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López-Cerdán A, Andreu Z, Hidalgo MR, Soler-Sáez I, de la Iglesia-Vayá M, Mikozami A, Guerini FR, García-García F. An integrated approach to identifying sex-specific genes, transcription factors, and pathways relevant to Alzheimer's disease. Neurobiol Dis 2024; 199:106605. [PMID: 39009097 DOI: 10.1016/j.nbd.2024.106605] [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: 01/24/2024] [Revised: 06/06/2024] [Accepted: 07/11/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Age represents a significant risk factor for the development of Alzheimer's disease (AD); however, recent research has documented an influencing role of sex in several features of AD. Understanding the impact of sex on specific molecular mechanisms associated with AD remains a critical challenge to creating tailored therapeutic interventions. METHODS The exploration of the sex-based differential impact on disease (SDID) in AD used a systematic review to first select transcriptomic studies of AD with data regarding sex in the period covering 2002 to 2021 with a focus on the primary brain regions affected by AD - the cortex (CT) and the hippocampus (HP). A differential expression analysis for each study and two tissue-specific meta-analyses were then performed. Focusing on the CT due to the presence of significant SDID-related alterations, a comprehensive functional characterization was conducted: protein-protein network interaction and over-representation analyses to explore biological processes and pathways and a VIPER analysis to estimate transcription factor activity. RESULTS We selected 8 CT and 5 HP studies from the Gene Expression Omnibus (GEO) repository for tissue-specific meta-analyses. We detected 389 significantly altered genes in the SDID comparison in the CT. Generally, female AD patients displayed more affected genes than males; we grouped said genes into six subsets according to their expression profile in female and male AD patients. Only subset I (repressed genes in female AD patients) displayed significant results during functional profiling. Female AD patients demonstrated more significant impairments in biological processes related to the regulation and organization of synapsis and pathways linked to neurotransmitters (glutamate and GABA) and protein folding, Aβ aggregation, and accumulation compared to male AD patients. These findings could partly explain why we observe more pronounced cognitive decline in female AD patients. Finally, we detected 23 transcription factors with different activation patterns according to sex, with some associated with AD for the first time. All results generated during this study are readily available through an open web resource Metafun-AD (https://bioinfo.cipf.es/metafun-ad/). CONCLUSION Our meta-analyses indicate the existence of differences in AD-related mechanisms in female and male patients. These sex-based differences will represent the basis for new hypotheses and could significantly impact precision medicine and improve diagnosis and clinical outcomes in AD patients.
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Affiliation(s)
- Adolfo López-Cerdán
- Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), 46012, Valencia, Spain; Biomedical Imaging Unit FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, 46012, Valencia, Spain
| | - Zoraida Andreu
- Foundation Valencian Institute of Oncology (FIVO), 46009, Valencia, Spain
| | - Marta R Hidalgo
- Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), 46012, Valencia, Spain
| | - Irene Soler-Sáez
- Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), 46012, Valencia, Spain
| | - María de la Iglesia-Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, 46012, Valencia, Spain
| | - Akiko Mikozami
- Oral Health/Brain Health/Total health (OBT) Research Center, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | | | - Francisco García-García
- Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), 46012, Valencia, Spain.
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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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Kang J, Hwang S, Lee T, Ahn K, Seo DM, Choi SJ, Uh Y. Prediction Model for Pre-Eclampsia Using Gestational-Age-Specific Serum Creatinine Distribution. BIOLOGY 2023; 12:816. [PMID: 37372101 DOI: 10.3390/biology12060816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023]
Abstract
Pre-eclampsia (PE) is a pregnancy-related disease, causing significant threats to both mothers and babies. Numerous studies have identified the association between PE and renal dysfunction. However, in clinical practice, kidney problems in pregnant women are often overlooked due to physiologic adaptations during pregnancy, including renal hyperfiltration. Recent studies have reported serum creatinine (SCr) level distribution based on gestational age (GA) and demonstrated that deviations from the expected patterns can predict adverse pregnancy outcomes, including PE. This study aimed to establish a PE prediction model using expert knowledge and by considering renal physiologic adaptation during pregnancy. This retrospective study included pregnant women who delivered at the Wonju Severance Christian Hospital. Input variables, such as age, gestational weeks, chronic diseases, and SCr levels, were used to establish the PE prediction model. By integrating SCr, GA, GA-specific SCr distribution, and quartile groups of GA-specific SCr (GAQ) were made. To provide generalized performance, a random sampling method was used. As a result, GAQ improved the predictive performance for any cases of PE and triple cases, including PE, preterm birth, and fetal growth restriction. We propose a prediction model for PE consolidating readily available clinical blood test information and pregnancy-related renal physiologic adaptations.
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Affiliation(s)
- Jieun Kang
- Department of Obstetrics and Gynecology, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Sangwon Hwang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Taesic Lee
- Division of Data-Mining and Computational Biology, Institute of Global Health Care and Development, Wonju 26426, Republic of Korea
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Kwangjin Ahn
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Dong Min Seo
- Department of Medical Information, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Seong Jin Choi
- Department of Obstetrics and Gynecology, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Young Uh
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
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Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease. Heliyon 2022; 9:e12202. [PMID: 36711310 PMCID: PMC9876837 DOI: 10.1016/j.heliyon.2022.e12202] [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: 05/27/2022] [Revised: 11/01/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Background A factor leading to Alzheimer's Disease (AD), portrayed by peripheral insulin resistance, is Type 2 diabetes mellitus (T2D). The likelihood of T2D cases would be at boosted danger in alternating AD cases has severe social consequences. Several genes have been detected via gene expression profiling or different techniques; despite the consideration of the utility of numerous of these genes stays insufficient. Methods This project is designed to uncover the mutual genomics motifs between AD and T2D via non-negative matrix factorization (NMF) of differentially expressed genes (DEGs) of T2D Mellitus of human cortical neurons of the neurovascular unit gene expression data. A rank factorization value is calculated by employing the combination of the NMF model with the unit invariant knee (UIK) point method. The metagenes are further determined by remarking the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment tools. In this study, the most highly expressed genes of metagenes are subjected to protein-protein interaction (PPI) network study to discover the most significant biomarkers of T2D Mellitus in the ageing brain. Results We screened the most important shared genes (CDKN1A, COL22A1, EIF4A, GFAP, SLC1A1, and VIM) and essential human molecular pathways that motivate these diseases. The study aimed to validate the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. Conclusions Using in silico tools, the computational pipeline has broadly examined transformed pathways and discovered promising biomarkers and drug targets. We validated the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. These consequences on brain cells hypothetically reserve to diabetic Alzheimer's so-called type 3 diabetes (T3D) and may offer promising methodologies for curative intrusion.
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Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome. Cells 2022; 11:cells11182867. [PMID: 36139449 PMCID: PMC9496853 DOI: 10.3390/cells11182867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 12/02/2022] Open
Abstract
Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.
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Kim J, Yoo G, Lee T, Kim JH, Seo DM, Kim J. Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis. BIOLOGY 2022; 11:biology11091310. [PMID: 36138789 PMCID: PMC9495746 DOI: 10.3390/biology11091310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 11/21/2022]
Abstract
Simple Summary Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe complications in patients with diabetic foot ulcers (DFUs). Although NF and OM often cause results including limb amputation and death, definite diagnoses of these are challenging. To aid the prompt and proper diagnosis of NF and OM in patients with DFU, we developed and evaluated a novel prediction model based on machine learning technology. In summary, our prediction model appropriately discriminated the NF and OM from diabetic foot. Moreover, this prediction model has advantages in that it is based on the demographic data and routine laboratory results, which requires no additional examinations which are complicated or expensive. Abstract Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.
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Affiliation(s)
- Jiye Kim
- Department of Plastic Surgery, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Gilsung Yoo
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Taesic Lee
- Division of Data Mining and Computational Biology, Institute of Global Health Care and Development, Wonju Severance Christian Hospital, Wonju 26411, Korea
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
- Center for Precision Medicine and Genomics, Wonju Severance Christian Hospital, Wonju 26411, Korea
| | - Jeong Ho Kim
- Department of Plastic Surgery, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Dong Min Seo
- Department of Medical Information, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Juwon Kim
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
- Center for Precision Medicine and Genomics, Wonju Severance Christian Hospital, Wonju 26411, Korea
- Correspondence: ; Tel.: +82-33-741-1596; Fax: +82-33-741-1780
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Kumar V, Kim SH, Bishayee K. Dysfunctional Glucose Metabolism in Alzheimer’s Disease Onset and Potential Pharmacological Interventions. Int J Mol Sci 2022; 23:ijms23179540. [PMID: 36076944 PMCID: PMC9455726 DOI: 10.3390/ijms23179540] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/05/2022] [Accepted: 08/21/2022] [Indexed: 12/04/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common age-related dementia. The alteration in metabolic characteristics determines the prognosis. Patients at risk show reduced glucose uptake in the brain. Additionally, type 2 diabetes mellitus increases the risk of AD with increasing age. Therefore, changes in glucose uptake in the cerebral cortex may predict the histopathological diagnosis of AD. The shifts in glucose uptake and metabolism, insulin resistance, oxidative stress, and abnormal autophagy advance the pathogenesis of AD syndrome. Here, we summarize the role of altered glucose metabolism in type 2 diabetes for AD prognosis. Additionally, we discuss diagnosis and potential pharmacological interventions for glucose metabolism defects in AD to encourage the development of novel therapeutic methods.
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Affiliation(s)
- Vijay Kumar
- Department of Biochemistry, Institute of Cell Differentiation and Aging, College of Medicine, Hallym University, Chuncheon 24252, Korea
| | - So-Hyeon Kim
- Biomedical Science Core-Facility, Soonchunhyang Institute of Medi-Bio Science, Soonchunhyang University, Cheonan 31151, Korea
| | - Kausik Bishayee
- Biomedical Science Core-Facility, Soonchunhyang Institute of Medi-Bio Science, Soonchunhyang University, Cheonan 31151, Korea
- Correspondence: or
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Zhou W, Wang H, Yang Y, Guo F, Yu B, Su Z. Trophoblast Cell Subtypes and Dysfunction in the Placenta of Individuals with Preeclampsia Revealed by Single‑Cell RNA Sequencing. Mol Cells 2022; 45:317-328. [PMID: 35289305 PMCID: PMC9095508 DOI: 10.14348/molcells.2021.0211] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 11/16/2021] [Indexed: 12/03/2022] Open
Abstract
Trophoblasts, important functional cells in the placenta, play a critical role in maintaining placental function. The heterogeneity of trophoblasts has been reported, but little is known about the trophoblast subtypes and distinctive functions during preeclampsia (PE). In this study, we aimed to gain insight into the cell type-specific transcriptomic changes by performing unbiased single-cell RNA sequencing (scRNA-seq) of placental tissue samples, including those of patients diagnosed with PE and matched healthy controls. A total of 29,006 cells were identified in 11 cell types, including trophoblasts and immune cells, and the functions of the trophoblast subtypes in the PE group and the control group were also analyzed. As an important trophoblast subtype, extravillous trophoblasts (EVTs) were further divided into 4 subgroups, and their functions were preliminarily analyzed. We found that some biological processes related to pregnancy, hormone secretion and immunity changed in the PE group. We also identified and analyzed the regulatory network of transcription factors (TFs) identified in the EVTs, among which 3 modules were decreased in the PE group. Then, through in vitro cell experiments, we found that in one of the modules, CEBPB and GTF2B may be involved in EVT dysfunction in PE. In conclusion, our study showed the different transcriptional profiles and regulatory modules in trophoblasts between placentas in the control and PE groups at the single-cell level; these changes may be involved in the pathological process of PE, providing a new molecular theoretical basis for preeclamptic trophoblast dysfunction.
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Affiliation(s)
- Wenbo Zhou
- International Genome Center, Jiangsu University, Zhenjiang 212013, China
- Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China
| | - Huiyan Wang
- Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China
| | - Yuqi Yang
- Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China
| | - Fang Guo
- Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China
| | - Bin Yu
- Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China
| | - Zhaoliang Su
- International Genome Center, Jiangsu University, Zhenjiang 212013, China
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Zhang C, Cheng M, Dong N, Sun D, Ma H. General Transcription Factor IIF Polypeptide 2: A Novel Therapeutic Target for Depression Identified Using an Integrated Bioinformatic Analysis. Front Aging Neurosci 2022; 14:918217. [PMID: 35711908 PMCID: PMC9197343 DOI: 10.3389/fnagi.2022.918217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/09/2022] [Indexed: 11/25/2022] Open
Abstract
Depression currently affects 4% of the world’s population; it is associated with disability in 11% of the global population. Moreover, there are limited resources to treat depression effectively. Therefore, we aimed to identify a promising novel therapeutic target for depression using bioinformatic analysis. The GSE54568, GSE54570, GSE87610, and GSE92538 gene expression data profiles were retrieved from the Gene Expression Omnibus (GEO) database. We prepared the four GEO profiles for differential analysis, protein–protein interaction (PPI) network construction, and weighted gene co-expression network analysis (WGCNA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes metabolic pathway analyses were conducted to determine the key functions of the corresponding genes. Additionally, we performed correlation analyses of the hub genes with transcription factors, immune genes, and N6-methyladenosine (m6A) genes to reveal the functional landscape of the core genes associated with depression. Compared with the control samples, the depression samples contained 110 differentially expressed genes (DEGs), which comprised 56 downregulated and 54 upregulated DEGs. Moreover, using the WGCNA and PPI clustering analysis, the blue module and cluster 1 were found to be significantly correlated with depression. GTF2F2 was the only common gene identified using the differential analysis and WGCNA; thus, it was used as the hub gene. According to the enrichment analyses, GTF2F2 was predominantly involved in the cell cycle and JAK-STAT, PI3K-Akt, and p53 signaling pathways. Furthermore, differential and correlation analyses revealed that 9 transcription factors, 12 immune genes, and 2 m6A genes were associated with GTF2F2 in depression samples. GTF2F2 may serve as a promising diagnostic biomarker and treatment target of depression, and this study provides a novel perspective and valuable information to explore the molecular mechanism of depression.
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Affiliation(s)
- Chi Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China
| | - Min Cheng
- Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China
| | - Naifu Dong
- Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China
| | - Dongjie Sun
- College of Basic Medical Sciences, Jilin University, Changchun, China
- *Correspondence: Dongjie Sun,
| | - Haichun Ma
- Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China
- Haichun Ma,
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Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.
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Shu J, Wei W, Zhang L. Identification of Molecular Signatures and Candidate Drugs in Vascular Dementia by Bioinformatics Analyses. Front Mol Neurosci 2022; 15:751044. [PMID: 35221911 PMCID: PMC8873373 DOI: 10.3389/fnmol.2022.751044] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/17/2022] [Indexed: 01/30/2023] Open
Abstract
Vascular dementia (VaD) is considered to be the second most common form of dementia after Alzheimer’s disease, and no specific drugs have been approved for VaD treatment. We aimed to identify shared transcriptomic signatures between the frontal cortex and temporal cortex in VaD by bioinformatics analyses. Gene ontology and pathway enrichment analyses, protein–protein interaction (PPI) and hub gene identification, hub gene–transcription factor interaction, hub gene–microRNA interaction, and hub gene–drug interaction analyses were performed. We identified 159 overlapping differentially expressed genes (DEGs) between the frontal cortex and temporal cortex that were enriched mainly in inflammation and innate immunity, synapse pruning, regeneration, positive regulation of angiogenesis, response to nutrient levels, and positive regulation of the digestive system process. We identified 10 hub genes in the PPI network (GNG13, CD163, C1QA, TLR2, SST, C1QB, ITGB2, CCR5, CRH, and TAC1), four central regulatory transcription factors (FOXC1, CREB1, GATA2, and HINFP), and four microRNAs (miR-27a-3p, miR-146a-5p, miR-335-5p, and miR-129-2-3p). Hub gene–drug interaction analysis found four drugs (maraviroc, cenicriviroc, PF-04634817, and efalizumab) that could be potential drugs for VaD treatment. Together, our results may contribute to understanding the underlying mechanisms in VaD and provide potential targets and drugs for therapeutic intervention.
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Shu J, Li N, Wei W, Zhang L. Detection of molecular signatures and pathways shared by Alzheimer's disease and type 2 diabetes. Gene 2022; 810:146070. [PMID: 34813915 DOI: 10.1016/j.gene.2021.146070] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/12/2023]
Abstract
Alzheimer's disease (AD) and type 2 diabetes (T2D) are common in the general elderly population, conferring heavy individual, social, and economic stresses on families and society. Accumulating evidence indicates T2D to be a risk factor for AD. However, the underlying mechanisms for this association are largely unknown. This study aimed to identify the shared molecular signatures between AD and T2D through integrated analysis of temporal cortex gene expression data. Gene Ontology (GO) and pathway enrichment analysis, protein over-representation analysis, protein-protein interaction, DEG-transcription factor interactions, DEG-microRNA interactions, protein-drug interactions, gene-disease association analysis, and protein subcellular localization analysis of the common DEGs were performed. We identified 16 common DEGs between the two datasets, which were mainly enriched in the biological processes of apoptosis, autophagy, inflammation, and hemostasis. We also identified five hub proteins encoded by the DEGs, five central regulatory transcription factors, and six microRNAs. Protein-drug interaction analysis showed C1QB to be associated with different drugs. Gene-disease association analysis revealed that hub genes, SFN and ITGB2, were actively engaged in other diseases. Collectively, these findings provide new insights into shared molecular mechanisms between AD and T2D and provide novel candidate targets for therapeutic intervention.
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Affiliation(s)
- Jun Shu
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China
| | - Nan Li
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China
| | - Wenshi Wei
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China.
| | - Li Zhang
- Department of Neurology, Cognitive Disorders Center, Huadong Hospital, Fudan University, No. 221, West Yan An Road, Shanghai, China.
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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Sambon M, Wins P, Bettendorff L. Neuroprotective Effects of Thiamine and Precursors with Higher Bioavailability: Focus on Benfotiamine and Dibenzoylthiamine. Int J Mol Sci 2021; 22:ijms22115418. [PMID: 34063830 PMCID: PMC8196556 DOI: 10.3390/ijms22115418] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/10/2021] [Accepted: 05/18/2021] [Indexed: 11/25/2022] Open
Abstract
Thiamine (vitamin B1) is essential for brain function because of the coenzyme role of thiamine diphosphate (ThDP) in glucose and energy metabolism. In order to compensate thiamine deficiency, several thiamine precursors with higher bioavailability were developed since the 1950s. Among these, the thioester benfotiamine (BFT) has been extensively studied and has beneficial effects both in rodent models of neurodegeneration and in human clinical studies. BFT has antioxidant and anti-inflammatory properties that seem to be mediated by a mechanism independent of the coenzyme function of ThDP. BFT has no adverse effects and improves cognitive outcome in patients with mild Alzheimer’s disease (AD). Recent in vitro studies show that another thiamine thioester, dibenzoylthiamine (DBT) is even more efficient that BFT, especially with respect to its anti-inflammatory potency. Thiamine thioesters have pleiotropic properties linked to an increase in circulating thiamine concentrations and possibly in hitherto unidentified metabolites in particular open thiazole ring derivatives. The identification of the active neuroprotective derivatives and the clarification of their mechanism of action open extremely promising perspectives in the field of neurodegenerative, neurodevelopmental and psychiatric conditions.
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Vidal C, Zhang L. An Analysis of the Neurological and Molecular Alterations Underlying the Pathogenesis of Alzheimer's Disease. Cells 2021; 10:cells10030546. [PMID: 33806317 PMCID: PMC7998384 DOI: 10.3390/cells10030546] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/26/2021] [Accepted: 03/03/2021] [Indexed: 12/19/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by amyloid beta (Aβ) plaques, neurofibrillary tangles, and neuronal loss. Unfortunately, despite decades of studies being performed on these histological alterations, there is no effective treatment or cure for AD. Identifying the molecular characteristics of the disease is imperative to understanding the pathogenesis of AD. Furthermore, uncovering the key causative alterations of AD can be valuable in developing models for AD treatment. Several alterations have been implicated in driving this disease, including blood–brain barrier dysfunction, hypoxia, mitochondrial dysfunction, oxidative stress, glucose hypometabolism, and altered heme homeostasis. Although these alterations have all been associated with the progression of AD, the root cause of AD has not been identified. Intriguingly, recent studies have pinpointed dysfunctional heme metabolism as a culprit of the development of AD. Heme has been shown to be central in neuronal function, mitochondrial respiration, and oxidative stress. Therefore, dysregulation of heme homeostasis may play a pivotal role in the manifestation of AD and its various alterations. This review will discuss the most common neurological and molecular alterations associated with AD and point out the critical role heme plays in the development of this disease.
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Affiliation(s)
| | - Li Zhang
- Correspondence: ; Tel.: +1-972-883-5757
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