Exploring the interaction between T-cell antigen receptor-related genes and MAPT or ACHE using integrated bioinformatics analysis

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Abstract

Alzheimer's disease (AD) is a neurodegenerative disease that primarily occurs in elderly individuals with cognitive impairment. Although extracellular β-amyloid (Aβ) accumulation and tau protein hyperphosphorylation are considered to be leading causes of AD, the molecular mechanism of AD remains unknown. Therefore, in this study, we aimed to explore potential biomarkers of AD. Next-generation sequencing (NGS) datasets, GSE173955 and GSE203206, were collected from the Gene Expression Omnibus (GEO) database. Analysis of differentially expressed genes (DEGs), gene ontology (GO) functional enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein networks were performed to identify genes that are potentially associated with AD. Analysis of the DEG based protein-protein interaction (PPI) network using Cytoscape indicated that neuroinflammation and T-cell antigen receptor (TCR)-associated genes ( LCK, ZAP70, and CD44) were the top three hub genes. Next, we validated these three hub genes in the AD database and utilized two machine learning models from different AD datasets (GSE15222) to observe their general relationship with AD. Analysis using the random forest classifier indicated that accuracy (78%) observed using the top three genes as inputs differed only slightly from that (84%) observed using all genes as inputs. Furthermore, another data set, GSE97760, which was analyzed using our novel eigenvalue decomposition method, indicated that the top three hub genes may be involved in tauopathies associated with AD, rather than Aβ pathology. In addition, protein-protein docking simulation revealed that the top hub genes could form stable binding sites with acetylcholinesterase (ACHE). This suggests a potential interaction between hub genes and ACHE, which plays an essential role in the development of anti-AD drug design. Overall, the findings of this study, which systematically analyzed several AD datasets, illustrated that LCK, ZAP70, and CD44 may be used as AD biomarkers. We also established a robust prediction model for classifying patients with AD.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Random Forests

Leo Breiman (2001)

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Author and article information

Contributors

Wenbo Guo: URI : http://loop.frontiersin.org/people/2083500/overview

Xun Gou: URI : http://loop.frontiersin.org/people/2226667/overview

Qi Zhang: URI : http://loop.frontiersin.org/people/1993427/overview

Ping Yang: URI : http://loop.frontiersin.org/people/2108447/overview

Minghui Pang: URI : http://loop.frontiersin.org/people/2213373/overview

Chaoyang Pang: URI : http://loop.frontiersin.org/people/2096530/overview

XiaoYu Zhang: URI : http://loop.frontiersin.org/people/728506/overview

Journal

Journal ID (nlm-ta): Front Neurol

Journal ID (iso-abbrev): Front Neurol

Journal ID (publisher-id): Front. Neurol.

Title: Frontiers in Neurology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-2295

Publication date (Electronic): 28 March 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 1129470

Affiliations

[1] ¹College of Computer Science, Sichuan Normal University , Chengdu, China

[2] ²College of Life Science, Sichuan Normal University , Chengdu, China

[3] ³College of Mathematics and Physics, Chengdu University of Technology , Chengdu, China

[4] ⁴West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University , Chengdu, China

[5] ⁵National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China , Chengdu, China

Author notes

Edited by: Xuefei Song, Shanghai Ninth People's Hospital, China

Reviewed by: Sen Qiao, Northwest Women's and Children's Hospital, China; Ricardo Vialle, Rush University, United States

*Correspondence: Chaoyang Pang cypang@ 123456sicnu.edu.cn

Yanyun Wei yywei@ 123456uestc.edu.cn

XiaoYu Zhang zhangxy2005@ 123456126.com

This article was submitted to Neurological Biomarkers, a section of the journal Frontiers in Neurology

†These authors have contributed equally to this work and share first authorship

Article

DOI: 10.3389/fneur.2023.1129470

PMC ID: 10086260

PubMed ID: 37056359

SO-VID: d21f7a18-26c9-4586-a907-09caf5ce4306

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 17 January 2023

Date accepted : 10 March 2023

Page count

Figures: 7, Tables: 3, Equations: 4, References: 70, Pages: 14, Words: 9387

Funding

Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;

Award ID: 61988102

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61988102).

Exploring the interaction between T-cell antigen receptor-related genes and MAPT or ACHE using integrated bioinformatics analysis

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Abstract

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Alternative approaches to Alzheimer's Disease

Most cited references 68

limma powers differential expression analyses for RNA-sequencing and microarray studies

Highly accurate protein structure prediction with AlphaFold

Random Forests

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