in Omics : a journal of integrative biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 51% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 3 countries.

Anil K Madugundu1 Manipal Academy of Higher Education (MAHE) , Manipal, Karnataka, India .
Babylakshmi Muthusamy1 Manipal Academy of Higher Education (MAHE) , Manipal, Karnataka, India .
Sreelakshmi K Sreenivasamurthy1 Manipal Academy of Higher Education (MAHE) , Manipal, Karnataka, India .
Chandra Bhavani2 Institute of Bioinformatics , International Technology Park, Bangalore, Karnataka, India .
Jyoti Sharma1 Manipal Academy of Higher Education (MAHE) , Manipal, Karnataka, India .
Bankatesh Kumar4 National Institute of Mental Health and Neurosciences , Bangalore, Karnataka, India .
Krishna R Murthy1 Manipal Academy of Higher Education (MAHE) , Manipal, Karnataka, India .
Raju Ravikumar4 National Institute of Mental Health and Neurosciences , Bangalore, Karnataka, India .
Akhilesh Pandey3 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine , Baltimore, Maryland.
Institute of Bioinformatics · INJohns Hopkins Medicine · USNational Institute of Mental Health and Neurosciences · INManipal Academy of Higher Education · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnimalsBorrelia burgdorferiHigh-Throughput Nucleotide SequencingHumansLyme DiseaseTicksBorreliadog tickLyme diseasemolecular diagnosistick-borne diseases

Identifiers

PMID30106352
PMCPMC6112187
OpenAlexW2886497293

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.