Imbalanced cellular metabolism compromises cartilage homeostasis and joint function in a mouse model of mucolipidosis type III gamma

Profiling native pulmonary basement membrane stiffness using atomic force microscopy

Mammalian cells sense and react to the mechanics of their immediate microenvironment. Therefore, the characterization of the biomechanical properties of tissues with high spatial resolution provides valuable insights into a broad variety of developmental, homeostatic and pathological processes within living organisms. The biomechanical properties of the basement membrane (BM), an extracellular matrix (ECM) substructure measuring only ∼100-400 nm across, are, among other things, pivotal to tumor progression and metastasis formation. Although the precise assignment of the Young's modulus E of such a thin ECM substructure especially in between two cell layers is still challenging, biomechanical data of the BM can provide information of eminent diagnostic potential. Here we present a detailed protocol to quantify the elastic modulus of the BM in murine and human lung tissue, which is one of the major organs prone to metastasis. This protocol describes a streamlined workflow to determine the Young's modulus E of the BM between the endothelial and epithelial cell layers shaping the alveolar wall in lung tissues using atomic force microscopy (AFM). Our step-by-step protocol provides instructions for murine and human lung tissue extraction, inflation of these tissues with cryogenic cutting medium, freezing and cryosectioning of the tissue samples, and AFM force-map recording. In addition, it guides the reader through a semi-automatic data analysis procedure to identify the pulmonary BM and extract its Young's modulus E using an in-house tailored user-friendly AFM data analysis software, the Center for Applied Tissue Engineering and Regenerative Medicine processing toolbox, which enables automatic loading of the recorded force maps, conversion of the force versus piezo-extension curves to force versus indentation curves, calculation of Young's moduli and generation of Young's modulus maps, where the pulmonary BM can be identified using a semi-automatic spatial filtering tool. The entire protocol takes 1-2 d.

Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression

Objective

Hypertrophic cardiomyopathy (HCM) is a complex heterogeneous heart disease. Recent reports found that long non-coding RNAs (lncRNAs) play an important role in the progression of cardiovascular diseases. The present study aimed to identify the novel lncRNAs and messenger RNAs (mRNAs) and determine the key pathways involved in HCM.

Methods

The lncRNA and mRNA sequencing datasets of GSE68316 and GSE130036 were downloaded from the Gene Expression Omnibus (GEO) database. An integrated co-expression network analysis was conducted to identify differentially expressed lncRNAs and differentially expressed mRNAs in patients with HCM. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were explored to identify the biological functions and signaling pathways of the co-expression network. Protein–protein interaction (PPI) and hub gene networks were constructed by using Cytoscape software. Plasma samples of patients with HCM and the GSE89714 dataset were used to validate the bioinformatics results.

Results

A total of 1,426 differentially expressed long non-coding RNAs (lncRNAs) and 1,715 differentially expressed mRNAs were obtained from GSE68316, of which 965 lncRNAs and 896 mRNAs were upregulated and 461 lncRNAs and 819 mRNAs were downregulated. A total of 469 differentially expressed lncRNAs and 2,407 differentially expressed mRNAs were screened from GSE130036, of which 183 lncRNAs and 1,283 mRNAs were upregulated and 286 lncRNAs and 1,124 mRNAs were downregulated. A co-expression network was constructed and contained 30 differentially expressed lncRNAs and 63 differentially expressed mRNAs, which were primarily involved in ‘G-protein beta/gamma-subunit complex binding,' ‘polyubiquitin modification-dependent protein binding,' ‘Apelin signaling pathway,' and ‘Wnt signaling pathway.' The 10 hub genes in the upregulated network [G Protein Subunit Alpha I2 (GNAI2), G Protein Subunit Alpha I1 (GNAI1), G Protein Subunit Alpha I3 (GNAI3), G Protein Subunit Gamma 2 (GNG2), G Protein Subunit Beta 1 (GNB1), G Protein Subunit Gamma 13 (GNG13), G Protein Subunit Gamma Transducin 1 (GNGT1), G Protein Subunit Gamma 12 (GNG12), AKT Serine/Threonine Kinase 1 (AKT1) and GNAS Complex Locus (GNAS)] and the 10 hub genes in the downregulated network [Nucleotide-Binding Oligomerization Domain Containing Protein 2 (NOD2), Receptor-Interacting Serine/Threonine Kinase 2 (RIPK2), Nucleotide-Binding Oligomerization Domain Containing Protein 1 (NOD1), Mitochondrial Antiviral Signaling Protein (MAVS), Autophagy Related 16-Like 1 (ATG16L1), Interferon Induced With Helicase C Domain 1 (IFIH1), Autophagy Related 5 (ATG5), TANK-Binding Kinase 1 (TBK1), Caspase Recruitment Domain Family Member 9 (CARD9), and von Willebrand factor (VWF)] were screened using cytoHubba. The expression of LA16c-312E8.2 and RP5-1160K1.3 in the plasma of patients with HCM was elevated, and the expression of the MIR22 host gene (MIR22HG) was decreased, which was consistent with our analysis, while the expression of LINC00324 and Small Nucleolar RNA Host Gene 12 (SNHG12) was not significantly different between the two groups. Verification analyses performed on GSE89714 showed the upregulated mRNAs of Chloride Voltage-Gated Channel 7 (CLCN7), N-Acetylglucosamine-1-Phosphate Transferase Subunit Gamma (GNPTG), Unk Like Zinc Finger (UNKL), Adenosine Monophosphate Deaminase 2 (AMPD2), GNAI3, WD Repeat Domain 81 (WDR81), and Serpin Family F Member 1 (SERPINF1) and downregulated mRNAs of TATA-Box Binding Protein Associated Factor 12 (TAF12) co-expressed with five crucial lncRNAs. Moreover, GNAI2, GNAI3, GNG12, and vWF were upregulated and GNAS was downregulated in the top 10 hub genes of upregulated and downregulated PPI networks.

Conclusion

These findings from integrative biological analysis of lncRNA-mRNA co-expression networks explored the key genes and pathways and provide new insights into the understanding of the mechanism and discovering new therapeutic targets for HCM. Three differentially expressed pivotal lncRNAs (LA16c-312E8.2, RP5-1160K1.3, and MIR22HG) in the co-expression network may serve as biomarkers and intervention targets for the diagnosis and treatment of HCM.

Pathogenic variants in GNPTAB and GNPTG encoding distinct subunits of GlcNAc-1-phosphotransferase differentially impact bone resorption in patients with mucolipidosis type II and III

Purpose

Pathogenic variants in GNPTAB and GNPTG, encoding different subunits of GlcNAc-1-phosphotransferase, cause mucolipidosis (ML) II, MLIII alpha/beta, and MLIII gamma. This study aimed to investigate the cellular and molecular bases underlying skeletal abnormalities in patients with MLII and MLIII.

Methods

We analyzed bone biopsies from patients with MLIII alpha/beta or MLIII gamma by undecalcified histology and histomorphometry. The skeletal status of Gnptg^koand Gnptab-deficient mice was determined and complemented by biochemical analysis of primary Gnptg^ko bone cells. The clinical relevance of the mouse data was underscored by systematic urinary collagen crosslinks quantification in patients with MLII, MLIII alpha/beta, and MLIII gamma.

Results

The analysis of iliac crest biopsies revealed that bone remodeling is impaired in patients with GNPTAB-associated MLIII alpha/beta but not with GNPTG-associated MLIII gamma. Opposed to Gnptab-deficient mice, skeletal remodeling is not affected in Gnptg^ko mice. Most importantly, patients with variants in GNPTAB but not in GNPTG exhibited increased bone resorption.

Conclusion

The gene-specific impact on bone remodeling in human individuals and in mice proposes distinct molecular functions of the GlcNAc-1-phosphotransferase subunits in bone cells. We therefore appeal for the necessity to classify MLIII based on genetic in addition to clinical criteria to ensure appropriate therapy.

First person – Lena Marie Westermann

First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Lena Marie Westermann is first author on ‘Imbalanced cellular metabolism compromises cartilage homeostasis and joint function in a mouse model of mucolipidosis type III gamma’, published in DMM. Lena Marie is an MD student in the lab of Dr Sandra Pohl at University Medical Center Hamburg-Eppendorf, Hamburg, Germany, investigating bone and connective tissue phenotypes in lysosomal storage disorders.