Evidence map›Paper›PMID 36404584›Full record

ArticleTransboundary and emerging diseases2022

Screen the unforeseen: Microbiome-profiling for detection of zoonotic pathogens in wild rats.

Marieke de Cock, Manoj Fonville, Ankje de Vries, Alex Bossers, Bartholomeus van den Bogert, Renate Hakze-van der Honing, Ad Koets, Hein Sprong, Wim van der Poel, Miriam Maas

Open access · hybridAbstract read
In one paragraph

Article in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 34% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

10 authors at 4 institutions in 1 country.

Marieke de CockCentre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.ORCID https://orcid.org/0000-0002-0940-1180
Manoj FonvilleCentre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
Ankje de VriesCentre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
Alex BossersWageningen Bioveterinary Research (WBVR), Lelystad, The Netherlands.
Bartholomeus van den BogertBaseClear B.V., Leiden, The Netherlands.
Renate Hakze-van der HoningWageningen Bioveterinary Research (WBVR), Lelystad, The Netherlands.
Ad KoetsWageningen Bioveterinary Research (WBVR), Lelystad, The Netherlands.
Hein SprongCentre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
Wim van der PoelWageningen Bioveterinary Research (WBVR), Lelystad, The Netherlands.ORCID https://orcid.org/0000-0002-7498-8002
Miriam MaasCentre for Infectious Diseases Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.ORCID https://orcid.org/0000-0003-0122-106X
National Institute for Public Health and the Environment · NLUtrecht University · NLWageningen University & Research · NLBioclear Earth (Netherlands) · NL

Funding

European Union's Horizon 2020 Research and Innovation (One Health European Joint Programme) 773830Ministry of Health, Welfare and Sports (NL)
6 · The paper itself

Abstract

Wild rats can host various zoonotic pathogens. Detection of these pathogens is commonly performed using molecular techniques targeting one or a few specific pathogens. However, this specific way of surveillance could lead to (emerging) zoonotic pathogens staying unnoticed. This problem may be overcome by using broader microbiome-profiling techniques, which enable broad screening of a sample's bacterial or viral composition. In this study, we investigated if 16S rRNA gene amplicon sequencing would be a suitable tool for the detection of zoonotic bacteria in wild rats. Moreover, we used virome-enriched (VirCapSeq) sequencing to detect zoonotic viruses. DNA from kidney samples of 147 wild brown rats (Rattus norvegicus) and 42 black rats (Rattus rattus) was used for 16S rRNA gene amplicon sequencing of the V3-V4 hypervariable region. Blocking primers were developed to reduce the amplification of rat host DNA. The kidney bacterial composition was studied using alpha- and beta-diversity metrics and statistically assessed using PERMANOVA and SIMPER analyses. From the sequencing data, 14 potentially zoonotic bacterial genera were identified from which the presence of zoonotic Leptospira spp. and Bartonella tribocorum was confirmed by (q)PCR or Sanger sequencing. In addition, more than 65% of all samples were dominated (>50% reads) by one of three bacterial taxa: Streptococcus (n = 59), Mycoplasma (n = 39) and Leptospira (n = 25). These taxa also showed the highest contribution to the observed differences in beta diversity. VirCapSeq sequencing in rat liver samples detected the potentially zoonotic rat hepatitis E virus in three rats. Although 16S rRNA gene amplicon sequencing was limited in its capacity for species level identifications and can be more difficult to interpret due to the influence of contaminating sequences in these low microbial biomass samples, we believe it has potential to be a suitable pre-screening method in the future to get a better overview of potentially zoonotic bacteria that are circulating in wildlife.

Indexed as

Bartonella InfectionsMicrobiotaRodent DiseasesAnimalsAnimals, WildBacteriaRatsRNA, Ribosomal, 16SRNA, Ribosomal, 16S16Skidneyratssurveillanceviromezoonoses

Identifiers

PMID36404584
PMCPMC10099244
OpenAlexW4309517453

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.