Evidence map›Paper›PMID 42650174›Full record

ArticleGenes2026

Single-Cell and Bulk Transcriptomics Reveal an Epithelial

Ana Luiza Labbate Bonaldo, Jeferson Dos Santos Souza, Jakeline Santos Oliveira, Amanda Piveta Schnepper, Caio Fernando Ferreira Mussatto, Victória Larissa Schimidt Camargo, Paula Paccielli Freire, Otavio Cabral-Marques, Sarah Santiloni Cury, Robson Francisco Carvalho

Abstract read
In one paragraph

Article in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ana Luiza Labbate BonaldoDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.
Jeferson Dos Santos SouzaDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.ORCID 0000-0002-4129-8769
Jakeline Santos OliveiraDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.
Amanda Piveta SchnepperDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.ORCID 0000-0002-5731-9085
Caio Fernando Ferreira MussattoDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.
Victória Larissa Schimidt CamargoDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.ORCID 0000-0002-1835-5673
Paula Paccielli FreireDepartment of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, SP, Brazil.ORCID 0000-0003-0649-8279
Otavio Cabral-MarquesDepartment of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, SP, Brazil.ORCID 0000-0002-3183-6236
Sarah Santiloni CuryDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.ORCID 0000-0002-4803-0933
Robson Francisco CarvalhoDepartment of Cellular and Molecular Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil.ORCID 0000-0002-4901-7714

Funding

Coordenação de Aperfeicoamento de Pessoal de Nível Superior 001Fundação de Amparo à Pesquisa do Estado de São Paulo 13/50343-1Fundação de Amparo à Pesquisa do Estado de São Paulo 2020/07069-0Fundação de Amparo à Pesquisa do Estado de São Paulo 2020/07972-1Fundação de Amparo à Pesquisa do Estado de São Paulo 2020/09146-1Santander Universidades
6 · The paper itself

Abstract

BACKGROUND/

objectivesThe molecular mechanisms underlying susceptibility to severe COVID-19 remain incompletely understood. We aimed to identify the signaling pathways associated with disease severity by integrating transcriptomic data and characterizing ligand-receptor interactions involved in the host response to SARS-CoV-2 infection.

methodsWe integrated publicly available bulk RNA-sequencing data from nasopharyngeal (NP) swabs (GSE152075) and single-cell RNA-sequencing data from bronchoalveolar lavage fluid samples (GSE145926). Analyses focused on secreted ligands and their cognate receptors and were performed in relation to demographic and clinical characteristics associated with susceptibility to severe COVID-19, including sex, age, and viral load.

resultsPatients with characteristics associated with increased susceptibility to severe disease, including male sex, advanced age, and high viral load, exhibited transcriptional programs enriched for inflammatory and immune-response pathways. In contrast, individuals with lower susceptibility displayed reduced expression of 43 ligand genes compared with matched negative controls, suggesting distinct secretory programs associated with the host response to infection. We identified an association between the expression of lactoferrin (

conclusionsOur findings provide insight into the molecular mechanisms associated with susceptibility to severe COVID-19 through the analysis of ligand and receptor expression in nasopharyngeal swabs and bronchoalveolar lavage fluid samples. The association between

Indexed as

COVID-19LactoferrinTranscriptomeAnimalsBronchoalveolar Lavage FluidDisease SusceptibilityEpithelial CellsFemaleHumansMaleSARS-CoV-2Signal TransductionSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisLactoferrinCOVID-19ligand and receptorLRP11LTFscRNA-Seqsecretome

Identifiers

PMID42650174
PMCPMC13511793

What OpenQuestion holds

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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.