Evidence map›Paper›PMID 34332568›Full record

ArticleBMC veterinary research2021

Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing.

Elton J R Vasconcelos, Chayan Roy, Joseph A Geiger, Kristina M Oney, Melody Koo, Songyang Ren, Brian B Oakley, Pedro Paulo V P Diniz

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC veterinary research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 2 institutions in 2 countries.

Elton J R VasconcelosCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Chayan RoyCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Joseph A GeigerCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Kristina M OneyCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Melody KooCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Songyang RenCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Brian B OakleyCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA.
Pedro Paulo V P DinizCollege of Veterinary Medicine, Western University of Health Sciences, 309 East 2nd Street, CA, 91766 - 1854, Pomona, USA. pdiniz@westernu.edu.
Western University of Health Sciences · USUniversity of Leeds · GB

Funding

American Kennel Club Canine Health Foundation 02292 and 02528
6 · The paper itself

Abstract

backgroundVector-borne diseases (VBDs) impact both human and veterinary medicine and pose special public health challenges. The main bacterial vector-borne pathogens (VBPs) of importance in veterinary medicine include Anaplasma spp., Bartonella spp., Ehrlichia spp., and Spotted Fever Group Rickettsia. Taxon-targeted PCR assays are the current gold standard for VBP diagnostics but limitations on the detection of genetically diverse organisms support a novel approach for broader detection of VBPs. We present a methodology for genetic characterization of VBPs using Next-Generation Sequencing (NGS) and computational approaches. A major advantage of NGS is the ability to detect multiple organisms present in the same clinical sample in an unsupervised (i.e. non-targeted) and semi-quantitative way. The Standard Operating Procedure (SOP) presented here combines industry-standard microbiome analysis tools with our ad-hoc bioinformatic scripts to form a complete analysis pipeline accessible to veterinary scientists and freely available for download and use at https://github.com/eltonjrv/microbiome.westernu/tree/SOP .

resultsWe tested and validated our SOP by mimicking single, double, and triple infections in genomic canine DNA using serial dilutions of plasmids containing the entire 16 S rRNA gene sequence of (A) phagocytophilum, (B) v. berkhoffii, and E. canis. NGS with broad-range 16 S rRNA primers followed by our bioinformatics SOP was capable of detecting these pathogens in biological replicates of different dilutions. These results illustrate the ability of NGS to detect and genetically characterize multi-infections with different amounts of pathogens in a single sample.

conclusionsBloodborne microbiomics & metagenomics approaches may help expand the molecular diagnostic toolbox in veterinary and human medicine. In this paper, we present both in vitro and in silico detailed protocols that can be combined into a single workflow that may provide a significant improvement in VBP diagnostics and also facilitate future applications of microbiome research in veterinary medicine.

Indexed as

AnimalsBacteriaDog DiseasesDogsHigh-Throughput Nucleotide SequencingReproducibility of ResultsRNA, BacterialRNA, Ribosomal, 16SVector Borne DiseasesRNA, BacterialRNA, Ribosomal, 16S16S rRNAblood microbiomecomputational pipelinediagnosticsNGSVector-borne diseasesvector-borne pathogens

Identifiers

PMID34332568
PMCPMC8325813
OpenAlexW3190474906

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.