Intracellular and Extracellular Markers of Lethality in Osteogenesis Imperfecta: A Quantitative Proteomic Approach

Abstract

Osteogenesis imperfecta (OI) is a heritable disorder that mainly affects the skeleton. The inheritance is mostly autosomal dominant and associated to mutations in one of the two genes, COL1A1 and COL1A2, encoding for the type I collagen α chains. According to more than 1500 described mutation sites and to outcome spanning from very mild cases to perinatal-lethality, OI is characterized by a wide genotype/phenotype heterogeneity. In order to identify common affected molecular-pathways and disease biomarkers in OI probands with different mutations and lethal or surviving phenotypes, primary fibroblasts from dominant OI patients, carrying COL1A1 or COL1A2 defects, were investigated by applying a Tandem Mass Tag labeling-Liquid Chromatography-Tandem Mass Spectrometry (TMT LC-MS/MS) proteomics approach and bioinformatic tools for comparative protein-abundance profiling. While no difference in α1 or α2 abundance was detected among lethal (type II) and not-lethal (type III) OI patients, 17 proteins, with key effects on matrix structure and organization, cell signaling, and cell and tissue development and differentiation, were significantly different between type II and type III OI patients. Among them, some non–collagenous extracellular matrix (ECM) proteins (e.g., decorin and fibrillin-1) and proteins modulating cytoskeleton (e.g., nestin and palladin) directly correlate to the severity of the disease. Their defective presence may define proband-failure in balancing aberrances related to mutant collagen.

Dissecting the phenotypic variability of osteogenesis imperfecta

“…Interestingly, both in vitro and in vivo data from OI animal models indicate that intracellular homeostasis and cytoskeletal organization have a role in modulating OI severity ( Forlino et al, 2007b ). These data have been confirmed in OI murine and zebrafish models as well as in cells of OI patients ( Besio et al, 2018 ; Bini et al, 2021 ; Garibaldi et al, 2021 ; Gioia et al, 2017 ). In particular, treatment with the chemical chaperone 4-phenylbutyrate (4-PBA), which reduces ER stress and UPR signaling, confirmed its role as modulator of the OI phenotype.…”

“…Interestingly, the bone tissue of a molecularly uncharacterized perinatal-lethal OI type II individual, completely lacked osteonectin, a glycoprotein highly enriched in bone and involved in initiating mineralization and promoting formation of mineral crystals ( Fedarko et al, 1995b ). More recently, reduced decorin and increased fibrillin-1 expression were reported in primary fibroblasts from individuals with lethal OI ( Bini et al, 2021 ). Bone proteome profiling of the Brtl murine model supports the hypothesis that additional alterations in ECM composition in the presence of the identical collagen mutation may contribute to the different outcomes.…”

“…Recently, proteomic analysis performed by using cell lysates of fibroblasts derived from three lethal (type II) and three non-lethal (type III) OI patients carrying glycine substitutions in either COL1A1 or COL1A2 , revealed 17 differentially expressed proteins exerting key effects on ECM structure and organization, cell signaling, as well as cell and tissue development and differentiation. Among these, the non-collagenous ECM protein decorin (DCN) was downregulated and fibrillin-1 (FBN1) was upregulated in individuals with lethal OI, whereas the cytoskeletal proteins palladin (PALLD1) and nestin (NES) were upregulated ( Bini et al, 2021 ). The altered expression of these proteins might modulate individual phenotypes.…”

Nusinersen Modulates Proteomics Profiles of Cerebrospinal Fluid in Spinal Muscular Atrophy Type 1 Patients

“…Biological Process (BP) Gene Ontology (GO) terms, annotating the differentially abundant proteins identified among T0 and T1 and control groups, were retrieved from UniProtKB ( ; 2020_06 release: 209,721,111 UniProt/TrEMBL entries) and by applying QuickGO browser [ 133 , 134 ] ( ). As previously described [ 135 ], redundancy reduction of the GO-term list was performed considering semantic similarity and by applying the clustering/summarizing algorithm of the REVIGO resource ( ; accessed on December 2020) [ 136 ]. A medium large GO list was obtained by setting the cut off value at 0.7.…”

Bioinformatics Analysis Identified miR-584-5p and Key miRNA-mRNA Networks Involved in the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells

“…Microarray techniques and bioinformatics analysis have been widely used to screen for the DEGs, functional pathways and PPI associated with osteogenic differentiation of stem cells ( Yang et al, 2019 ; Fan et al, 2020 ; Bini et al, 2021 ; Shin et al, 2021 ). In this study, three microarray datasets (total 16 samples) were downloaded from Gene Expression Omnibus (GEO) for analysis to identify DEGs and DEMs between the control group and the induction group.…”