Evidence map›Paper›PMID 39591472›Full record

ArticlePloS one2024

Exploring the host response in infected lung organoids using NanoString technology: A statistical analysis of gene expression data.

Mostafa Rezapour, Stephen J Walker, David A Ornelles, Muhammad Khalid Khan Niazi, Patrick M McNutt, Anthony Atala, Metin Nafi Gurcan

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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

15 citing papers in PubMed.

  1. Article
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  5. Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions.Computational and structural biotechnology journal · 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.

Mostafa RezapourCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.ORCID 0000-0001-9569-118X
Stephen J WalkerWake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.ORCID 0000-0002-0732-2366
David A OrnellesDepartment of Microbiology and Immunology, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Muhammad Khalid Khan NiaziCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Patrick M McNuttWake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Anthony AtalaWake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.
Metin Nafi GurcanCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we used a three-dimensional airway "organ tissue equivalent" (OTE) model at an air-liquid interface (ALI) to mimic human airways. We investigated the effects of three viruses (Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) on this model, incorporating various control conditions for data integrity. Our primary objective was to assess gene expression using the NanoString platform in OTE models infected with these viruses at 24- and 72-hour intervals, focusing on 773 specific genes. To enhance the comprehensiveness of our analysis, we introduced a novel algorithm, namely MAS (Magnitude-Altitude Score). This innovative approach uniquely combines biological significance, as indicated by fold changes in gene expression, with statistical rigor, as represented by adjusted p-values. By incorporating both dimensions, MAS ensures that the genes identified as differentially expressed are not mere statistical artifacts but hold genuine biological relevance, providing a more holistic understanding of the airway tissue response to viral infections. Our results unveiled distinct patterns of gene expression in response to viral infections. At 24 hours post-IAV infection, a robust interferon-stimulated gene (ISG) response was evident, marked by the upregulation of key genes including IFIT2, RSAD2, IFIT3, IFNL1, IFIT1, IFNB1, ISG15, OAS2, OASL, and MX1, collectively highlighting a formidable antiviral defense. MPV infection at the same time point displayed a dual innate and adaptive immune response, with highly expressed ISGs, immune cell recruitment signaled by CXCL10, and early adaptive immune engagement indicated by TXK and CD79A. In contrast, PIV3 infection at 24 hours triggered a transcriptional response dominated by ISGs, active immune cell recruitment through CXCL10, and inflammation modulation through OSM. The picture evolved at 72 hours post-infection. For IAV, ISGs and immune responses persisted, suggesting a sustained impact. MPV infection at this time point showed a shift towards IL17A and genes related to cellular signaling and immune responses, indicating adaptation to the viral challenge over time. In the case of PIV3, the transcriptional response remained interferon-centric, indicating a mature antiviral state. Our analysis underscored the pivotal role of ISGs across all infections and time points, emphasizing their universal significance in antiviral defense. Temporal shifts in gene expression indicative of adaptation and fine-tuning of the immune response. Additionally, the identification of shared and unique genes unveiled host-specific responses to specific pathogens. IAV exerted a sustained impact on genes from the initial 24 hours, while PIV3 displayed a delayed yet substantial genomic response, suggestive of a gradual and nuanced strategy.

Indexed as

LungOrganoidsGene Expression ProfilingGene Expression RegulationHost-Pathogen InteractionsHumansInfluenza A virusInfluenza, HumanMetapneumovirusParainfluenza Virus 3, Human

Identifiers

PMID39591472
PMCPMC11594423

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.