Matrix metalloproteinase-7 is increased in lung bases but not apices in idiopathic pulmonary fibrosis

Abstract

Introduction/AimIdiopathic pulmonary fibrosis (IPF) is a progressively fibrotic lung condition with poor prognosis. Matrix metalloproteinase-7 (MMP7) is a protein secreted by epithelial cells in IPF lungs. It is not known if MMP7 expression correlates with fibrotic changes in lung tissue.MethodsTissue samples from lung apices and bases were obtained from 20 IPF patients and 14 non-diseased control (NDC) donors. In formalin-fixed paraffin embedded (FFPE) sections, histological assessment of fibrosis was performed; overall MMP7 positivity was assessed by immunohistochemistry (IHC) and MMP7+ cells were quantified using multiplex IHC. Protein expression of MMP7 in whole lung lysates was quantified by western blotting. Bulk tissue transcriptomic profiles of 101 samples were analysed using RNA sequencing technologies.ResultsLung tissue from IPF bases was more fibrotic than in apices. MMP7 protein is elevated in IPF lung base tissue. In IPF whole lung lysates, MMP7 protein levels are increased compared to NDC donors and was increased in IPF lung bases compared to apices. MMP7 protein levels correlated with MMP7 gene expression levels in lung tissue. MMP7 transcript levels were increased in IPF base compared to NDC base lung tissue and increased in IPF base tissue compared to IPF apex tissue.ConclusionsOur cross-sectional study suggests that lung epithelial MMP7 expression increases as the tissue becomes more fibrotic and identifies a potentially non-epithelial or immune cell source. Mechanisms of disease progression in IPF are still unclear and our study suggests aberrant MMP7 production may be a histological starting point of lung tissue fibrosis.

Multi‐omics integration reveals a nonlinear signature that precedes progression of lung fibrosis

“…Given the importance of the transcriptomics layer in MOFA‐discovered Factor1 and Factor2, we evaluated the predictive performance of their top genes using machine learning (ML). To ensure accurate ML model training and performance estimation, the presented transcriptomics dataset was integrated with a larger published dataset from the same patient cohort 21 . Four ML models were trained and compared using a leave‐one‐group‐out cross‐validation (LOGCV) approach.…”

“…74 Annotated proteincoding genes were retained and low expression genes filtered using the filterByExpr function from edgeR v3.36.0 before normalisation with vsn R package v3.62.0. For integrated analysis with previous datasets, raw fastq files were downloaded from the SRA from Jaffar et al (GSE213001), 21 Luzina et al (GSE99621) 15 and McDonough et al (GSE124685). 16 Files were processed using the above pipeline, including batch effect removal using ComBat-seq 75 with study as the batch effect and disease (NDC or IPF) as a biological condition.…”

“…The top 50 genes with the highest weights in Factor1 and Factor2 were used as predictor variables for IPF and NDC, and early (IPF apex) and late-stage disease (IPF base) respectively. To increase the sample size for model training and performance evaluation, Jaffar et al's previously published dataset 21 was included. Validation and performance estimations of four models were performed using a leave-group-out cross-validation approach using the caret R package, in which the data were folded 509 into 85% training data and 15% validation data.…”

Construction and evaluation of prognostic model of genes related to cell burial in idiopathic pulmonary fibrosis (IPF)

“…Among the top 10 upregulated genes, some of them have been con rmed to be consistent with the results of this analysis, and some of them have not been studied, indicating that these genes may be new research targets for the pathogenesis of IPF. For example, SPP1 is up-regulated in the lower lung of IPF (late IPF) [27], CYTL1 has the function of promoting brosis and angiogenesis [28, 29], MMP7 promotes brosis [30,31], and CAMP can inhibit IPF brosis [32]. After crossing DEGs with ECRGs, 18 IPF DEECRGs were obtained.…”

Serum levels of matrix metalloproteinases 1, 2, and 7, and their tissue inhibitors 1, 2, 3, and 4 in polytraumatized patients: Time trajectories, correlations, and their ability to predict mortality

“…Like most MMPs, MMP1, MMP2, and MMP7 are synthesized and secreted as inactive zymogens [24][25][26], in which a propeptide domain blocks the active site [27] requiring proteolytic cleavage under physiological conditions to promote the release of the propeptide domain bound to the catalytic site and generate active MMPs [28]. The proteolytic activity of MMPs during tissue remodeling is mainly regulated by four endogenous proteins known as tissue inhibitors of metalloproteinases TIMP1, TIMP2, TIMP3, and TIMP4 [29,30], which bind tightly to the MMP active site [31], forming 1:1 stoichiometric complexes [32].…”