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1.
Neuroscience ; 544: 64-74, 2024 Apr 19.
Article in English | MEDLINE | ID: mdl-38458535

ABSTRACT

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.


Subject(s)
Parkinson Disease , Humans , Parkinson Disease/genetics , Histones , Algorithms , Area Under Curve , Brain
2.
Mol Diagn Ther ; 28(2): 225-235, 2024 Mar.
Article in English | MEDLINE | ID: mdl-38341835

ABSTRACT

BACKGROUND: The effects of genes on the development of intracranial aneurysms (IAs) remain to be elucidated, and reliable blood biomarkers for diagnosing IAs are yet to be established. This study aimed to identify genes associated with IAs pathogenesis and explore their diagnostic value by analyzing IAs datasets, conducting vascular smooth muscle cells (VSMC) experiments, and performing blood detection. METHODS: IAs datasets were collected and the differentially expressed genes were analyzed. The selected genes were validated in external datasets. Autophagy was induced in VSMC and the effect of selected genes was determined. The diagnostic value of selected gene on the IAs were explored using area under curve (AUC) analysis using IAs plasma samples. RESULTS: Analysis of 61 samples (32 controls and 29 IAs tissues) revealed a significant increase in expression of ADORA3 compared with normal tissues using empirical Bayes methods of "limma" package; this was further validated by two external datasets. Additionally, induction of autophagy in VSMC lead to upregulation of ADORA3. Conversely, silencing ADORA3 suppressed VSMC proliferation and autophagy. Furthermore, analysis of an IAs blood sample dataset and clinical plasma samples demonstrated increased ADORA3 expression in patients with IA compared with normal subjects. The diagnostic value of blood ADORA3 expression in IAs was moderate when analyzing clinical samples (AUC: 0.756). Combining ADORA3 with IL2RB or CCR7 further enhanced the diagnostic ability for IAs, with the AUC value over 0.83. CONCLUSIONS: High expression of ADORA3 is associated with IAs pathogenesis, likely through its promotion of VSMC autophagy. Furthermore, blood ADORA3 levels have the potential to serve as an auxiliary diagnostic biomarker for IAs.


Subject(s)
Intracranial Aneurysm , Humans , Intracranial Aneurysm/diagnosis , Intracranial Aneurysm/genetics , Intracranial Aneurysm/pathology , Bayes Theorem , Gene Expression Profiling , Transcriptome , Biomarkers
3.
Front Aging Neurosci ; 12: 605961, 2020.
Article in English | MEDLINE | ID: mdl-33324198

ABSTRACT

Background: The pathogenesis of Alzheimer's disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways. Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed. Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls. Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.

5.
Comput Biol Med ; 41(4): 221-7, 2011 Apr.
Article in English | MEDLINE | ID: mdl-21396631

ABSTRACT

In order to predict variations of drug concentration during a given period of time, numerical solutions of pharmacokinetic models need to be obtained efficiently. Analytical solutions of linear pharmacokinetic models are usually obtained using the Laplace transform and inverse Laplace tables. The derivations of solutions to complex nonlinear models are tedious, and such solution process may be difficult to implement as a robust software code. For nonlinear models, the fourth-order Runge-Kutta (RK4) is the most classical numerical method in obtaining approximate numerical solutions, which is impossible to be implemented in distributed computing environments without much modification. The reason is that numerical solutions obtained by using RK4 can only be computed in sequential time steps. In this paper, time-domain decomposition methods are adapted for nonlinear pharmacokinetic models. The numerical Inverse Laplace method for PharmacoKinetic models (ILPK) is implemented to solve pharmacokinetic models with iterative inverse Laplace transform in each time interval. The distributed ILPK algorithm, which is based on a two-level time-domain decomposition concept, is proposed to improve its efficiency. Solutions on the coarser temporal mesh at the top level are obtained one by one, and then those on the finer temporal mesh at the bottom level are calculated concurrently by using those initial solutions that have been obtained at the top level decomposition. Accuracy and efficiency of the proposed algorithm and its distributed equivalent are investigated by using several test models. Results indicate that the ILPK algorithm and its distributed equivalent are good candidates for both linear and nonlinear pharmacokinetic models.


Subject(s)
Algorithms , Computer Simulation , Models, Biological , Pharmacokinetics , Software , Animals , Humans
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