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Nashiry MA, Sumi SS, Alyami SA, Moni MA. Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome. Front Mol Neurosci 2023; 16:1232805. [PMID: 37654790 PMCID: PMC10466791 DOI: 10.3389/fnmol.2023.1232805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/12/2023] [Indexed: 09/02/2023] Open
Abstract
Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.
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Affiliation(s)
- Md Asif Nashiry
- Data Analytics, Northern Alberta Institute of Technology, Edmonton, AB, Canada
| | - Shauli Sarmin Sumi
- Computer Science and Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Salem A. Alyami
- Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mohammad Ali Moni
- Artificial Intelligence and Data Science, Faculty of Health and Behavioural Sciences, School of Health and Rehabilitation Sciences, The University of Queensland, Saint Lucia, QLD, Australia
- Artificial Intelligence and Cyber Futures Institute, Charles Stuart University, Bathurst, NSW, Australia
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Liu ZY, Liu F, Cao Y, Peng SL, Pan HW, Hong XQ, Zheng PF. ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction. Aging (Albany NY) 2023; 15:1394-1411. [PMID: 36863716 PMCID: PMC10042701 DOI: 10.18632/aging.204542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/13/2023] [Indexed: 03/04/2023]
Abstract
Lipid metabolism plays an essential role in the genesis and progress of acute myocardial infarction (AMI). Herein, we identified and verified latent lipid-related genes involved in AMI by bioinformatic analysis. Lipid-related differentially expressed genes (DEGs) involved in AMI were identified using the GSE66360 dataset from the Gene Expression Omnibus (GEO) database and R software packages. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to analyze lipid-related DEGs. Lipid-related genes were identified by two machine learning techniques: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The receiver operating characteristic (ROC) curves were used to descript diagnostic accuracy. Furthermore, blood samples were collected from AMI patients and healthy individuals, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the RNA levels of four lipid-related DEGs. Fifty lipid-related DEGs were identified, 28 upregulated and 22 downregulated. Several enrichment terms related to lipid metabolism were found by GO and KEGG enrichment analyses. After LASSO and SVM-RFE screening, four genes (ACSL1, CH25H, GPCPD1, and PLA2G12A) were identified as potential diagnostic biomarkers for AMI. Moreover, the RT-qPCR analysis indicated that the expression levels of four DEGs in AMI patients and healthy individuals were consistent with bioinformatics analysis results. The validation of clinical samples suggested that 4 lipid-related DEGs are expected to be diagnostic markers for AMI and provide new targets for lipid therapy of AMI.
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Affiliation(s)
- Zheng-Yu Liu
- Department of Cardiology, Hunan Provincial People's Hospital, Changsha 410000, China
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
| | - Fen Liu
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
- The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha 410000, China
| | - Yan Cao
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
- Department of Emergency, Hunan Provincial People's Hospital, Changsha 410000, China
| | - Shao-Liang Peng
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Data Center, Hunan Provincial People's Hospital, Changsha 410000, China
| | - Hong-Wei Pan
- Department of Cardiology, Hunan Provincial People's Hospital, Changsha 410000, China
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
| | - Xiu-Qin Hong
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
- The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha 410000, China
| | - Peng-Fei Zheng
- Department of Cardiology, Hunan Provincial People's Hospital, Changsha 410000, China
- Department of Epidemiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410000, China
- Clinical Medicine Research Center of Heart Failure of Hunan Province, Changsha 410000, China
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Zhang C, Ma Z, Nan X, Wang W, Zeng X, Chen J, Cai Z, Wang J. Comprehensive analysis to identify the influences of SARS-CoV-2 infections to inflammatory bowel disease. Front Immunol 2023; 14:1024041. [PMID: 36817436 PMCID: PMC9936160 DOI: 10.3389/fimmu.2023.1024041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) and inflammatory bowel disease (IBD) are both caused by a disordered immune response and have direct and profound impacts on health care services. In this study, we implemented transcriptomic and single-cell analysis to detect common molecular and cellular intersections between COVID-19 and IBD that help understand the linkage of COVID-19 to the IBD patients. Methods Four RNA-sequencing datasets (GSE147507, GSE126124, GSE9686 and GSE36807) from Gene Expression Omnibus (GEO) database are extracted to detect mutual differentially expressed genes (DEGs) for IBD patients with the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to find shared pathways, candidate drugs, hub genes and regulatory networks. Two single-cell RNA sequencing (scRNA-eq) datasets (GSE150728, PRJCA003980) are used to analyze the immune characteristics of hub genes and the proportion of immune cell types, so as to find common immune responses between COVID-19 and IBD. Results A total of 121 common DEGs were identified among four RNA-seq datasets, and were all involved in the functional enrichment analysis related to inflammation and immune response. Transcription factors-DEGs interactions, miRNAs-DEGs coregulatory networks, and protein-drug interactions were identified based on these datasets. Protein-protein interactions (PPIs) was built and 59 hub genes were identified. Moreover, scRNA-seq of peripheral blood monocyte cells (PBMCs) from COVID-19 patients revealed a significant increase in the proportion of CD14+ monocytes, in which 38 of 59 hub genes were highly enriched. These genes, encoding inflammatory cytokines, were also highly expressed in inflammatory macrophages (IMacrophage) of intestinal tissues of IBD patients. Conclusions We conclude that COVID-19 may promote the progression of IBD through cytokine storms. The candidate drugs and DEGs-regulated networks may suggest effective therapeutic methods for both COVID-19 and IBD.
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Affiliation(s)
- Chengyan Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Zeyu Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xi Nan
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenhui Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianchang Zeng
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinming Chen
- Department of Anorectal, Affiliated Hangzhou Dermatology Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhijian Cai
- Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China.,Department of Orthopaedics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianli Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China.,Institute of Hematology, Zhejiang University, Hangzhou, China.,Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
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