Regulation of the HTRA2 Protease Activity by an Inhibitory Antibody-Derived Peptide Ligand and the Influence on HTRA2-Specific Protein Interaction Networks in Retinal Tissues

The mitochondrial serine protease HTRA2 has many versatile biological functions ranging from being an important regulator of apoptosis to being an essential component for neuronal cell survival and mitochondrial homeostasis. Loss of HTRA2 protease function is known to cause neurodegeneration, whereas overactivation of its proteolytic function is associated with cell death and inflammation. In accordance with this, our group verified in a recent study that the synthetic peptide ASGYTFTNYGLSWVR, encoding the hypervariable sequence part of an antibody, showed a high affinity for the target protein HTRA2 and triggered neuroprotection in an in vitro organ culture model for glaucoma. To unravel this neuroprotective mechanism, the present study showed for the first time that the synthetic CDR1 peptide significantly (p < 0.01) inhibited the proteolytic activity of HTRA2 up to 50% using a specific protease function assay. Furthermore, using state-of-the-art co-immunoprecipitation technologies in combination with highresolution MS, we identified 50 significant protein interaction partners of HTRA2 in the retina of house swine (p < 0.01; log2 fold change > 1.5). Interestingly, 72% of the HTRA2-specific interactions (23 of 31 binders) were inhibited by additional treatment with UCF-101 (HTRA2 protease inhibitor) or the synthetic CDR peptide. On the other hand, the remaining 19 binders of HTRA2 were exclusively identified in the UCF101 and/or CDR group. However, many of the interactors were involved in the ER to Golgi anterograde transport (e.g., AP3D1), aggrephagy (e.g., PSMC1), and the pyruvate metabolism/citric acid cycle (e.g., SHMT2), and illustrated the complex protein interaction networks of HTRA2 in neurological tissues. In conclusion, the present study provides, for the first time, a comprehensive protein catalogue of HTRA2-specific interaction partners in the retina, and will serve as reference map in the future for studies focusing on HTRA2mediated neurodegeneration.

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An in vitro microfluidic approach to generating protein-interaction networks

Nature Methods ◽

10.1038/nmeth.1289 ◽

2008 ◽

Vol 6 (1) ◽

pp. 71-74 ◽

Cited By ~ 110

Author(s):

Doron Gerber ◽

Sebastian J Maerkl ◽

Stephen R Quake

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks

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Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Cancer Cell International ◽

10.1186/s12935-021-02308-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fu-peng Ding ◽

Jia-yi Tian ◽

Jing Wu ◽

Dong-feng Han ◽

Ding Zhao

Keyword(s):

Clinical Significance ◽

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Support Vector ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

Key Genes

Abstract Background Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways. Results Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition. Conclusion We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

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