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Zhang Z, Liu X, Zhang S, Song Z, Lu K, Yang W. A review and analysis of key biomarkers in Alzheimer's disease. Front Neurosci 2024; 18:1358998. [PMID: 38445255 PMCID: PMC10912539 DOI: 10.3389/fnins.2024.1358998] [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: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.
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Affiliation(s)
- Zhihao Zhang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Xiangtao Liu
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Suixia Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhixin Song
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Ke Lu
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| | - Wenzhong Yang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
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Piryaei Z, Salehi Z, Ebrahimie E, Ebrahimi M, Kavousi K. Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer. BMC Med Genomics 2023; 16:219. [PMID: 37715225 PMCID: PMC10503144 DOI: 10.1186/s12920-023-01655-z] [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: 02/24/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression. METHODS In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines. RESULTS Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated. CONCLUSION The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.
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Affiliation(s)
- Zeynab Piryaei
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Ebrahimie
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, Australia
| | - Mansour Ebrahimi
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Zhuang J, Tian J, Xiong X, Li T, Chen Z, Chen R, Chen J, Li X. Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method. Front Aging Neurosci 2023; 15:1052783. [PMID: 36936501 PMCID: PMC10017840 DOI: 10.3389/fnagi.2023.1052783] [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: 09/24/2022] [Accepted: 02/08/2023] [Indexed: 03/06/2023] Open
Abstract
Abstract Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance. Methods To this end, this paper proposed a hypergraph-based netNMF (HG-netNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles. Results Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses. Conclusion Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively.
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Affiliation(s)
- Junli Zhuang
- Department of Vascular Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jinping Tian
- Faculty of Medicine, Jianghan University, Wuhan, China
| | - Xiaoxing Xiong
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Xiaoxing Xiong,
| | - Taihan Li
- Department of Clinical Laboratory, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Taihan Li,
| | - Zhengwei Chen
- Department of Radiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Rong Chen
- Department of Radiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Jun Chen
- Department of Radiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Xiang Li
- School of Health, Wuhan University, Wuhan, China
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Shukla M, Vincent B. Melatonin as a Harmonizing Factor of Circadian Rhythms, Neuronal Cell Cycle and Neurogenesis: Additional Arguments for Its Therapeutic Use in Alzheimer's Disease. Curr Neuropharmacol 2023; 21:1273-1298. [PMID: 36918783 PMCID: PMC10286584 DOI: 10.2174/1570159x21666230314142505] [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: 10/19/2022] [Revised: 12/07/2022] [Accepted: 12/31/2022] [Indexed: 03/16/2023] Open
Abstract
The synthesis and release of melatonin in the brain harmonize various physiological functions. The apparent decline in melatonin levels with advanced aging is an aperture to the neurodegenerative processes. It has been indicated that down regulation of melatonin leads to alterations of circadian rhythm components, which further causes a desynchronization of several genes and results in an increased susceptibility to develop neurodegenerative diseases. Additionally, as circadian rhythms and memory are intertwined, such rhythmic disturbances influence memory formation and recall. Besides, cell cycle events exhibit a remarkable oscillatory system, which is downstream of the circadian phenomena. The linkage between the molecular machinery of the cell cycle and complex fundamental regulatory proteins emphasizes the conjectural regulatory role of cell cycle components in neurodegenerative disorders such as Alzheimer's disease. Among the mechanisms intervening long before the signs of the disease appear, the disturbances of the circadian cycle, as well as the alteration of the machinery of the cell cycle and impaired neurogenesis, must hold our interest. Therefore, in the present review, we propose to discuss the underlying mechanisms of action of melatonin in regulating the circadian rhythm, cell cycle components and adult neurogenesis in the context of AD pathogenesis with the view that it might further assist to identify new therapeutic targets.
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Affiliation(s)
- Mayuri Shukla
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom 73170, Thailand
- Present Address: Chulabhorn Graduate Institute, Chulabhorn Royal Academy, 10210, Bangkok, Thailand
| | - Bruno Vincent
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom 73170, Thailand
- Institute of Molecular and Cellular Pharmacology, Laboratory of Excellence DistALZ, Université Côte d'Azur, INSERM, CNRS, Sophia-Antipolis, 06560, Valbonne, France
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Beck JS, Madaj Z, Cheema CT, Kara B, Bennett DA, Schneider JA, Gordon MN, Ginsberg SD, Mufson EJ, Counts SE. Co-expression network analysis of frontal cortex during the progression of Alzheimer's disease. Cereb Cortex 2022; 32:5108-5120. [PMID: 35076713 PMCID: PMC9667180 DOI: 10.1093/cercor/bhac001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 01/29/2023] Open
Abstract
Mechanisms of Alzheimer's disease (AD) and its putative prodromal stage, amnestic mild cognitive impairment (aMCI), involve the dysregulation of multiple candidate molecular pathways that drive selective cellular vulnerability in cognitive brain regions. However, the spatiotemporal overlap of markers for pathway dysregulation in different brain regions and cell types presents a challenge for pinpointing causal versus epiphenomenal changes characterizing disease progression. To approach this problem, we performed Weighted Gene Co-expression Network Analysis and STRING interactome analysis of gene expression patterns quantified in frontal cortex samples (Brodmann area 10) from subjects who died with a clinical diagnosis of no cognitive impairment, aMCI, or mild/moderate AD. Frontal cortex was chosen due to the relatively protracted involvement of this region in AD, which might reveal pathways associated with disease onset. A co-expressed network correlating with clinical diagnosis was functionally associated with insulin signaling, with insulin (INS) being the most highly connected gene within the network. Co-expressed networks correlating with neuropathological diagnostic criteria (e.g., NIA-Reagan Likelihood of AD) were associated with platelet-endothelium-leucocyte cell adhesion pathways and hypoxia-oxidative stress. Dysregulation of these functional pathways may represent incipient alterations impacting disease progression and the clinical presentation of aMCI and AD.
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Affiliation(s)
- John S Beck
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
| | - Zachary Madaj
- Bioinformatics and Biostatistics Core, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Calvin T Cheema
- Department of Mathematics and Computer Science, Kalamazoo College, Kalamazoo, MI 49006, USA
| | - Betul Kara
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
- Cell and Molecular Biology Program, Michigan State University, East Lansing, MI 48824, USA
| | - David A Bennett
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
- Rush Alzheimer’s Disease Research Center, Chicago, IL 60612, USA
| | - Julie A Schneider
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
- Rush Alzheimer’s Disease Research Center, Chicago, IL 60612, USA
| | - Marcia N Gordon
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
| | - Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Elliott J Mufson
- Department of Neurobiology, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Scott E Counts
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, USA
- Cell and Molecular Biology Program, Michigan State University, East Lansing, MI 48824, USA
- Department of Family Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Hauenstein Neurosciences Center, Mercy Health Saint Mary’s Hospital, Grand Rapids, MI 49503, USA
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48109, USA
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Integrative genomic analysis of PPP3R1 in Alzheimer's disease: a potential biomarker for predictive, preventive, and personalized medical approach. EPMA J 2021; 12:647-658. [PMID: 34956428 DOI: 10.1007/s13167-021-00261-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/18/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) is associated with abnormal calcium signaling, a pathway regulated by the calcium-dependent protein phosphatase. This study aimed to investigate the molecular function of protein phosphatase 3 regulatory subunit B (PPP3R1) underlying AD, which may provide novel insights for the predictive diagnostics, targeted prevention, and personalization of medical services in AD by targeting PPP3R1. A total of 1860 differentially expressed genes (DEGs) from 13,049 background genes were overlapped in AD/control and PPP3R1-low/high cohorts. Based on these DEGs, six co-expression modules were constructed by weight gene correlation network analysis (WGCNA). The turquoise module had the strongest correlation with AD and low PPP3R1, in which DEGs participated in axon guidance, glutamatergic synapse, long-term potentiation (LTP), mitogen-activated protein kinase (MAPK), Ras, and hypoxia-inducible factor 1 (HIF-1) signaling pathways. Furthermore, the cross-talking pathways of PPP3R1, such as axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, were identified in the global regulatory network. The area under the curve (AUC) analysis showed that low PPP3R1 could accurately predict the onset of AD. Therefore, our findings highlight the involvement of PPP3R1 in the pathogenesis of AD via axon guidance, glutamatergic synapse, LTP, and MAPK signaling pathways, and identify downregulation of PPP3R1 as a potential biomarker for AD treatment in the context of 3P medicine-predictive diagnostics, targeted prevention, and personalization of medical services. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-021-00261-2.
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