Evidence map›Paper›PMID 28207848›Full record

ReviewPLoS pathogens2017

Resolving host-pathogen interactions by dual RNA-seq.

Alexander J Westermann, Lars Barquist, Jörg Vogel

Abstract readReview
In one paragraph

Review in PLoS pathogens, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
159citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

159 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. Facultative mutualism betweenISME communications · 2026
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  17. Frontiers in bioinformatics · 2026
    Review
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99 more citing papers are in PubMed but not listed here.

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

3 authors.

Alexander J WestermannRNA Biology Group, Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany.
Lars BarquistRNA Biology Group, Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany.ORCID 0000-0003-4732-2667
Jörg VogelRNA Biology Group, Institute for Molecular Infection Biology, University of Würzburg, Würzburg, Germany.ORCID 0000-0003-2220-1404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transcriptome is a powerful proxy for the physiological state of a cell, healthy or diseased. As a result, transcriptome analysis has become a key tool in understanding the molecular changes that accompany bacterial infections of eukaryotic cells. Until recently, such transcriptomic studies have been technically limited to analyzing mRNA expression changes in either the bacterial pathogen or the infected eukaryotic host cell. However, the increasing sensitivity of high-throughput RNA sequencing now enables "dual RNA-seq" studies, simultaneously capturing all classes of coding and noncoding transcripts in both the pathogen and the host. In the five years since the concept of dual RNA-seq was introduced, the technique has been applied to a range of infection models. This has not only led to a better understanding of the physiological changes in pathogen and host during the course of an infection but has also revealed hidden molecular phenotypes of virulence-associated small noncoding RNAs that were not visible in standard infection assays. Here, we use the knowledge gained from these recent studies to suggest experimental and computational guidelines for the design of future dual RNA-seq studies. We conclude this review by discussing prospective applications of the technique.

Indexed as

AnimalsHigh-Throughput Nucleotide SequencingHost-Pathogen InteractionsHumansSequence Analysis, RNA

Identifiers

PMID28207848
PMCPMC5313147

What OpenQuestion holds

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LicenceCC BY
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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.