ADAMTS4 is involved in the production of the Alzheimer disease amyloid biomarker APP669-711

Matsuzaki, Masaki; Yokoyama, Miyabishara; Yoshizawa, Yota; Kaneko, Naoki; Naito, Hiroshi; Kobayashi, Honoka; Korenaga, Akihito; Sekiya, Sadanori; Ikemura, Kentaro; Opoku, Gabriel; Hirohata, Satoshi; Iwamoto, Shinichi; Tanaka, Kōichi; Tomita, Taisuke

doi:10.1038/s41380-023-01946-y

Cited by 7 publications

(15 citation statements)

References 45 publications

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“…Six genes were found to be shared between AD and at least two immune-related diseases: ADAMTS4, JAZF1, Y_RNA, C1orf106, AC195454.1, and RP11-95M15.1. As discussed above, ADAMTS4 is responsible for amyloid deposition in AD 54,55 . JAZF1 is a protein-coding gene which functions as a transcriptional repressor; it is involved in glucose and lipid metabolisms 65 .…”

Section: Discussionmentioning

confidence: 88%

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Genetic overlap between Alzheimer’s disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

Enduru,

Fernandes,

Bahrami

et al. 2024

Mol Psychiatry

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The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroin ammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a signi cant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identi ed shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identi ed protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

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Section: Discussionmentioning

confidence: 88%

“…Its RNA is mostly expressed in oligodendrocytes, and is related to myelination. The enzyme coded by ADAMTS4 is responsible for amyloid deposition in AD 54,55 . HBEGF has been associated with AD, with its overexpression resulting in increased APP protein level 56 .…”

Section: Discussionmentioning

confidence: 99%

Genetic overlap between Alzheimer’s disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

Enduru,

Fernandes,

Bahrami

et al. 2024

Mol Psychiatry

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“…Alerted expression of MS4A2 [356], CD48 [357], HLA-DRB5 [358], FCGR2B [359], CCL5 [360], CCL3 [361], LTF (lactotransferrin) [362], GPNMB (glycoprotein nmb) [363], CTLA4 [364], TRIML2 [365], PTGDR (prostaglandin D2 receptor) [366], DRD3 [367], LHCGR (luteinizing hormone/choriogonadotropin receptor) [368], CD163 [369], PRPH (peripherin) [211], MSR1 [370], HGF (hepatocyte growth factor) [371], TTR (transthyretin) [372], IL9 [373], ADM (adrenomedullin) [374], CHI3L1 [375], CNP (2’,3’-cyclic nucleotide 3’ phosphodiesterase) [376], CDH1 [377], OLIG2 [378], KLK8 [379]. CFTR (CF transmembrane conductance regulator) [380], ABCA2 [381], HK2 [382], NGFR (nerve growth factor receptor) [383], TF (transferrin) [384], GLUL (glutamate-ammonia ligase) [324], ADAMTS4 [385], BIN1 [386], HSPA2 [387], LMNA (lamin A/C) [388], HSD11B1 [389], HTRA1 [390], APLP1 [391], MT3 [392], ADORA1 [393], DYSF (dysferlin) [394], SLC24A4 [395], RHOB (ras homolog family member B) [396], NINJ2 [397], LRP2 [398], LIPC (lipase C, hepatic type) [399], DHCR7 [400], SCD (stearoyl-CoA desaturase) [401], F11 [402], PFKL (phosphofructokinase, liver type) [403], ALDH1A1 [404], SPON1 [405] and CTNNA3 [406] are significantly associated with the Alzheimer’s Disease. CCR4 [407], CCR2 [408], CD48 [409], CD28 [410], CCL3 [411], CTLA4 [412], C6 [413], MICB (MHC class I polypeptide-related sequence B) [414], DRD3 [415], HGF (hepatocyte growth factor) [416], DIO3 [417], TTR (transthyretin) [418], GRHL3 [419], VEGFA (vascular endothelial growth factor A) [420], ADM (adrenomedull...…”

Section: Discussionmentioning

confidence: 99%

Section: Construction Of the Tf-hub Gene Regulatory Networkmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

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“…BDNF acts through the TrkB receptor, activating pathways like MAPK and PI3K, which are important for neuronal survival and plasticity [65]. It also modulates amyloid-beta (Aβ) production by enhancing alpha-secretase activity and reducing BACE1 levels, mitigating Aβ-induced toxicity [66][67][68][69][70]. Thus, exercise-induced BDNF not only offers direct neuroprotective effects but may also contribute to the reduction in AD pathology.…”

mentioning

confidence: 99%

Exercise Intervention for Alzheimer’s Disease: Unraveling Neurobiological Mechanisms and Assessing Effects

Ren,

Xiao

2023

Life

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Alzheimer’s disease (AD) is a progressive neurodegenerative disease and a major cause of age-related dementia, characterized by cognitive dysfunction and memory impairment. The underlying causes include the accumulation of beta-amyloid protein (Aβ) in the brain, abnormal phosphorylation, and aggregation of tau protein within nerve cells, as well as neuronal damage and death. Currently, there is no cure for AD with drug therapy. Non-pharmacological interventions such as exercise have been widely used to treat AD, but the specific molecular and biological mechanisms are not well understood. In this narrative review, we integrate the biology of AD and summarize the knowledge of the molecular, neural, and physiological mechanisms underlying exercise-induced improvements in AD progression. We discuss various exercise interventions used in AD and show that exercise directly or indirectly affects the brain by regulating crosstalk mechanisms between peripheral organs and the brain, including “bone–brain crosstalk”, “muscle–brain crosstalk”, and “gut–brain crosstalk”. We also summarize the potential role of artificial intelligence and neuroimaging technologies in exercise interventions for AD. We emphasize that moderate-intensity, regular, long-term exercise may improve the progression of Alzheimer’s disease through various molecular and biological pathways, with multimodal exercise providing greater benefits. Through in-depth exploration of the molecular and biological mechanisms and effects of exercise interventions in improving AD progression, this review aims to contribute to the existing knowledge base and provide insights into new therapeutic strategies for managing AD.

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ADAMTS4 is involved in the production of the Alzheimer disease amyloid biomarker APP669-711

Cited by 7 publications

References 45 publications

Genetic overlap between Alzheimer’s disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

Genetic overlap between Alzheimer’s disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Exercise Intervention for Alzheimer’s Disease: Unraveling Neurobiological Mechanisms and Assessing Effects

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