Temporal Cortex Microarray Analysis Revealed Impaired Ribosomal Biogenesis and Hyperactivity of the Glutamatergic System: An Early Signature of Asymptomatic Alzheimer's Disease

Kumari, Ankita; Rahaman, Abdul; Zeng, Xin‐An; Farooq, Muhammad Adil; Huang, Ya-Lin; Yao, Runyu; Ali, Muhammad Yasir; Ishrat, Romana; Ali, Rafat

doi:10.3389/fnins.2022.966877

Cited by 7 publications

(1 citation statement)

References 145 publications

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“…Some research has suggested that CALD1 may be involved in the development of AD, although more research is needed to fully understand its role in this condition [ 16 ]. In a particular study, it was found that CALD1 expression was significantly increased in the brains of people with AD compared to those without the condition and that CALD1 may be involved in the production of amyloid beta, a protein that is believed to be a key contributor to the development of AD [ 17 , 18 ]. Some research has suggested that CALD1 may be involved in the development of periodontitis, a type of gum disease characterised by inflammation and loss of the tissue and bone that support the teeth [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

Hamarsha

Balachandran

Sailan

et al. 2023

IJMS

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The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer’s, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

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Section: Discussionmentioning

confidence: 99%

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

Hamarsha

Balachandran

Sailan

et al. 2023

IJMS

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Electroacupuncture Enhances the Functional Connectivity of Limbic System to Neocortex in the 5xFAD Mouse Model of Alzheimer's Disease

Xu,

Lin,

Wen

et al. 2024

Neuroscience

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Augmented Doubly Robust Post-Imputation Inference for Proteomic data

Moon,

Du,

Lei

et al. 2024

Preprint

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Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such data is challenging due to the large proportion of missing values. A common strategy to address this issue is to utilize an imputed dataset, which often introduces systematic bias into downstream analyses if the imputation errors are ignored. In this paper, we propose a statistical framework inspired by doubly robust estimators that offers valid and efficient inference for proteomic data. Our framework combines powerful machine learning tools, such as variational autoencoders, to augment the imputation quality with high-dimensional peptide data, and a parametric model to estimate the propensity score for debiasing imputed outcomes. Our estimator is compatible with the double machine learning framework and has provable properties. In application to both single-cell and bulk-cell proteomic data our method utilizes the imputed data to gain additional, meaningful discoveries and yet maintains good control of false positives.

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Temporal Cortex Microarray Analysis Revealed Impaired Ribosomal Biogenesis and Hyperactivity of the Glutamatergic System: An Early Signature of Asymptomatic Alzheimer's Disease

Cited by 7 publications

References 145 publications

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

Electroacupuncture Enhances the Functional Connectivity of Limbic System to Neocortex in the 5xFAD Mouse Model of Alzheimer's Disease

Augmented Doubly Robust Post-Imputation Inference for Proteomic data

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