Evidence map›Paper›PMID 31665066›Full record

ArticleMicrobiome2019

Microbiota analysis optimization for human bronchoalveolar lavage fluid.

Pierre H H Schneeberger, Janice Prescod, Liran Levy, David Hwang, Tereza Martinu, Bryan Coburn

Open access · goldAbstract read
In one paragraph

Article in Microbiome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
11.8field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 37 citations in OpenAlex.

  1. Review
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  3. Review
  4. Review
  5. Article
  6. Review
  7. What Makes the Gut-Lung Axis Working? From the Perspective of Microbiota and Traditional Chinese Medicine.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2024
    Review
  8. Article
  9. Microbiota and mycobiota in bronchoalveolar lavage fluid of silicosis patients.Journal of occupational medicine and toxicology (London, England) · 2023
    Article
  10. Article
  11. Article
  12. Article
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  19. Review
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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

6 authors at 2 institutions in 1 country.

Pierre H H SchneebergerDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada. pierre.schneeberger@gmail.com.
Janice PrescodDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada.
Liran LevyDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada.
David HwangDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada.
Tereza MartinuDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada.
Bryan CoburnDepartments of Medicine and Laboratory Medicine & Pathobiology, University of Toronto, Toronto, M5G 1L7, Canada. bryan.coburn@utoronto.ca.
University of Toronto · CAUniversity Health Network · CA

Funding

CIHR PJT149057
6 · The paper itself

Abstract

backgroundIt is now possible to comprehensively characterize the microbiota of the lungs using culture-independent, sequencing-based assays. Several sample types have been used to investigate the lung microbiota, each presenting specific challenges for preparation and analysis of microbial communities. Bronchoalveolar lavage fluid (BALF) enables the identification of microbiota specific to the lower lung but commonly has low bacterial density, increasing the risk of false-positive signal from contaminating DNA. The objectives of this study were to investigate the extent of contamination across a range of sample densities representative of BALF and identify features of contaminants that facilitate their removal from sequence data and aid in the interpretation of BALF sample 16S sequencing data.

resultsUsing three mock communities across a range of densities ranging from 8E+ 02 to 8E+ 09 16S copies/ml, we assessed taxonomic accuracy and precision by 16S rRNA gene sequencing and the proportion of reads arising from contaminants. Sequencing accuracy, precision, and the relative abundance of mock community members decreased with sample input density, with a significant drop-off below 8E+ 05 16S copies/ml. Contaminant OTUs were commonly inversely correlated with sample input density or not reproduced between technical replicates. Removal of taxa with these features or physical concentration of samples prior to sequencing improved both sequencing accuracy and precision for samples between 8E+ 04 and 8E+ 06 16S copies/ml. For the lowest densities, below 8E+ 03 16S copies/ml BALF, accuracy and precision could not be significantly improved using these approaches. Using clinical BALF samples across a large density range, we observed that OTUs with features of contaminants identified in mock communities were also evident in low-density BALF samples.

conclusionRelative abundance data and community composition generated by 16S sequencing of BALF samples across the range of density commonly observed in this sample type should be interpreted in the context of input sample density and may be improved by simple pre- and post-sequencing steps for densities above 8E+ 04 16S copies/ml.

Indexed as

DNA ContaminationFalse Positive ReactionsBacteriaBronchoalveolar Lavage FluidHumansLungMicrobiotaRNA, Ribosomal, 16SRNA, Ribosomal, 16SLung microbiotaSequencing accuracySequencing precision, bronchoalveolar lavage

Identifiers

PMID31665066
PMCPMC6821041
OpenAlexW2982504733

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.