Neuron-specific gene NSG1 binds to and positively regulates sortilin ectodomain shedding via a metalloproteinase-dependent mechanism.

Increasing evidence suggests that aberrant regulation of sortilin ectodomain shedding can contribute to amyloid-ß pathology and frontotemporal dementia, although the mechanism by which this occurs has not been elucidated. Here, we probed for novel binding partners of sortilin using multiple and complementary approaches and identified two proteins of the neuron-specific gene (NSG) family, NSG1 and NSG2, that physically interact and colocalize with sortilin. We show both NSG1 and NSG2 induce subcellular redistribution of sortilin to NSG1- and NSG2-enriched compartments. However, using cell surface biotinylation, we found only NSG1 reduced sortilin cell surface expression, which caused significant reductions in uptake of progranulin, a molecular determinant for frontotemporal dementia. In contrast, we demonstrate NSG2 has no effect on sortilin cell surface abundance or progranulin uptake, suggesting specificity for NSG1 in the regulation of sortilin cell surface expression. Using metalloproteinase inhibitors and A disintegrin and metalloproteinase 10 KO cells, we further show that NSG1-dependent reduction of cell surface sortilin occurred via proteolytic processing by A disintegrin and metalloproteinase 10 with a concomitant increase in shedding of sortilin ectodomain to the extracellular space. This represents a novel regulatory mechanism for sortilin ectodomain shedding that is regulated in a neuron-specific manner. Furthermore, this finding has implications for the development of strategies for brain-specific regulation of sortilin and possibly sortilin-driven pathologies.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1ß-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

mRNA expression of pattern recognition receptors and their signaling mediators in healthy and diseased gingival tissues.

BACKGROUND: Gingivitis and periodontitis are initiated by inflammation caused by microorganisms. Pathogen-associated molecular patterns (PAMPs) from these microorganisms are recognized through various toll-like receptors (TLRs) and NOD-like receptors (NLRs). In this study, we have chosen five TLRs and two NLRs as representatives taking part in the recognition and inflammation process, along with a few of their signaling mediators including CD14, MYD88, and TRIF to compare their mRNA expression levels between healthy and diseased gingival tissues. This will provide deeper insight into the mechanisms underlying gingivitis and periodontitis. Understanding the mechanisms involved in the onset and progression of the periodontal diseases could greatly help in establishing effective ways for prevention and treatment of these diseases besides decreasing the risk factor for relevant systemic disorders. MATERIALS AND METHODS: Gingival tissue samples for mRNA extraction and cDNA synthesis were taken from patients with gingivitis and periodontitis and from healthy control subjects. Messenger RNA expression of all genes was assessed using real-time polymerase chain reaction (PCR). RESULTS: Among the genes studied in different groups, only MYD88 mRNA expression was significantly higher in the periodontitis group compared to that of the controls. The expression level of this molecule was also significantly higher in patients with severe periodontitis compared to other patients and also compared to healthy individuals. In different tissues, positive significant correlations were observed between the mRNA expression levels of some genes. CONCLUSIONS: Elevated mRNA levels of MYD88 in periodontitis might have a key role in the pathogenesis of this disease. Therefore, MYD88 may be a useful target for the therapy of this disease.