Upregulation of ribosome complexes at the blood-brain barrier in Alzheimer's disease patients

Suzuki, Masayoshi; Tezuka, Kenta; Handa, Takumi; Sato, Rei; Takeuchi, Hina; Takao, Masaki; Tano, Mitsutoshi; Uchida, Yasuo

doi:10.1177/0271678x221111602

Cited by 34 publications

(30 citation statements)

References 86 publications

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“…In the prediction results of Alzheimer’s disease, 18 genes (90%) have been related to reference databases and literature evidence. Among the two newly predicted candidate genes, the latest research [ 50 ] shows that the RPL11 gene is significantly up-regulated in Alzheimer patients. As a tumor invasion-enhancing gene, the ANXA4 gene can promote trophoblast invasion in preeclampsia patients through PI3K/Akt/eNOS pathway [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Predicting disease genes based on multi-head attention fusion

Zhang

et al. 2023

BMC Bioinformatics

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Background The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. Results This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. Conclusions The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

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Section: Resultsmentioning

confidence: 99%

Predicting disease genes based on multi-head attention fusion

Zhang

et al. 2023

BMC Bioinformatics

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“…The mitochondrial chaperone Hspe1 and TCA enzymes, 6-phosphogluconate dehydrogenase (PGD) and isocitrate dehydrogenase (IDH3B) have altered expression in postmortem AD subjects compared to healthy controls [63–65]. AD patient brain capillaries and blood respectively have elevated levels of ER translocon protein SSR4 and Y chromosome linked protein DDX3Y [66, 67]. Similarly, CLIP-associating protein 1 (CLASP1) and cAMP-dependent protein kinase II-β regulatory subunit (PRKAR2B) gene expression is elevated in APP/PS1 mouse brains [68].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Amyloid fibril proteomics of AD brains reveals modifiers of aggregation and toxicity

Upadhyay

Chhangani

Rao

et al. 2023

Preprint

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BackgroundThe accumulation of amyloid beta (Aβ) peptides in fibrils is prerequisite for Alzheimer’s disease (AD). Our understanding of the proteins that promote Aβ fibril formation and mediate neurotoxicity has been limited due to technical challenges in isolating pure amyloid fibrils from brain extracts.MethodsTo investigate how amyloid fibrils form and cause neurotoxicity in AD brain, we developed a robust biochemical strategy. We benchmarked the success of our purifications using electron microscopy, amyloid dyes, and a large panel of Aβ immunoassays. Tandem mass-spectrometry based proteomic analysis workflows provided quantitative measures of the amyloid fibril proteome. These methods allowed us to compare amyloid fibril composition from human AD brains, three amyloid mouse models, transgenic Aβ42 flies, and Aβ42 seeded cultured neurons.ResultsAmyloid fibrils are primarily composed by Aβ42 and unexpectedly harbor Aβ38 but generally lack Aβ40 peptides. Multidimensional quantitative proteomics allowed us to redefine the fibril proteome by identifying 17 new amyloid-associated proteins. Notably, we confirmed 126 previously reported plaque-associated proteins. We validated a panel of these proteins as bona fide amyloid-interacting proteins using antibodies and orthogonal proteomic analysis. One metal-binding chaperone metallothionein-3 is tightly associated with amyloid fibrils and modulates fibril formationin vitro.Lastly, we used a transgenic Aβ42 fly model to test if knock down or over-expression of fibril-interacting gene homologues modifies neurotoxicity. Eight RNAi lines suppressed and 11 enhanced Aβ42 toxicity.ConclusionsThese discoveries and subsequent confirmation indicate that fibril-associated proteins play a key role in amyloid formation and AD pathology.

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“…However, in this research, the ribosomal protein expression found in the AD samples was elevated, we speculate that the increase was not due to age, but due to the pathological factors of AD. Masayoshi et.al [51] by employing comprehensive and accurate quantitative proteomics in Alzheimer's disease (AD)to explore the AD molecular mechanism. The results indicate that of the 29 ribosomal proteins that were quanti ed, 28 (RPLP0, RPL4, RPL6, RPL7A, RPL8, RPL10A, RPL11, RPL12, RPL14, RPL15, RPL18, RPL23, RPL27, RPL27A, RPL31, RPL35A, RPS2, RPS3, RPS3A, RPS4X, RPS7, RPS8, RPS14, RPS16, RPS20, RPS24, RPS25 and RPSA) were signi cantly upregulated in AD patients.…”

Section: Ribosomal Proteins and Alzheimer's Diseasementioning

confidence: 99%

“…This is in preliminary agreement with our experimental results. Therefore, they put forward that the increase of ribosome function is a common phenomenon in Alzheimer's disease and drug treatment of the disease is positively correlated with the inhibition of ribosome biosynthesis [51]. Especially, Donepezil, which is clinically used AD drugs, has also been reported to inhibit ribosome biosynthesis [52].…”

Section: Ribosomal Proteins and Alzheimer's Diseasementioning

confidence: 99%

Proteomics reveals ribosome associated proteins as potential biomarkers of Alzheimer's disease

Feng

Cui

Sun

et al. 2023

Preprint

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Accumulated evidences confirmed that amyloid-β (Aβ) deposition, tau phosphorylation, acetylcholine decrease, etc, all contributed to the pathogenesis of Alzheimer's disease (AD). However, the underlying mechanism remains unclear, and novel AD biomarkers are urgently needed to be discovered. Herein, APP/PS1 double transgenic mice (AD mice) were employed, and the differentially expressed (DE) proteins of mice hippocampus were identified and analyzed by 4D label Free quantitative proteomics technology and parallel reaction monitoring (PRM). The results indicated that the hippocampus of AD mice showed significant Aβ deposition. Compared to that of wild type mice, 29 proteins were up-regulated and 25 proteins were down-regulated in the AD mice group. Gene Ontology (GO) enrichment analysis of BP showed that DE proteins were mainly enriched in Ribosomal large subunit biogenesis. MF results showed that DE proteins were mainly enriched in 5.8S rRNA binding and Structural constituent of ribosome. CC results showed that DE proteins were mainly enriched in Polysomal ribosome, Cytosolic large ribosomal subunit, Cytosolic ribosome, Large ribosomal subunit, etc. KEGG results showed that DE proteins were mainly enriched in the Ribosome signaling pathway. The main target proteins were Rpl18, Rpl17, Rpl19, Rpl24, Rpl35, and Rpl6, which all showed significant differentially expression compared with the wild type mice. The PRM verification results were consistent with the results of 4D Label-free quantitative proteomics. Taken together, our findings revealed that Rpl18, Rpl17, Rpl19, Rpl24, Rpl35 and Rpl6 as AD biomarkers may be the potential targets for drugs design.

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Upregulation of ribosome complexes at the blood-brain barrier in Alzheimer's disease patients

Cited by 34 publications

References 86 publications

Predicting disease genes based on multi-head attention fusion

Predicting disease genes based on multi-head attention fusion

Amyloid fibril proteomics of AD brains reveals modifiers of aggregation and toxicity

Proteomics reveals ribosome associated proteins as potential biomarkers of Alzheimer's disease

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