TLR5 signalling is hyper-responsive in porcine cystic fibrosis airways epithelium

Fleurot, Isabelle; López-Gálvez, Raquel; Barbry, Pascal; Guillon, Antoine; Si‐Tahar, Mustapha; Bähr, Andrea; Klymiuk, Nikolai; Sirard, Jean‐Claude; Caballero, Ignacio

doi:10.1016/j.jcf.2021.08.002

Cited by 5 publications

(8 citation statements)

References 18 publications

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“…This increase in TMPRSS2 expression was more notable in Calu-3-CFTR-KD than in Calu-3-CFTR-WT (Figure 2B), and the TMPRSS2 upregulation was also detected at the protein level (Figure 2D). As expected, flagellin induced the synthesis of the proinflammatory cytokines interleukin (IL)-8 and IL-6 (Supplementary Figure 2A, B) both in Calu-3-CFTR-WT and Calu-3-CFTR-KD cells, and this inflammatory response was relatively higher in the Calu-3-CFTR-KD cells, which agrees with previous work showing that CF epithelial cells from human (19) or porcine (20) origin exhibit an enhanced inflammatory response to flagellin.…”

Section: Ace2 Furin and Tmprss2 Expression In Cf And Non-cf Primary Human Aecs Upon P Aeruginosa Infectionsupporting

confidence: 92%

Flagellin From Pseudomonas aeruginosa Modulates SARS-CoV-2 Infectivity in Cystic Fibrosis Airway Epithelial Cells by Increasing TMPRSS2 Expression

et al. 2021

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In the coronavirus disease 2019 (COVID-19) health crisis, one major challenge is to identify the susceptibility factors of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in order to adapt the recommendations for populations, as well as to reduce the risk of COVID-19 development in the most vulnerable people, especially patients with chronic respiratory diseases such as cystic fibrosis (CF). Airway epithelial cells (AECs) play a critical role in the modulation of both immune responses and COVID-19 severity. SARS-CoV-2 infects the airway through the receptor angiotensin-converting enzyme 2, and a host protease, transmembrane serine protease 2 (TMPRSS2), plays a major role in SARS-CoV-2 infectivity. Here, we show that Pseudomonas aeruginosa increases TMPRSS2 expression, notably in primary AECs with deficiency of the ion channel CF transmembrane conductance regulator (CFTR). Further, we show that the main component of P. aeruginosa flagella, the protein flagellin, increases TMPRSS2 expression in primary AECs and Calu-3 cells, through activation of Toll-like receptor-5 and p38 MAPK. This increase is particularly seen in Calu-3 cells deficient for CFTR and is associated with an intracellular increased level of SARS-CoV-2 infection, however, with no effect on the amount of virus particles released. Considering the urgency of the COVID-19 health crisis, this result may be of clinical significance for CF patients, who are frequently infected with and colonized by P. aeruginosa during the course of CF and might develop COVID-19.

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Section: Ace2 Furin and Tmprss2 Expression In Cf And Non-cf Primary Human Aecs Upon P Aeruginosa Infectionsupporting

confidence: 92%

Flagellin From Pseudomonas aeruginosa Modulates SARS-CoV-2 Infectivity in Cystic Fibrosis Airway Epithelial Cells by Increasing TMPRSS2 Expression

et al. 2021

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“…Indeed, CFTR belongs to a protein-protein interactions (PPI) network (Pereira et al, 2021; Farinha and Gentzsch, 2021), and the absence of CFTR may perturb its direct or indirect interactors, and propagate dysregulations towards various biological pathways in which these interactors play a role. In agreement with this idea, studies on CFTR -/- knockout mice (Crites et al, 2015), CFTR -/- knockout piglets (Fleurot et al, 2022), and cell lines in which CFTR is inactivated by the CRISPR/Cas9 technology (Hao et al, 2020) have reported that the absence of CFTR affects cell signalling and transcriptional regulation. These dysregulations may explain various and apparently unrelated cellular phenotypes, including uncontrolled pro-inflammatory response (Jacquot et al, 2008), unbalanced oxidative stress with increased reactive oxygen species (Jeanson et al, 2012), impaired epithelial regeneration (Conese and Di Gioia, 2021), or perturbation of cell junctions and cytoskeleton (Pankonien et al, 2022).…”

Section: Introductionmentioning

confidence: 76%

From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis

Najm,

Martignetti,

Cornet

et al. 2023

Preprint

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BackgroundCystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways.MethodsWe propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes.ResultsBased on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotype was found to be consistent with CF pathology.ConclusionAnalysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

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“…NTRK3 [81], MMP9 [82], IGF2 [83], SOCS3 [84], IGFBP2 [85], SLC1A3 [86], NOS1AP [87], HP (haptoglobin) [88], TLR5 [89], ARG1 [90], NRG1 [91], RGS5 [92], LCN2 [93], TSPO (translocator protein) [94], PLAU (plasminogen activator, urokinase) [95], MME (membrane metalloendopeptidase) [96], BST1 [97], NR6A1 [98], TLR4 [99], NRN1 [100], ABCA13 [101], CTSD (cathepsin D) [102], NCAM1 [103], SIGMAR1 [104], CNR2 [105], CCR4 [106], ABCB1 [107], TIMELESS (timeless circadian regulator) [108], IRF8 [109], TXNIP (thioredoxin interacting protein) [110], SUV39H1 [111], CRY1 [112], CRY2 [113], PDCD4 [114] and MGAT5 [115] played an important role in depression and anxiety. Accumulating evidence has demonstrated that MMP9 [116], S100A12 [117], HP (haptoglobin) [118], TLR5 [119], NRG1 [120], PLAU (plasminogen activator, urokinase) [121], SLC11A1 [122], AQP9 [123], CHIT1 [124], TLR4 [125], SLC26A8 [126], CTSD (cathepsin D) [127], SERPINB1 [128], FASLG (Fas ligand) [129], SLC4A4 [130], AQP3 [131] and IRF8 [132] appears to be constitutively associated with CF . MMP9 [133], S100A12 [134], SOCS3 [135], MMP8 [136], CRISP3 [137], S100A9 [138], RETN (resistin) [139], IL1RN [140], TLR4 [141], GGT1 [142], CTSD (cathepsin D) [143], FASLG (Fas ligand) [144], CCR4 [145], FCRL3 [146] and ABCF1 [147] were revealed and regarded as diagnostic biomarker in pancreatitis.…”

Section: Discussionmentioning

confidence: 99%

“…CHGA (chromogranin A) [48] and IL23R [49] [99], NRN1 [100], ABCA13 [101], CTSD (cathepsin D) [102], NCAM1 [103], SIGMAR1 [104], CNR2 [105], CCR4 [106], ABCB1 [107], TIMELESS (timeless circadian regulator) [108], IRF8 [109], TXNIP (thioredoxin interacting protein) [110], SUV39H1 [111], CRY1 [112], CRY2 [113], PDCD4 [114] and MGAT5 [115] played an important role in depression and anxiety. Accumulating evidence has demonstrated that MMP9 [116], S100A12 [117], HP (haptoglobin) [118], TLR5 [119], NRG1 [120], PLAU (plasminogen activator, urokinase) [121], SLC11A1 [122], AQP9 [123], CHIT1 [124], TLR4 [125], SLC26A8 [126], CTSD (cathepsin D) [127], SERPINB1 [128], FASLG (Fas ligand) [129], SLC4A4 [130], AQP3 [131] and IRF8 [132] appears to be constitutively associated with CF. MMP9 [133], S100A12 [134], SOCS3 [135], MMP8 [136], CRISP3 [137], S100A9 [138], RETN (resistin) [139], IL1RN [140], TLR4 [141], GGT1 [142], CTSD (cathepsin D) [143], FASLG (Fas ligand) [144], CCR4 [145], FCRL3 [146] and ABCF1 [147] were revealed and regarded as diagnostic biomarker in pancreatitis.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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TLR5 signalling is hyper-responsive in porcine cystic fibrosis airways epithelium

Cited by 5 publications

References 18 publications

Flagellin From Pseudomonas aeruginosa Modulates SARS-CoV-2 Infectivity in Cystic Fibrosis Airway Epithelial Cells by Increasing TMPRSS2 Expression

Flagellin From Pseudomonas aeruginosa Modulates SARS-CoV-2 Infectivity in Cystic Fibrosis Airway Epithelial Cells by Increasing TMPRSS2 Expression

From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

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