Evidence map›Paper›PMID 36324140›Full record

ArticleMicrobiome2022

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

Ilya B Slizovskiy, Marco Oliva, Jonathen K Settle, Lidiya V Zyskina, Mattia Prosperi, Christina Boucher, Noelle R Noyes

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

18 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Evolution, Mechanisms, and Therapeutic Implications of Mobile Tetracycline Destructases.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026
    Review
  3. Review
  4. Article
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  7. Enrichment techniques for clinical metagenomics.Frontiers in cellular and infection microbiology · 2026
    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

7 authors at 3 institutions in 1 country.

Ilya B Slizovskiy *Food-Centric Corridor, Infectious Disease Laboratory, Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Marco Oliva *Department of Computer and Information Science and Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA.
Jonathen K SettleDepartment of Computer and Information Science and Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA.
Lidiya V ZyskinaProgram in Human-Computer Interaction, College of Information Studies, University of Maryland, College Park, MD, USA.
Mattia ProsperiData Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
Christina BoucherDepartment of Computer and Information Science and Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA.
Noelle R NoyesFood-Centric Corridor, Infectious Disease Laboratory, Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA. nnoyes@umn.edu.
University of Florida · USUniversity of Minnesota · USUniversity of Maryland, College Park · US

Funding

Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
NIAID NIH HHS R01 AI141810NIH HHS 1R01AI141810-01
6 · The paper itself

Abstract

backgroundMetagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq).

resultsUsing technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT.

conclusionsTELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

Indexed as

Anti-Bacterial AgentsMetagenomeAnimalsDrug Resistance, BacterialGenes, BacterialHumansMetagenomicsAnti-Bacterial AgentsAntimicrobial resistanceLong-read sequencingMetagenomicsMicrobiomeMobile genetic elementsPublic healthResistome

Identifiers

PMID36324140
PMCPMC9628182
OpenAlexW4308133474

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.