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Johansson L, Reyes JF, Ali T, Schätzl H, Gilch S, Hallbeck M. Lack of cellular prion protein causes Amyloid β accumulation, increased extracellular vesicle abundance, and changes to exosome biogenesis proteins. Mol Cell Biochem 2024:10.1007/s11010-024-05059-0. [PMID: 38970706 DOI: 10.1007/s11010-024-05059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 06/29/2024] [Indexed: 07/08/2024]
Abstract
Alzheimer's disease (AD) progression is closely linked to the propagation of pathological Amyloid β (Aβ), a process increasingly understood to involve extracellular vesicles (EVs), namely exosomes. The specifics of Aβ packaging into exosomes remain elusive, although evidence suggests an ESCRT (Endosomal Sorting Complex Required for Transport)-independent origin to be responsible in spreading of AD pathogenesis. Intriguingly, PrPC, known to influence exosome abundance and bind oligomeric Aβ (oAβ), can be released in exosomes via both ESCRT-dependent and ESCRT-independent pathways, raising questions about its role in oAβ trafficking. Thus, we quantified Aβ levels within EVs, cell medium, and intracellularly, alongside exosome biogenesis-related proteins, following deletion or overexpression of PrPC. The same parameters were also evaluated in the presence of specific exosome inhibitors, namely Manumycin A and GW4869. Our results revealed that deletion of PrPC increases intracellular Aβ accumulation and amplifies EV abundance, alongside significant changes in cellular levels of exosome biogenesis-related proteins Vps25, Chmp2a, and Rab31. In contrast, cellular expression of PrPC did not alter exosomal Aβ levels. This highlights PrPC's influence on exosome biogenesis, albeit not in direct Aβ packaging. Additionally, our data confirm the ESCRT-independent exosome release of Aβ and we show a direct reduction in Chmp2a levels upon oAβ challenge. Furthermore, inhibition of opposite exosome biogenesis pathway resulted in opposite cellular PrPC levels. In conclusion, our findings highlight the intricate relationship between PrPC, exosome biogenesis, and Aβ release. Specifically, they underscore PrPC's critical role in modulating exosome-associated proteins, EV abundance, and cellular Aβ levels, thereby reinforcing its involvement in AD pathogenesis.
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Affiliation(s)
- Lovisa Johansson
- Department of Biomedical and Clinical Sciences and Department of Clinical Pathology, Linköping University, Linköping, Sweden.
| | - Juan F Reyes
- Department of Biomedical and Clinical Sciences and Department of Clinical Pathology, Linköping University, Linköping, Sweden
| | - Tahir Ali
- Calgary Prion Research Unit, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Hermann Schätzl
- Calgary Prion Research Unit, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Sabine Gilch
- Calgary Prion Research Unit, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Hallbeck
- Department of Biomedical and Clinical Sciences and Department of Clinical Pathology, Linköping University, Linköping, Sweden.
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Bugaj AM, Kunath N, Saasen VL, Fernandez-Berrocal MS, Vankova A, Sætrom P, Bjørås M, Ye J. Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion. Prog Neurobiol 2024; 235:102599. [PMID: 38522610 DOI: 10.1016/j.pneurobio.2024.102599] [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: 01/04/2024] [Revised: 03/09/2024] [Accepted: 03/19/2024] [Indexed: 03/26/2024]
Abstract
Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.
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Affiliation(s)
- Anna M Bugaj
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Nicolas Kunath
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway; Department of Neurology, University Hospital of Trondheim, Trondheim 7491, Norway
| | - Vidar Langseth Saasen
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Marion S Fernandez-Berrocal
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Ana Vankova
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Pål Sætrom
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway
| | - Magnar Bjørås
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway; Department of Microbiology, Oslo University Hospital, University of Oslo, Oslo 0424, Norway; Centre for Embryology and Healthy Development, University of Oslo, Oslo 0373, Norway.
| | - Jing Ye
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway.
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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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Liang J, LaFleur B, Hussainy S, Perry G. Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes. J Alzheimers Dis 2024; 99:S249-S263. [PMID: 37092222 PMCID: PMC11091573 DOI: 10.3233/jad-221241] [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] [Accepted: 02/27/2023] [Indexed: 04/25/2023]
Abstract
Background Alzheimer's disease (AD) is the most common form of dementia in the elderly marked by central nervous system (CNS) neuronal loss and amyloid plaques. FAM222A, encoding an amyloid plaque core protein, is an AD brain atrophy susceptibility gene that mediates amyloid-β aggregation. However, the expression interplay between FAM222A and other AD-related pathway genes is unclear. Objective Our goal was to study FAM222A's whole-genome co-expression profile in multiple tissues and investigate its interplay with other AD-related genes. Methods We analyzed gene expression correlations in Genotype-Tissue Expression (GTEx) tissues to identify FAM222A co-expressed genes and performed functional enrichment analysis on identified genes in CNS system. Results Genome-wide gene expression profiling identified 673 genes significantly correlated with FAM222A (p < 2.5×10-6) in 48 human tissues, including 298 from 13 CNS tissues. Functional enrichment analysis revealed that FAM222A co-expressed CNS genes were enriched in multiple AD-related pathways. Gene co-expression network analysis for identified genes in each brain region predicted other disease associated genes with similar biological function. Furthermore, co-expression of 25 out of 31 AD-related pathways genes with FAM222A was replicated in brain samples from 107 aged subjects from the Aging, Dementia and TBI Study. Conclusion This gene co-expression study identified multiple AD-related genes that are associated with FAM222A, indicating that FAM222A and AD-associated genes can be active simultaneously in similar biological processes, providing evidence that supports the association of FAM222A with AD.
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Affiliation(s)
- Jingjing Liang
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - Bonnie LaFleur
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - Sadiya Hussainy
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - George Perry
- College of Sciences, University of Texas at San Antonio, San Antonio, TX, USA
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Lv Y, Xie X, Zou G, Kong M, Yang J, Chen J, Xiang B. miR-181b-5p/SOCS2/JAK2/STAT5 axis facilitates the metastasis of hepatoblastoma. PRECISION CLINICAL MEDICINE 2023; 6:pbad027. [PMID: 37955014 PMCID: PMC10639105 DOI: 10.1093/pcmedi/pbad027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/18/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Hepatoblastoma (HB) is a malignant liver tumor predominantly found in children and tumor metastasis is one of the main causes of poor prognosis in affected patients. The precise molecular mechanisms responsible for HB metastasis remain incompletely understood. However, there is evidence suggesting a connection between the dysregulation of microRNAs (miRNAs) and the progression of tumor metastasis in HB. Methods The study utilized weighted gene co-expression network analysis (WGCNA) to analyze a miRNA microarray dataset of HB. The expression of miR-181b-5p in HB tissues and cells was detected using quantitative real-time PCR. The impact of miR-181b-5p on the metastatic capacity of HB was evaluated through scratch and Transwell assays. The effects of exogenously expressing miR-181b on the metastatic phenotypes of HB cells were evaluated in vivo. Furthermore, a luciferase reporter assay was performed to validate a potential target of miR-181b-5p in HB. Results We found that miR-181b-5p was highly expressed in HB tissues and HB cell lines. Overexpression of miR-181b enhanced scratch healing, cell migration, and invasion abilities in vitro, as well as enhancing HB lung metastasis potential in vivo. Dual-luciferase reporter assays showed that Suppressor Of Cytokine Signaling 2 (SOCS2) was a direct target of miR-181b. The overexpression of miR-181b resulted in the suppression of SOCS2 expression, subsequently activating the epithelial-mesenchymal transition and JAK2/STAT5 signaling pathways. The rescue experiment showed that SOCS2 overexpression attenuated the effects of miR-181b on HB cells. Conclusion Our study showed that miR-181b promotes HB metastasis by targeting SOCS2 and may be a potential therapeutic target for HB.
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Affiliation(s)
- Yong Lv
- Department of Pediatric Surgery and Laboratory of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaolong Xie
- Department of Pediatric Surgery and Laboratory of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Guoyou Zou
- Department of General Surgery, People's Hospital of Tibet Autonomous Region, Tibet 850000, China
| | - Meng Kong
- Department of Pediatric Surgery, Children's Hospital Affiliated to Shandong University, Jinan 250022, China
| | - Jiayin Yang
- Liver Transplantation Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jing Chen
- Department of Pediatric Surgery and Laboratory of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bo Xiang
- Department of Pediatric Surgery and Laboratory of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
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Zhu L, Chen C, Kang M, Ma X, Sun X, Xue Y, Fang Y. KIF11 serves as a cell cycle mediator in childhood acute lymphoblastic leukemia. J Cancer Res Clin Oncol 2023; 149:15609-15622. [PMID: 37656243 PMCID: PMC10620298 DOI: 10.1007/s00432-023-05240-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVE To identify key gene in childhood acute lymphoblastic leukemia (ALL) through weighted gene co-expression network analysis (WGCNA), and their enriched biological functions and signaling pathways. METHODS Array data of the GSE73578 dataset, involving 46 childhood ALL samples, were acquired from the Gene Expression Omnibus (GEO) database. Hub modules associated with childhood ALL were screened out by WGCNA. Enriched biological functions and signaling pathways were then identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by overlapping those between down-regulated genes in GSE73578, GSE4698 and the hub module. Guilt by association (GBA) was adopted to verify the function of the identified KIF11 gene and to predict its target genes. Regulatory effects of KIF11 on the proliferation and cell cycle progression of ALL in vitro were determined by cytological experiments. RESULTS WGCNA showed that the yellow module was the most relevant to childhood ALL treatment, containing 698 genes that were enriched in cell division, mitotic nuclear division, DNA replication and DNA repair, cell cycle, DNA replication and the P53 signaling pathway. The KIF11 gene was screened out and predicted as a cell cycle mediator in childhood ALL. Knockdown of KIF11 in ALL cells inhibited cell proliferation and arrested cell cycle progression in G2/M phase. CONCLUSIONS The KIF11 gene is critical in the treatment process of childhood ALL, which is a promising therapeutic target for childhood ALL.
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Affiliation(s)
- Liwen Zhu
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Chuqin Chen
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Meiyun Kang
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Xiaopeng Ma
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Xiaoyan Sun
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Yao Xue
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China
| | - Yongjun Fang
- Department of Hematology and Oncology, Children's Hospital of Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China.
- Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 220000, Jiangsu Province, China.
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Lee WP, Wang H, Dombroski B, Cheng PL, Tucci A, Si YQ, Farrell J, Tzeng JY, Leung YY, Malamon J, Wang LS, Vardarajan B, Farrer L, Schellenberg G. Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects. RESEARCH SQUARE 2023:rs.3.rs-3353179. [PMID: 37886469 PMCID: PMC10602095 DOI: 10.21203/rs.3.rs-3353179/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,905) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). We found a significant burden of deletions and duplications in AD cases, particularly for singletons and homozygous events. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in complete LD with rs143080277 in NCK2. We also identified 16 SVs associated with AD and 13 SVs linked to AD-related pathological/cognitive endophenotypes. This study highlights the pivotal role of SVs in shaping our understanding of AD genetics.
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Wang H, Dombroski BA, Cheng PL, Tucci A, Si YQ, Farrell JJ, Tzeng JY, Leung YY, Malamon JS, Wang LS, Vardarajan BN, Farrer LA, Schellenberg GD, Lee WP. Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.13.23295505. [PMID: 37745545 PMCID: PMC10516060 DOI: 10.1101/2023.09.13.23295505] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics.
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Affiliation(s)
- Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Po-Liang Cheng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Albert Tucci
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - Ya-Qin Si
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - John J Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, MA 02118, USA
| | - Jung-Ying Tzeng
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - John S Malamon
- Department of Surgery, Scholl of Medicine, University of Colorado, CO 80045, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, NY 10032, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, NY 10032, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, MA 02118, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
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Patel AO, Caldwell AB, Ramachandran S, Subramaniam S. Endotype Characterization Reveals Mechanistic Differences Across Brain Regions in Sporadic Alzheimer's Disease. J Alzheimers Dis Rep 2023; 7:957-972. [PMID: 37849634 PMCID: PMC10578327 DOI: 10.3233/adr-220098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 07/21/2023] [Indexed: 10/19/2023] Open
Abstract
Background While Alzheimer's disease (AD) pathology is associated with altered brain structure, it is not clear whether gene expression changes mirror the onset and evolution of pathology in distinct brain regions. Deciphering the mechanisms which cause the differential manifestation of the disease across different regions has the potential to help early diagnosis. Objective We aimed to identify common and unique endotypes and their regulation in tangle-free neurons in sporadic AD (SAD) across six brain regions: entorhinal cortex (EC), hippocampus (HC), medial temporal gyrus (MTG), posterior cingulate (PC), superior frontal gyrus (SFG), and visual cortex (VCX). Methods To decipher the states of tangle-free neurons across different brain regions in human subjects afflicted with AD, we performed analysis of the neural transcriptome. We explored changes in differential gene expression, functional and transcription factor target enrichment, and co-expression gene module detection analysis to discern disease-state transcriptomic variances and characterize endotypes. Additionally, we compared our results to tangled AD neuron microarray-based study and the Allen Brain Atlas. Results We identified impaired neuron function in EC, MTG, PC, and VCX resulting from REST activation and reversal of mature neurons to a precursor-like state in EC, MTG, and SFG linked to SOX2 activation. Additionally, decreased neuron function and increased dedifferentiation were linked to the activation of SUZ12. Energetic deficit connected to NRF1 inactivation was found in HC, PC, and VCX. Conclusions Our findings suggest that SAD manifestation varies in scale and severity in different brain regions. We identify endotypes, such as energetic shortfalls, impaired neuronal function, and dedifferentiation.
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Affiliation(s)
- Ashay O. Patel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Andrew B. Caldwell
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Shankar Subramaniam
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
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10
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Lv SJ, Sun JN, Gan L, Sun J. Identification of molecular subtypes and immune infiltration in endometriosis: a novel bioinformatics analysis and In vitro validation. Front Immunol 2023; 14:1130738. [PMID: 37662927 PMCID: PMC10471803 DOI: 10.3389/fimmu.2023.1130738] [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: 12/30/2022] [Accepted: 07/27/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Endometriosis is a worldwide gynacological diseases, affecting in 6-10% of women of reproductive age. The aim of this study was to investigate the gene network and potential signatures of immune infiltration in endometriosis. Methods The expression profiles of GSE51981, GSE6364, and GSE7305 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and central genes related to immune characteristics were identified using a weighted gene coexpression network analysis. Bioinformatics analysis was performed to identify central genes in immune infiltration. Protein-protein interaction (PPI) network was used to identify the hub genes. We then constructed subtypes of endometriosis samples and calculated their correlation with hub genes. qRTPCR and Western blotting were used to verify our findings. Results We identified 10 candidate hub genes (GZMB, PRF1, KIR2DL1, KIR2DL3, KIR3DL1, KIR2DL4, FGB, IGFBP1, RBP4, and PROK1) that were significantly correlated with immune infiltration. Our study established a detailed immune network and systematically elucidated the molecular mechanism underlying endometriosis from the aspect of immune infiltration. Discussion Our study provides comprehensive insights into the immunology involved in endometriosis and might contribute to the development of immunotherapy for endometriosis. Furthermore, our study sheds light on the underlying molecular mechanism of endometriosis and might help improve the diagnosis and treatment of this condition.
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Affiliation(s)
- Si-ji Lv
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jia-ni Sun
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Gan
- Department of Gynaecology and Obstetrics, Ningbo First Hospital, Ningbo, Zhejiang, China
| | - Jing Sun
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
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11
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Ni P, Pan K, Zhao B. Influence of N6-methyladenosine (m6A) modification on cell phenotype in Alzheimer's disease. PLoS One 2023; 18:e0289068. [PMID: 37549144 PMCID: PMC10406241 DOI: 10.1371/journal.pone.0289068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 07/11/2023] [Indexed: 08/09/2023] Open
Abstract
OBJECTIVE Recent research has suggested that m6A modification takes on critical significance to Neurodegeneration. As indicated by the genome-wide map of m6A mRNA, genes in Alzheimer's disease model achieved significant m6A methylation. This study aimed to investigate the hub gene and pathway of m6A modification in the pathogenesis of AD. Moreover, possible brain regions with higher gene expression levels and compounds exerting potential therapeutic effects were identified. Thus, this study can provide a novel idea to explore the treatment of AD. METHODS Differential expression genes (DEGs) of GSE5281 and GSE48350 from the Gene Expression Omnibus (GEO) database were screened using the Limma package. Next, the enrichment analysis was conducted on the screened DEGs. Moreover, the functional annotation was given for N6-methyladenosine (m6A) modification gene. The protein-protein interaction network (PPI) analysis and the visualization analysis were conducted using STRING and Cytoscape. The hub gene was identified using CytoHubba. The expression levels of Hub genes in different regions of brain tissue were analyzed based on Human Protein Atlas (HPA) database and Bgee database. Subsequently, the candidate drugs targeting hub genes were screened using cMAP. RESULTS A total of 42 m6A modified genes were identified in AD (20 up-regulated and 22 down-regulated genes). The above-described genes played a certain role in biological processes (e.g., retinoic acid, DNA damage response and cysteine-type endopeptidase activity), cellular components (e.g., mitochondrial protein complex), and molecular functions (e.g., RNA methyltransferase activity and ubiquitin protein ligase). KEGG results suggested that the above-mentioned genes were primarily involved in the Hippo signaling pathway of neurodegeneration disease. A total of 10 hub genes were screened using the protein-protein interaction network, and the expression of hub genes in different regions of human brain was studied. Furthermore, 10 compounds with potential therapeutic effects on AD were predicted. CONCLUSION This study revealed the potential role of the m6A modification gene in Alzheimer's disease through the bioinformatics analysis. The biological changes may be correlated with retinoic acid, DNA damage response and cysteine-type endopeptidase activity, which may occur through Hippo signaling pathway. The hub genes (SOX2, KLF4, ITGB4, CD44, MSX1, YAP1, AQP1, EGR2, YWHAZ and TFAP2C) and potential drugs may provide novel research directions for future prognosis and precise treatment.
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Affiliation(s)
- Pengyun Ni
- Department of Science and Education, Baoji Traditional Chinese Medicine Hospital, Baoji, Shannxi, P.R China
| | - Kaiting Pan
- Department of Neurology, Baoji Third Hospital, Baoji, Shannxi, P.R China
| | - Bingbing Zhao
- Emergency Department, Baoji Traditional Chinese Medicine Hospital, Baoji, Shannxi, P.R China
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12
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Lin Y, Li Y, Chen H, Meng J, Li J, Chu J, Zheng R, Wang H, Pan P, Su J, Jiang J, Ye L, Liang H, An S. Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients. BMC Med Genomics 2023; 16:59. [PMID: 36966292 PMCID: PMC10039774 DOI: 10.1186/s12920-023-01490-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/27/2023] Open
Abstract
The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care Unit status and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein-protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients.
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Affiliation(s)
- Yao Lin
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yueqi Li
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hubin Chen
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jun Meng
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jingyi Li
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jiemei Chu
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Ruili Zheng
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hailong Wang
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Peijiang Pan
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jinming Su
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Junjun Jiang
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Li Ye
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hao Liang
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
- Biosafety Level 3 Laboratory and Guangxi Collaborative Innovation Centre for Biomedicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Sanqi An
- Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China.
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
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13
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Akushevich I, Yashkin A, Ukraintseva S, Yashin AI, Kravchenko J. The Construction of a Multidomain Risk Model of Alzheimer's Disease and Related Dementias. J Alzheimers Dis 2023; 96:535-550. [PMID: 37840484 PMCID: PMC10657690 DOI: 10.3233/jad-221292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and related dementia (ADRD) risk is affected by multiple dependent risk factors; however, there is no consensus about their relative impact in the development of these disorders. OBJECTIVE To rank the effects of potentially dependent risk factors and identify an optimal parsimonious set of measures for predicting AD/ADRD risk from a larger pool of potentially correlated predictors. METHODS We used diagnosis record, survey, and genetic data from the Health and Retirement Study to assess the relative predictive strength of AD/ADRD risk factors spanning several domains: comorbidities, demographics/socioeconomics, health-related behavior, genetics, and environmental exposure. A modified stepwise-AIC-best-subset blanket algorithm was then used to select an optimal set of predictors. RESULTS The final predictive model was reduced to 10 features for AD and 19 for ADRD; concordance statistics were about 0.85 for one-year and 0.70 for ten-year follow-up. Depression, arterial hypertension, traumatic brain injury, cerebrovascular diseases, and the APOE4 proxy SNP rs769449 had the strongest individual associations with AD/ADRD risk. AD/ADRD risk-related co-morbidities provide predictive power on par with key genetic vulnerabilities. CONCLUSION Results confirm the consensus that circulatory diseases are the main comorbidities associated with AD/ADRD risk and show that clinical diagnosis records outperform comparable self-reported measures in predicting AD/ADRD risk. Model construction algorithms combined with modern data allows researchers to conserve power (especially in the study of disparities where disadvantaged groups are often grossly underrepresented) while accounting for a high proportion of AD/ADRD-risk-related population heterogeneity stemming from multiple domains.
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Affiliation(s)
- Igor Akushevich
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Anatoliy I. Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Julia Kravchenko
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
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Aslanis I, Krokidis MG, Dimitrakopoulos GN, Vrahatis AG. Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1423:207-214. [PMID: 37525046 DOI: 10.1007/978-3-031-31978-5_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.
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Affiliation(s)
- Ioannis Aslanis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Georgios N Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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15
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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16
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Naik S, Mohammed A. Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions. Front Genet 2022; 13:917636. [PMID: 36482897 PMCID: PMC9722774 DOI: 10.3389/fgene.2022.917636] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 11/08/2022] [Indexed: 07/30/2023] Open
Abstract
Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.
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Affiliation(s)
- Surabhi Naik
- Department of Surgery, James D. Eason Transplant Institute, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Akram Mohammed
- Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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17
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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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18
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Zhao N, Quicksall Z, Asmann YW, Ren Y. Network approaches for omics studies of neurodegenerative diseases. Front Genet 2022; 13:984338. [PMID: 36186441 PMCID: PMC9523597 DOI: 10.3389/fgene.2022.984338] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
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Affiliation(s)
- Na Zhao
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yan W. Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
| | - Yingxue Ren
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States
- *Correspondence: Yingxue Ren,
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Duan K, Ma Y, Tan J, Miao Y, Zhang Q. Identification of genetic molecular markers and immune infiltration characteristics of Alzheimer's disease through weighted gene co-expression network analysis. Front Neurol 2022; 13:947781. [PMID: 36071897 PMCID: PMC9441600 DOI: 10.3389/fneur.2022.947781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022] Open
Abstract
Background Alzheimer's disease (AD) is a progressive neurodegenerative disease that leads to cognitive impairment and memory loss. Currently, the pathogenesis and underlying causative genes of AD remain unclear, and there exists no effective treatment for this disease. This study explored AD-related diagnostic and therapeutic biomarkers from the perspective of immune infiltration by analyzing public data from the NCBI Gene Expression Omnibus database. Method In this study, weighted gene co-expression network analysis (WGCNA) was conducted to identify modules and hub genes contributing to AD development. A protein–protein interaction network was constructed when the genes in the modules were enriched and examined by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, a gene network was established using topological WGCNA, from which five hub genes were selected. Logistic regression analysis and receiver operating characteristic curve analysis were performed to explore the clinical value of genes in AD diagnosis. The genes in the core module intersected with the hub genes, and four intersection genes (ATP2A2, ATP6V1D, CAP2, and SYNJ1) were selected. These four genes were enriched by gene set enrichment analysis (GSEA). Finally, an immune infiltration analysis was performed. Results The GO/KEGG analysis suggested that genes in the core module played a role in the differentiation and growth of neural cells and in the transmission of neurotransmitters. The GSEA of core genes showed that these four genes were mainly enriched in immune/infection pathways (e.g., cholera infection and Helicobacter pylori infection pathways) and other metabolic pathways. An investigation of immune infiltration characteristics revealed that activated mast cells, regulatory T cells, plasma cells, neutrophils, T follicular helper cells, CD8 T cells, resting memory CD4 T cells, and M1 macrophages were the core immune cells contributing to AD progression. qRT-PCR analysis showed that the ATP6V1D is upregulated in AD. Conclusion The results of enrichment and immuno-osmotic analyses indicated that immune pathways and immune cells played an important role in the occurrence and development of AD. The selected key genes were used as biomarkers related to the pathogenesis of AD to further explore the pathways and cells, which provided new perspectives on therapeutic targets in AD.
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Affiliation(s)
- KeFei Duan
- Department of Geriatrics, Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Ma
- Department of Geriatrics, Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Jin Tan
- Department of Geriatrics, Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuyang Miao
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Qiang Zhang
- Department of Geriatrics, Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Qiang Zhang
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20
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Zhang T, Wong G. Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA). Comput Struct Biotechnol J 2022; 20:3851-3863. [PMID: 35891798 PMCID: PMC9307959 DOI: 10.1016/j.csbj.2022.07.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/09/2022] [Accepted: 07/09/2022] [Indexed: 12/24/2022] Open
Abstract
Weighted gene co-expression network analysis (WGCNA) is used to detect clusters with highly correlated genes. Measurements of correlation most typically rely on linear relationships. However, a linear relationship does not always model pairwise functional-related dependence between genes. In this paper, we first compared 6 different correlation methods in their ability to capture complex dependence between genes in three different tissues. Next, we compared their gene-pairwise coefficient results and corresponding WGCNA results. Finally, we applied a recently proposed correlation method, Hellinger correlation, as a more sensitive correlation measurement in WGCNA. To test this method, we constructed gene networks containing co-expression gene modules from RNA-seq data of human frontal cortex from Alzheimer's disease patients. To test the generality, we also used a microarray data set from human frontal cortex, single cell RNA-seq data from human prefrontal cortex, RNA-seq data from human temporal cortex, and GTEx data from heart. The Hellinger correlation method captures essentially similar results as other linear correlations in WGCNA, but provides additional new functional relationships as exemplified by uncovering a link between inflammation and mitochondria function. We validated the network constructed with the microarray and single cell sequencing data sets and a RNA-seq dataset of temporal cortex. We observed that this new correlation method enables the detection of non-linear biologically meaningful relationships among genes robustly and provides a complementary new approach to WGCNA. Thus, the application of Hellinger correlation to WGCNA provides a more flexible correlation approach to modelling networks in gene expression analysis that uncovers novel network relationships.
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Affiliation(s)
- Tianjiao Zhang
- Cancer Centre, Centre for Reproduction, Development and Aging, Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa 999078, Macau Special Administrative Region
| | - Garry Wong
- Cancer Centre, Centre for Reproduction, Development and Aging, Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa 999078, Macau Special Administrative Region
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Alarabi AB, Mohsen A, Mizuguchi K, Alshbool FZ, Khasawneh FT. Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets. BMC Med Genomics 2022; 15:83. [PMID: 35421970 PMCID: PMC9008611 DOI: 10.1186/s12920-022-01222-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/21/2022] [Indexed: 01/23/2023] Open
Abstract
Corona virus disease 2019 (COVID-19) increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The underlying pathophysiological processes are complex, and remain poorly understood. To this end, platelets play important roles in regulating the cardiovascular system, including via contributions to coagulation and inflammation. There is ample evidence that circulating platelets are activated in COVID-19 patients, which is a primary driver of the observed thrombotic outcome. However, the comprehensive molecular basis of platelet activation in COVID-19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID-19. These cluster of genes successfully identify COVID-19 cases, relative to healthy in a separate validation data set using machine learning, thereby validating our findings. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID-19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID-19 disease at the molecular level, which might aid in defining new targets for treatment of COVID-19–induced thrombosis.
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Teng L, Shen L, Zhao W, Wang C, Feng S, Wang Y, Bi Y, Rong S, Shushakova N, Haller H, Chen J, Jiang H. SLAMF8 Participates in Acute Renal Transplant Rejection via TLR4 Pathway on Pro-Inflammatory Macrophages. Front Immunol 2022; 13:846695. [PMID: 35432371 PMCID: PMC9012444 DOI: 10.3389/fimmu.2022.846695] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/03/2022] [Indexed: 01/10/2023] Open
Abstract
Background Acute rejection (AR) in kidney transplantation is an established risk factor that reduces the survival rate of allografts. Despite standard immunosuppression, molecules with regulatory control in the immune pathway of AR can be used as important targets for therapeutic operations to prevent rejection. Methods We downloaded the microarray data of 15 AR patients and 37 non-acute rejection (NAR) patients from Gene Expression Omnibus (GEO). Gene network was constructed, and genes were classified into different modules using weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cytoscape were applied for the hub genes in the most related module to AR. Different cell types were explored by xCell online database and single-cell RNA sequencing. We also validated the SLAMF8 and TLR4 levels in Raw264.7 and human kidney tissues of TCMR. Results A total of 1,561 differentially expressed genes were filtered. WGCNA was constructed, and genes were classified into 12 modules. Among them, the green module was most closely associated with AR. These genes were significantly enriched in 20 pathway terms, such as cytokine–cytokine receptor interaction, chemokine signaling pathway, and other important regulatory processes. Intersection with GS > 0.4, MM > 0.9, the top 10 MCC values and DEGs in the green module, and six hub genes (DOCK2, NCKAP1L, IL2RG, SLAMF8, CD180, and PTPRE) were identified. Their expression levels were all confirmed to be significantly elevated in AR patients in GEO, Nephroseq, and quantitative real-time PCR (qRT-PCR). Single-cell RNA sequencing showed that AR patient had a higher percentage of native T, CD1C+_B DC, NKT, NK, and monocytes in peripheral blood mononuclear cells (PBMCs). Xcell enrichment scores of 20 cell types were significantly different (p<0.01), mostly immune cells, such as B cells, CD4+ Tem, CD8+ T cells, CD8+ Tcm, macrophages, M1, and monocytes. GSEA suggests that highly expressed six hub genes are correlated with allograft rejection, interferon γ response, interferon α response, and inflammatory response. In addition, SLAMF8 is highly expressed in human kidney tissues of TCMR and in M1 phenotype macrophages of Raw264.7 cell line WGCNA accompanied by high expression of TLR4. Conclusion This study demonstrates six hub genes and functionally enriched pathways related to AR. SLAMF8 is involved in the M1 macrophages via TLR4, which contributed to AR process.
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Affiliation(s)
- Lisha Teng
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Lingling Shen
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Wenjun Zhao
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Cuili Wang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Shi Feng
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Yucheng Wang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Yan Bi
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Song Rong
- Department of Nephrology, Hannover Medical School, Hannover, Germany
| | - Nelli Shushakova
- Department of Nephrology, Hannover Medical School, Hannover, Germany
| | - Hermann Haller
- Department of Nephrology, Hannover Medical School, Hannover, Germany
| | - Jianghua Chen
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Hong Jiang
- Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Nephropathy, Hangzhou, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
- *Correspondence: Hong Jiang,
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Chai K, Zhang X, Tang H, Gu H, Ye W, Wang G, Chen S, Wan F, Liang J, Shen D. The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter. Front Neurol 2022; 13:807349. [PMID: 35280300 PMCID: PMC8907380 DOI: 10.3389/fneur.2022.807349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/24/2022] [Indexed: 01/11/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination, which leads to the formation of white matter lesions (WMLs) and gray matter lesions (GMLs). Recently, a large amount of transcriptomics or proteomics research works explored MS, but few studies focused on the differences and similarities between GMLs and WMLs in transcriptomics. Furthermore, there are astonishing pathological differences between WMLs and GMLs, for example, there are differences in the type and abundance of infiltrating immune cells between WMLs and GMLs. Here, we used consensus weighted gene co-expression network analysis (WGCNA), single-sample gene set enrichment analysis (ssGSEA), and machine learning methods to identify the transcriptomic differences and similarities of the MS between GMLs and WMLs, and to find the co-expression modules with significant differences or similarities between them. Through weighted co-expression network analysis and ssGSEA analysis, CD56 bright natural killer cell was identified as the key immune infiltration factor in MS, whether in GM or WM. We also found that the co-expression networks between the two groups are quite similar (density = 0.79), and 28 differentially expressed genes (DEGs) are distributed in the midnightblue module, which is most related to CD56 bright natural killer cell in GM. Simultaneously, we also found that there are huge disparities between the modules, such as divergences between darkred module and lightyellow module, and these divergences may be relevant to the functions of the genes in the modules.
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Affiliation(s)
- Keping Chai
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
- *Correspondence: Keping Chai
| | - Xiaolin Zhang
- Department of Neurological Surgery, Tongji Hospital, Tongji Medical College, Huazhong University Science and Technology, Wuhan, China
| | - Huitao Tang
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Huaqian Gu
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Weiping Ye
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Gangqiang Wang
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Shufang Chen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Feng Wan
- Department of Neurological Surgery, Tongji Hospital, Tongji Medical College, Huazhong University Science and Technology, Wuhan, China
- Feng Wan
| | - Jiawei Liang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Jiawei Liang
| | - Daojiang Shen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
- Daojiang Shen
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Gao X, Jiang C, Yao S, Ma L, Wang X, Cao Z. Identification of hub genes related to immune cell infiltration in periodontitis using integrated bioinformatic analysis. J Periodontal Res 2022; 57:392-401. [PMID: 34993975 DOI: 10.1111/jre.12970] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/18/2021] [Accepted: 12/24/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND OBJECTIVE Periodontitis is an inflammatory disease of the periodontium. However, the hub genes in periodontitis and their correlation with immune cells are not clear. This study aimed to identify hub genes and immune infiltration properties in periodontitis and to explore the correlation between hub genes and immune cells. MATERIAL AND METHODS Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed both on GSE10334 and GSE173078 datasets. Hub genes were identified via WGCNA and DEGs. The proportions of infiltrating immune cells were calculated by CIBERSORT algorithm, and single-cell RNA-sequencing dataset GSE164241 was used to explore cell-type-specific expression profiles of hub genes. RESULTS Eight hub genes (DERL3, FKBP11, LAX1, CD27, SPAG4, ST6GAL1, MZB1, and SEL1L3) were selected via WGCNA and DEGs by combining GSE10334 and GSE173078 datasets. CIBERSORT analysis showed a significant difference in the proportion of B cells, dendritic cells resting, and neutrophils in the gingival tissues between healthy and periodontitis patients, and expressions of these genes were highly correlated with the infiltration of B cells in periodontitis. Furthermore, real-time quantitative PCR results further confirmed the overexpression of hub genes. Analysis of GSE164241dataset further identified that most of hub genes were mainly expressed in B cells. CONCLUSIONS By integrating WGCNA, DEGs, and CIBERSORT analysis, eight genes were identified to be the hub genes of periodontitis and most of them were mainly expressed in B cells encouraging further researches on B cells in periodontitis pathogenesis.
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Affiliation(s)
- Xudong Gao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Chenxi Jiang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Siqi Yao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Li Ma
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xiaoxuan Wang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Periodontology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhengguo Cao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST KLOS) & Key Laboratory for Oral Biomedical Engineering of Ministry of Education (KLOBME), School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Periodontology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
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25
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Qu Q, Sun JY, Zhang ZY, Su Y, Li SS, Li F, Wang RX. Hub microRNAs and genes in the development of atrial fibrillation identified by weighted gene co-expression network analysis. BMC Med Genomics 2021; 14:271. [PMID: 34781940 PMCID: PMC8591905 DOI: 10.1186/s12920-021-01124-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/08/2021] [Indexed: 01/17/2023] Open
Abstract
Co-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF.
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Affiliation(s)
- Qiang Qu
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China.,Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jin-Yu Sun
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China.,Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhen-Ye Zhang
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China
| | - Yue Su
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China.,Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shan-Shan Li
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China
| | - Feng Li
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China
| | - Ru-Xing Wang
- Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Wuxi, 214023, China.
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26
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Chai K, Liang J, Zhang X, Cao P, Chen S, Gu H, Ye W, Liu R, Hu W, Peng C, Liu GL, Shen D. Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain. Front Aging Neurosci 2021; 13:707165. [PMID: 34733151 PMCID: PMC8558222 DOI: 10.3389/fnagi.2021.707165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 09/27/2021] [Indexed: 01/31/2023] Open
Abstract
Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging.
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Affiliation(s)
- Keping Chai
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Jiawei Liang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaolin Zhang
- Key Laboratory of Ministry of Education for Neurological Disorders, Department of Pathophysiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Panlong Cao
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Shufang Chen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Huaqian Gu
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Weiping Ye
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Rong Liu
- Key Laboratory of Ministry of Education for Neurological Disorders, Department of Pathophysiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenjun Hu
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Caixia Peng
- Key Laboratory for Molecular Diagnosis of Hubei Province, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China.,Central Laboratory, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Logan Liu
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Daojiang Shen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
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27
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Guo L, Liu Y, Wang J. Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease. Front Aging Neurosci 2021; 13:727928. [PMID: 34539387 PMCID: PMC8446362 DOI: 10.3389/fnagi.2021.727928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 08/12/2021] [Indexed: 12/04/2022] Open
Abstract
The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.
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Affiliation(s)
- Liyuan Guo
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yushan Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jing Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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28
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Kui L, Kong Q, Yang X, Pan Y, Xu Z, Wang S, Chen J, Wei K, Zhou X, Yang X, Wu T, Mastan A, Liu Y, Miao J. High-Throughput In Vitro Gene Expression Profile to Screen of Natural Herbals for Breast Cancer Treatment. Front Oncol 2021; 11:684351. [PMID: 34490085 PMCID: PMC8418118 DOI: 10.3389/fonc.2021.684351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/23/2021] [Indexed: 11/13/2022] Open
Abstract
Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer in women worldwide. Some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in breast cancer. However, the chemical composition and underlying anti-tumor mechanisms of TCM still need to be investigated. The primary aim of this study is to provide unique insights to screen the natural components for breast cancer therapy using high-throughput transcriptome analysis. Differentially expressed genes were identified based on two conditions: single samples and groups were classified according to their pharmaceutical effect. Subsequently, the sample treated with E. cochinchinensis Lour. generated the most significant DEGs set, including 1,459 DEGs, 805 upregulated and 654 downregulated. Similarly, group 3 treatment contained the most DEGs (414 DEGs, 311 upregulated and 103 downregulated). KEGG pathway analyses showed five significant pathways associated with the inflammatory and metastasis processes in cancer, which include the TNF, IL−17, NF-kappa B, MAPK signaling pathways, and transcriptional misregulation in cancer. Samples were classified into 13 groups based on their pharmaceutical effects. The results of the KEGG pathway analyses remained consistent with signal samples; group 3 presents a high significance. A total of 21 genes were significantly regulated in these five pathways, interestingly, IL6, TNFAIP3, and BRIC3 were enriched on at least two pathways, seven genes (FOSL1, S100A9, CXCL12, ID2, PRS6KA3, AREG, and DUSP6) have been reported as the target biomarkers and even the diagnostic tools in cancer therapy. In addition, weighted correlation network analysis (WGCNA) was used to identify 18 modules. Among them, blue and thistle2 were the most relevant modules. A total of 26 hub genes in blue and thistle2 modules were identified as the hub genes. In conclusion, we screened out three new TCM (R. communis L., E. cochinchinensis Lour., and B. fruticosa) that have the potential to develop natural drugs for breast cancer therapy, and obtained the therapeutic targets.
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Affiliation(s)
- Ling Kui
- Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China.,Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.,School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Qinghua Kong
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Xiaonan Yang
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Medicinal Botanical Garden, Nanning, China.,Guangxi Engineering Research Center of Traditional Chinese Medicine (TCM) Resource Intelligent Creation, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Yunbing Pan
- Nowbio Biotechnology Company, Kunming, China
| | - Zetan Xu
- Nowbio Biotechnology Company, Kunming, China
| | | | - Jian Chen
- International Genome Center, Jiangsu University, Zhenjiang, China
| | - Kunhua Wei
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Medicinal Botanical Garden, Nanning, China.,Guangxi Engineering Research Center of Traditional Chinese Medicine (TCM) Resource Intelligent Creation, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Xiaolei Zhou
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Medicinal Botanical Garden, Nanning, China.,Guangxi Engineering Research Center of Traditional Chinese Medicine (TCM) Resource Intelligent Creation, Guangxi Botanical Garden of Medicinal Plants, Nanning, China
| | - Xingzhi Yang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Tingqin Wu
- Department of Cell Biology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Anthati Mastan
- Research Center, Microbial Technology Laboratory, Council of Scientific & Industrial Research (CSIR)-Central Institute of Medicinal and Aromatic Plants, Bangalore, India
| | - Yao Liu
- Baoji High-tech Hospital , Baoji, China
| | - Jianhua Miao
- Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Medicinal Botanical Garden, Nanning, China.,School of Pharmacy, Guangxi Medical University, Nanning, China
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High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA). BIOMED RESEARCH INTERNATIONAL 2021; 2021:5955343. [PMID: 34485520 PMCID: PMC8416370 DOI: 10.1155/2021/5955343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 07/13/2021] [Indexed: 12/23/2022]
Abstract
Lung cancer is known as the leading cause which presents the highest fatality rate worldwide; non-small-cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with high severity and affects 80% of patients with lung malignancies. Up to now, the general treatment for NSCLC includes surgery, chemotherapy, and radiotherapy; however, some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in lung cancer. Therefore, it is necessary to investigate the chemical composition and underlying antitumor mechanisms of TCM, so as to get a better understanding of the potential natural ingredient for lung cancer treatment. In this study, we selected 78 TCM to treat NSCLC cell line (A549) and obtained 92 transcriptome data; differential expression and WGCNA were applied to screen the potential natural ingredient and target genes. The sample which was treated with A. pierreana generated the most significant DEG set, including 6130 DEGs, 2479 upregulated, and 3651 downregulated. KEGG pathway analyses found that four pathways (MAPK, NF-kappa B, p53, and TGF-beta signaling pathway) were significantly enriched; 16 genes were significantly regulated in these four pathways. Interestingly, some of them such as EGFR, DUSP4, IL1R1, IL1B, MDM2, CDKNIA, and IDs have been used as the target biomarkers for cancer diagnosis and therapy. In addition, classified samples into 14 groups based on their pharmaceutical effects, WGCNA was used to identify 27 modules. Among them, green and darkgrey were the most relevant modules. Eight genes in the green module and four in darkgrey were identified as hub genes. In conclusion, we screened out three new TCM (B. fruticose, A. pierreana, and S. scandens) that have the potential to develop natural anticancer drugs and obtained the therapeutic targets for NSCLC therapy. Our study provides unique insights to screen the natural components for NSCLC therapy using high-throughput transcriptome analysis.
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Wang X, Bui H, Vemuri P, Graff-Radford J, Jack CR, Petersen RC, Mielke MM. Lipidomic Network of Mild Cognitive Impairment from the Mayo Clinic Study of Aging. J Alzheimers Dis 2021; 81:533-543. [PMID: 33814434 DOI: 10.3233/jad-201347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Lipid alterations contribute to Alzheimer's disease (AD) pathogenesis. Lipidomics studies could help systematically characterize such alterations and identify potential biomarkers. OBJECTIVE To identify lipids associated with mild cognitive impairment and amyloid-β deposition, and to examine lipid correlation patterns within phenotype groupsMethods:Eighty plasma lipids were measured using mass spectrometry for 1,255 non-demented participants enrolled in the Mayo Clinic Study of Aging. Individual lipids associated with mild cognitive impairment (MCI) were first identified. Correlation network analysis was then performed to identify lipid species with stable correlations across conditions. Finally, differential correlation network analysis was used to determine lipids with altered correlations between phenotype groups, specifically cognitively unimpaired versus MCI, and with elevated brain amyloid versus without. RESULTS Seven lipids were associated with MCI after adjustment for age, sex, and APOE4. Lipid correlation network analysis revealed that lipids from a few species correlated well with each other, demonstrated by subnetworks of these lipids. 177 lipid pairs differently correlated between cognitively unimpaired and MCI patients, whereas 337 pairs of lipids exhibited altered correlation between patients with and without elevated brain amyloid. In particular, 51 lipid pairs showed correlation alterations by both cognitive status and brain amyloid. Interestingly, the lipids central to the network of these 51 lipid pairs were not significantly associated with either MCI or amyloid, suggesting network-based approaches could provide biological insights complementary to traditional association analyses. CONCLUSION Our attempt to characterize the alterations of lipids at network-level provides additional insights beyond individual lipids, as shown by differential correlations in our study.
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Affiliation(s)
- Xuewei Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hai Bui
- Eli Lilly and Company, Indianapolis, IN, USA
| | | | | | | | - Ronald C Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Michelle M Mielke
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.,Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Wang H, Han X, Gao S. Identification of potential biomarkers for pathogenesis of Alzheimer's disease. Hereditas 2021; 158:23. [PMID: 34225819 PMCID: PMC8259215 DOI: 10.1186/s41065-021-00187-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/31/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD. METHODS GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes. RESULTS There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms. CONCLUSIONS In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.
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Affiliation(s)
- Huimin Wang
- Department of Neurology, Tianjin Hospital of ITCWM Nankai Hospital, 300100, Tianjin, China
| | - Xiujiang Han
- Department of Geriatrics, Tianjin Hospital of ITCWM Nankai Hospital, No.6 Changjiang Road, Nankai, 300100, Tianjin, China
| | - Sheng Gao
- Department of Geriatrics, Tianjin Hospital of ITCWM Nankai Hospital, No.6 Changjiang Road, Nankai, 300100, Tianjin, China.
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Wang X, Huang K, Yang F, Chen D, Cai S, Huang L. Association between structural brain features and gene expression by weighted gene co-expression network analysis in conversion from MCI to AD. Behav Brain Res 2021; 410:113330. [PMID: 33940051 DOI: 10.1016/j.bbr.2021.113330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/16/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease. Mild cognitive impairment (MCI) represents a state of cognitive function between normal cognition and dementia. Longitudinal studies showed that some MCI patients remained in a state of MCI, and some developed AD. The reason for these different conversions from MCI remains to be investigated. 180 MCI participants were followed for eight years. 143 MCI patients maintained the MCI state (MCI_S), and the remaining thirty-seven MCI patients were re-evaluated as having AD (MCI_AD). We obtained 1,036 structural brain characteristics and 15,481 gene expression values from the 180 MCI participants and applied weighted gene co-expression network analysis (WGCNA) to explore the relationship between structural brain features and gene expression. Regulating mediator effect analysis was employed to explore the relationships among gene expression, brain region measurements and clinical phenotypes. We found that 60 genes from the MCI_S group and 18 genes from the MCI_AD group respectively had the most significant correlations with left paracentral lobule and sulcus (L.PTS) and right subparietal sulcus (R.SubPS) thickness; CTCF, UQCR11 and WDR5B were the mutual genes between the two groups. The expression of CTCF gene and clinical score are completely mediated by L.PTS thickness, and the UQCR11 and WDR5B gene expression levels significantly regulate the mediating effect pathway. In conclusion, the factors affecting the different conversions from MCI are closely related to L.PTS thickness and the CTCF, UQCR11 and WDR5B gene expression levels. Our results add a theoretical foundation of imaging genetics for conversion from MCI to AD.
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Affiliation(s)
- Xuwen Wang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Kexin Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Fan Yang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Dihun Chen
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Suping Cai
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
| | - Liyu Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
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Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome. Sci Rep 2021; 11:5585. [PMID: 33692493 PMCID: PMC7946886 DOI: 10.1038/s41598-021-85044-5] [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: 09/16/2020] [Accepted: 02/17/2021] [Indexed: 11/25/2022] Open
Abstract
Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.
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Chen Q, Zhao Z, Yin G, Yang C, Wang D, Feng Z, Ta N. Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:466. [PMID: 33850863 PMCID: PMC8039699 DOI: 10.21037/atm-21-340] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist. Methods To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network. Results The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the co-expression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI. Conclusions These results may provide a novel therapeutic strategy for the treatment of SCI.
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Affiliation(s)
- Qi Chen
- Department of Orthopedics, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ziru Zhao
- Department of Orthopedics, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guoyong Yin
- Department of Orthopedics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chuanjun Yang
- Department of Orthopedics, Anting Hospital, Shanghai, China
| | - Danfeng Wang
- Department of Orthopedics, Anting Hospital, Shanghai, China
| | - Zhi Feng
- Department of Orthopedics, Anting Hospital, Shanghai, China
| | - Na Ta
- Department of Nursing Management, Anting Hospital, Shanghai, China
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Lin X, Li J, Tan R, Zhong X, Yang J, Wang L. Identification of Hub Genes Associated with the Development of Acute Kidney Injury by Weighted Gene Co-Expression Network Analysis. Kidney Blood Press Res 2021; 46:63-73. [PMID: 33401265 DOI: 10.1159/000511661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/17/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a severe clinical syndrome, causing a profound medical and socioeconomic burden worldwide. This study aimed to explore underlying molecular targets related to the progression of AKI. METHODS A public database originated from the NCBI GEO database (serial number: GSE121190) and a well-established and unbiased method of weighted gene co-expression network analysis (WGCNA) to identify hub genes and potential pathways were used. Furthermore, the unbiased hub genes were validated in 2 classic models of AKI in a rodent model: chemically established AKI by cisplatin- and ischemia reperfusion-induced AKI. RESULTS A total of 17 modules were finally obtained by the unbiased method of WGCNA, where the genes in turquoise module displayed strong correlation with the development of AKI. In addition, the results of gene ontology revealed that the genes in turquoise module were involved in renal injury and renal fibrosis. Thus, the hub genes were further validated by experimental methods and primarily obtained Rplp1 and Lgals1 as key candidate genes related to the progression of AKI by the advantage of quantitative PCR, Western blotting, and in situ tissue fluorescence. Importantly, the expression of Rplp1 and Lgals1 at the protein level showed positive correlation with renal function, including serum Cr and BUN. CONCLUSIONS By the advantage of unbiased bioinformatic method and consequent experimental verification, this study lays the foundation basis for the pathogenesis and therapeutic agent development of AKI.
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Affiliation(s)
- Xiao Lin
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China
| | - Jianchun Li
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China
| | - Ruizhi Tan
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China
| | - Xia Zhong
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China
| | - Jieke Yang
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China
| | - Li Wang
- Laboratory of Organ Fibrosis Prophylaxis and Treatment by Combine Traditional Chinese and Western Medicine, Research Center of Integrated Traditional Chinese and Western Medicine, Affiliated Traditional Medicine Hospital, Southwest Medical University, Luzhou, China,
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Wu Z, Hai E, Di Z, Ma R, Shang F, Wang Y, Wang M, Liang L, Rong Y, Pan J, Wu W, Su R, Wang Z, Wang R, Zhang Y, Li J. Using WGCNA (weighted gene co-expression network analysis) to identify the hub genes of skin hair follicle development in fetus stage of Inner Mongolia cashmere goat. PLoS One 2020; 15:e0243507. [PMID: 33351808 PMCID: PMC7755285 DOI: 10.1371/journal.pone.0243507] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 11/20/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Mature hair follicles represent an important stage of hair follicle development, which determines the stability of hair follicle structure and its ability to enter the hair cycle. Here, we used weighted gene co-expression network analysis (WGCNA) to identify hub genes of mature skin and hair follicles in Inner Mongolian cashmere goats. METHODS We used transcriptome sequencing data for the skin of Inner Mongolian cashmere goats from fetal days 45-135 days, and divided the co expressed genes into different modules by WGCNA. Characteristic values were used to screen out modules that were highly expressed in mature skin follicles. Module hub genes were then selected based on the correlation coefficients between the gene and module eigenvalue, gene connectivity, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The results were confirmed by quantitative polymerase chain reaction (qPCR). RESULTS Ten modules were successfully defined, of which one, with a total of 3166 genes, was selected as a specific module through sample and gene expression pattern analyses. A total of 584 candidate hub genes in the module were screened by the correlation coefficients between the genes and module eigenvalue and gene connectivity. Finally, GO/KEGG functional enrichment analyses detected WNT10A as a key gene in the development and maturation of skin hair follicles in fetal Inner Mongolian cashmere goats. qPCR showed that the expression trends of 13 genes from seven fetal skin samples were consistent with the sequencing results, indicating that the sequencing results were reliable.n.
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Affiliation(s)
- Zhihong Wu
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Erhan Hai
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Zhengyang Di
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Rong Ma
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Fangzheng Shang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Yu Wang
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Min Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Lili Liang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Youjun Rong
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Jianfeng Pan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Wenbin Wu
- Zhenlai Hehe Animal Husbandry Development Co., Ltd, Baicheng, China
| | - Rui Su
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Zhiying Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Ruijun Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Yanjun Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
- Engineering Research Center for Goat Genetics and Breeding, Hohhot, Inner Mongolia Autonomous Region, China
- * E-mail: (JL); , (YZ)
| | - Jinquan Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Hohhot, Inner Mongolia Autonomous Region, China
- Key Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture, Hohhot, China
- Engineering Research Center for Goat Genetics and Breeding, Hohhot, Inner Mongolia Autonomous Region, China
- * E-mail: (JL); , (YZ)
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WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11. Biosci Rep 2020; 40:226138. [PMID: 32812032 PMCID: PMC7468096 DOI: 10.1042/bsr20200935] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 08/13/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023] Open
Abstract
Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of the present study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in ‘superoxide anion generation’ and ‘complement and coagulation cascades’. Finally, we validated the key genes by Gene Expression Profiling Interactive Analysis (GEPIA) and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in the present study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.
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Zhu N, Hou J. Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma. Cancer Cell Int 2020; 20:577. [PMID: 33292275 PMCID: PMC7709254 DOI: 10.1186/s12935-020-01672-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas. METHOD The "CIBERSORT" algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the "ESTIMATE" algorithm was used to assess the "Estimate," "Immune," and "Stromal" scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the "clusterProfiler" package in R for annotation and visualization. RESULTS Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. CONCLUSION Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.
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Affiliation(s)
- Naiqiang Zhu
- Department of Minimally Invasive Spinal Surgery, Affiliated Hospital of Chengde Medical College, Chengde, 067000, China
| | - Jingyi Hou
- Department of Minimally Invasive Spinal Surgery, Affiliated Hospital of Chengde Medical College, Chengde, 067000, China.
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Zeng D, He S, Ma C, Wen Y, Song W, Xu Q, Zhao N, Wang Q, Yu Y, Shen Y, Huang J, Li H. Network-based approach to identify molecular signatures in the brains of depressed suicides. Psychiatry Res 2020; 294:113513. [PMID: 33137553 DOI: 10.1016/j.psychres.2020.113513] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 10/14/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Suicide is a serious and global health problem that has a strong association with major depressive disorder (MDD). Weighted gene co-expression network analysis (WGCNA) was performed for the construction of a co-expression network to get important gene modules associated with depressed suicide. METHODS Transcriptome sequencing data from dorsolateral prefrontal cortex was used, which included 29 non-psychiatric controls (CON), 21 MDD suicides (MDD-S) and 9 MDD non-suicides (MDD-NS) of medication-free sudden death individuals. RESULTS The highest correlation in the module-traits relationship was discovered between the black module and suicide (r = -0.30, p = 0.024) as well as MDD (r = -0.34, p = 0.010).Furthermore, the expression levels of genes decreased progressively across the three groups (CON>MDD-NS>MDD-S). Therefore, the genes in the black module was selected for subsequent analyses. Protein-Protein Interaction Network found that the top 10 hub genes were somehow involved in depressed suicide including JUN, FOS, ATF3, MYC, EGR1, FOSB, DUSP1, NFKBIA, TLR2, NR4A1. Most of the GO terms were enriched in cell death and apoptosis and KEGG was mainly enriched in MAPK pathway. Cell Type-Specific Analysis found these genes were significantly enriched in endothelial and microglia (p<0.000) cell types. In addition, 92 genes in this module had at least one highly significant differentially methylated positions between MDD-S and controls. CONCLUSION Cell death and apoptosis may participate in the interplay between depressed suicide and neuro-inflammation system.
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Affiliation(s)
- Duan Zeng
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Shen He
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Changlin Ma
- Shanghai Jiading District Mental Health Center, Shanghai, PR China
| | - Yi Wen
- Shanghai Jiading District Mental Health Center, Shanghai, PR China
| | - Weichen Song
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
| | - Qingqing Xu
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Nan Zhao
- Department of Psychiatry, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, PR China
| | - Qiang Wang
- Department of Psychiatry, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, PR China
| | - Yimin Yu
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China; Shanghai Clinical Research Center for Mental Health, Shanghai, PR China
| | - Yifeng Shen
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China; Shanghai Clinical Research Center for Mental Health, Shanghai, PR China
| | - Jingjing Huang
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China; Shanghai Clinical Research Center for Mental Health, Shanghai, PR China.
| | - Huafang Li
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China; Shanghai Clinical Research Center for Mental Health, Shanghai, PR China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China.
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Ma Z, Shen Z, Gong Y, Zhou J, Chen X, Lv Q, Wang M, Chen J, Yu M, Fu G, He H, Lai D. Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1348. [PMID: 33313093 PMCID: PMC7723587 DOI: 10.21037/atm-20-3337] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Myocarditis is an inflammatory myocardial disease, which may lead to heart failure and sudden death. Despite extensive research into the pathogenesis of myocarditis, effective treatments for this condition remain elusive. This study aimed to explore the potential pathogenesis and hub genes for viral myocarditis. Methods A weighted gene co-expression network analysis (WGCNA) was performed based on the gene expression profiles derived from mouse models at different stages of viral myocarditis (GSE35182). Functional annotation was executed within the key modules. Potential hub genes were predicted based on the intramodular connectivity (IC). Finally, potential microRNAs that regulate gene expression were predicted by miRNet analysis. Results Three gene co-expression modules showed the strongest correlation with the acute or chronic disease stage. A significant positive correlation was detected between the acute disease stage and the turquoise module, the genes of which were mainly enriched in antiviral response and immune-inflammatory activation. Furthermore, a significant positive correlation and a negative correlation were identified between the chronic disease stage and the brown and yellow modules, respectively. These modules were mainly associated with the cytoskeleton, phosphorylation, cellular catabolic process, and autophagy. Subsequently, we predicted the underlying hub genes and microRNAs in the three modules. Conclusions This study revealed the main biological processes in different stages of viral myocarditis and predicted hub genes in both the acute and chronic disease stages. Our results may be helpful for developing new therapeutic targets for viral myocarditis in future research.
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Affiliation(s)
- Zetao Ma
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhida Shen
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingchao Gong
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoou Chen
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingbo Lv
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Meihui Wang
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiawen Chen
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mei Yu
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guosheng Fu
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hong He
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dongwu Lai
- Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Xiang C, Cong S, Liang B, Cong S. Bioinformatic gene analysis for potential therapeutic targets of Huntington's disease in pre-symptomatic and symptomatic stage. J Transl Med 2020; 18:388. [PMID: 33054835 PMCID: PMC7559361 DOI: 10.1186/s12967-020-02549-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 09/27/2020] [Indexed: 01/18/2023] Open
Abstract
Background Huntington’s disease (HD) is a neurodegenerative disorder characterized by psychiatric symptoms, serious motor and cognitive deficits. Certain pathological changes can already be observed in pre-symptomatic HD (pre-HD) patients; however, the underlying molecular pathogenesis is still uncertain and no effective treatments are available until now. Here, we reanalyzed HD-related differentially expressed genes from the GEO database between symptomatic HD patients, pre-HD individuals, and healthy controls using bioinformatics analysis, hoping to get more insight in the pathogenesis of both pre-HD and HD, and shed a light in the potential therapeutic targets of the disease. Methods Pre-HD and symptomatic HD differentially expressed genes (DEGs) were screened by bioinformatics analysis Gene Expression Omnibus (GEO) dataset GSE1751. A protein–protein interaction (PPI) network was used to select hub genes. Subsequently, Gene Ontology (GO) enrichment analysis of DEGs and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of hub genes were applied. Dataset GSE24250 was downloaded to verify our hub genes by the Kaplan–Meier method using Graphpad Prism 5.0. Finally, target miRNAs of intersected hub genes involved in pre-HD and symptomatic HD were predicted. Results A total of 37 and 985 DEGs were identified in pre-HD and symptomatic HD, respectively. The hub genes, SIRT1, SUZ12, and PSMC6, may be implicated in pre-HD, and the hub genes, FIS1, SIRT1, CCNH, SUZ12, and 10 others, may be implicated in symptomatic HD. The intersected hub genes, SIRT1 and SUZ12, and their predicted target miRNAs, in particular miR-22-3p and miR-19b, may be significantly associated with pre-HD. Conclusion The PSMC6, SIRT1, and SUZ12 genes and their related ubiquitin-mediated proteolysis, transcriptional dysregulation, and histone metabolism are significantly associated with pre-HD. FIS1, CCNH, and their related mitochondrial disruption and transcriptional dysregulation processes are related to symptomatic HD, which might shed a light on the elucidation of potential therapeutic targets in HD.
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Affiliation(s)
- Chunchen Xiang
- Department of Neurology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, People's Republic of China
| | - Shengri Cong
- Department of Neurology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, People's Republic of China
| | - Bin Liang
- Bioinformatics of Department, School of Life Sciences, China Medical University, Shenyang, China
| | - Shuyan Cong
- Department of Neurology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning, People's Republic of China.
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Clinical Features and Prognostic Impact of Coexpression Modules Constructed by WGCNA for Diffuse Large B-Cell Lymphoma. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7947208. [PMID: 32596373 PMCID: PMC7298280 DOI: 10.1155/2020/7947208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 02/06/2020] [Accepted: 05/04/2020] [Indexed: 12/11/2022]
Abstract
Objective Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive malignant tumor, accounting for 30-40% of non-Hodgkin's lymphoma. Our aim was to construct novel prognostic models of candidate genes based on clinical features. Methods RNA-seq and clinical data of DLBCL were retrieved from TCGA database. Coexpression modules were constructed by WGCNA. Then, we investigated the interactions between modules and clinical features. By overall survival analysis, prognostic candidate genes from modules of interest were identified. A coexpression network of prognostic candidate genes was then constructed through WGCNA. GEPIA was used to analyze the expression of a candidate gene between DLBCL and normal samples. Results 19 coexpression modules were constructed by 12813 genes from 52 DLBCL samples. The number of genes in modules ranged from 34 to 5457. We found that the purple module was significantly related with histological type (p value = 1e-04). Overall survival analysis revealed that MAFA-AS1, hsa-mir-338, and hsa-mir-891a were related with prognosis of DLBCL (p value = 0.027, 0.039, and 0.022, respectively). A coexpression network was constructed for the three prognostic genes. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA, we found that MAFA-AS1 showed low expression in DLBCL samples (p < 0.01). Conclusion We constructed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers and therapeutic targets of DLBCL.
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Wang M, Wang L, Pu L, Li K, Feng T, Zheng P, Li S, Sun M, Yao Y, Jin L. LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA). Genomics 2020; 112:2302-2308. [DOI: 10.1016/j.ygeno.2020.01.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/09/2019] [Accepted: 01/06/2020] [Indexed: 12/13/2022]
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Malamon JS, Kriete A. Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease. Front Neurosci 2020; 14:228. [PMID: 32265636 PMCID: PMC7099620 DOI: 10.3389/fnins.2020.00228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 03/02/2020] [Indexed: 12/16/2022] Open
Abstract
We have applied a novel and integrative analysis framework for next-generation sequencing (NGS) data to 503 human subjects provided by the Religious Orders Study and Memory and Aging Project (ROSMAP) to examine changes in transcriptomic organization and common variants in association with late-onset Alzheimer's disease (LOAD). Our framework identified seven reproducible, co-regulated modules after quality control (QC), clinical segregation, preservation filtering, and functional ontology analysis. These modules were specifically enriched in several innate and adaptive immune system processes, the synaptic vesicle cycle, and Hippo signaling. Topological and functional erosion of these modules due to shedding of genes and loss of in-module connectivity was diagnostic of disease progression. Perturbation analysis revealed that only 1% of eQTLs overlapped genes participating in these co-regulated modules. Common variants nevertheless identified components of the immune systems like human leukocyte antigen (HLA) complex and microtubule-associated protein tau (MAPT) regions in association with LOAD. Our results implicate microglial function, adaptive immune response, and the structural degeneration of neurons as contributors to the transcriptional deregulation observed along with common genetic variants in the progression of LOAD.
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Affiliation(s)
- John Stephen Malamon
- Bossone Research Center, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Andres Kriete
- Bossone Research Center, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
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Lavin KM, Sealfon SC, McDonald MLN, Roberts BM, Wilk K, Nair VD, Ge Y, Lakshman Kumar P, Windham ST, Bamman MM. Skeletal muscle transcriptional networks linked to type I myofiber grouping in Parkinson's disease. J Appl Physiol (1985) 2020; 128:229-240. [PMID: 31829804 PMCID: PMC7052589 DOI: 10.1152/japplphysiol.00702.2019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/20/2019] [Accepted: 12/06/2019] [Indexed: 12/11/2022] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder impacting cognition, movement, and quality of life in >10 million individuals worldwide. We recently characterized and quantified a skeletal muscle pathology in PD represented by exaggerated type I myofiber grouping presumed to result from denervation-reinnervation processes. Our previous findings indicated that impaired neuromuscular junction integrity may be involved in type I grouping, which is associated with excessive motor unit activation during weight-bearing tasks. In this study, we performed transcriptional profiling to test the hypothesis that type I grouping severity would link to distinct gene expression networks. We generated transcriptome-wide poly(A) RNA-Seq data from skeletal muscle of individuals with PD [n = 12 (9 men, 3 women); 67 ± 2 yr], age- and sex-matched older adults (n = 12; 68 ± 2 yr), and sex-matched young adults (n = 12; 30 ± 1 yr). Differentially expressed genes were evaluated across cohorts. Weighted gene correlation network analysis (WGCNA) was performed to identify gene networks most correlated with indicators of abnormal type I grouping. Among coexpression networks mapping to phenotypes pathologically increased in PD muscle, one network was highly significantly correlated to type I myofiber group size and another to percentage of type I myofibers found in groups. Annotation of coexpressed networks revealed that type I grouping is associated with altered expression of genes involved in neural development, postsynaptic signaling, cell cycle regulation and cell survival, protein and energy metabolism, inflammation/immunity, and posttranscriptional regulation (microRNAs). These transcriptomic findings suggest that skeletal muscle may play an active role in signaling to promote myofiber survival, reinnervation, and remodeling, perhaps to an extreme in PD.NEW & NOTEWORTHY Despite our awareness of the impact of Parkinson's disease (PD) on motor function for over two centuries, limited attention has focused on skeletal muscle. We previously identified type I myofiber grouping, a novel indicator of muscle dysfunction in PD, presumably a result of heightened rates of denervation/reinnervation. Using transcriptional profiling to identify networks associated with this phenotype, we provide insight into potential mechanistic roles of skeletal muscle in signaling to promote its survival in PD.
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Affiliation(s)
- Kaleen M Lavin
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Merry-Lynn N McDonald
- Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brandon M Roberts
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Katarzyna Wilk
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Venugopalan D Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Preeti Lakshman Kumar
- Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Samuel T Windham
- Department of Surgery, University of Alabama at Birmingham, Birmingham, Alabama
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Marcas M Bamman
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs Medical Center, Birmingham, Alabama
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Monaco A, Amoroso N, Bellantuono L, Lella E, Lombardi A, Monda A, Tateo A, Bellotti R, Tangaro S. Shannon entropy approach reveals relevant genes in Alzheimer's disease. PLoS One 2019; 14:e0226190. [PMID: 31891941 PMCID: PMC6938408 DOI: 10.1371/journal.pone.0226190] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 11/19/2019] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer’s disease.
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Affiliation(s)
- Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
- * E-mail:
| | - Loredana Bellantuono
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Eufemia Lella
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Anna Monda
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Andrea Tateo
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
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Shen Z, Lu J, Wei J, Zhao J, Wang M, Wang M, Shen X, Lü X, Zhou B, Zhao Y, Fu G. Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:664. [PMID: 31930065 DOI: 10.21037/atm.2019.10.43] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury. Methods A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools. Results A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module. Conclusions Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.
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Affiliation(s)
- Zhida Shen
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Jiangting Lu
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Jiejin Wei
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.,Department of Electrocardiogram, Shaoxing People's Hospital, Shaoxing 312000, China
| | - Juanjuan Zhao
- Biomedical Research Center, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Meihui Wang
- Biomedical Research Center, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Ming Wang
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Xiaohua Shen
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Xue Lü
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Binquan Zhou
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Yanbo Zhao
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Guosheng Fu
- Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
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Liu Y, Gu HY, Zhu J, Niu YM, Zhang C, Guo GL. Identification of Hub Genes and Key Pathways Associated With Bipolar Disorder Based on Weighted Gene Co-expression Network Analysis. Front Physiol 2019; 10:1081. [PMID: 31481902 PMCID: PMC6710482 DOI: 10.3389/fphys.2019.01081] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 08/07/2019] [Indexed: 12/11/2022] Open
Abstract
Bipolar disorder (BD) is a complex mental disorder with high mortality and disability rates worldwide; however, research on its pathogenesis and diagnostic methods remains limited. This study aimed to elucidate potential candidate hub genes and key pathways related to BD in a pre-frontal cortex sample. Raw gene expression profile files of GSE53987, including 36 samples, were obtained from the gene expression omnibus (GEO) database. After data pre-processing, 10,094 genes were selected for weighted gene co-expression network analysis (WGCNA). After dividing highly related genes into 19 modules, we found that the pink, midnight blue, and brown modules were highly correlated with BD. Functional annotation and pathway enrichment analysis for modules, which indicated some key pathways, were conducted based on the Enrichr database. One of the most remarkable significant pathways is the Hippo signaling pathway and its positive transcriptional regulation. Finally, 30 hub genes were identified in three modules. Hub genes with a high degree of connectivity in the PPI network are significantly enriched in positive regulation of transcription. In addition, the hub genes were validated based on another dataset (GSE12649). Taken together, the identification of these 30 hub genes and enrichment pathways might have important clinical implications for BD treatment and diagnosis.
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Affiliation(s)
- Yang Liu
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Hui-Yun Gu
- Department of Orthopedic, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jie Zhu
- Trade Union, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yu-Ming Niu
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Chao Zhang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Guang-Ling Guo
- Center of Women’s Health Sciences, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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Xu T, Ruan H, Song Z, Cao Q, Wang K, Bao L, Liu D, Tong J, Yang H, Chen K, Zhang X. Identification of CXCL13 as a potential biomarker in clear cell renal cell carcinoma via comprehensive bioinformatics analysis. Biomed Pharmacother 2019; 118:109264. [PMID: 31390578 DOI: 10.1016/j.biopha.2019.109264] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 07/19/2019] [Accepted: 07/24/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system. However, there are still no reliable biomarkers for the diagnosis and prognosis of ccRCC. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for ccRCC. METHODS Differentially expressed genes (DEGs) were screened using NetworkAnalyst. Protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were utilized to identify hub genes. Then, we assessed the prognostic and diagnostic values of hub genes to screen candidate biomarkers. Gene Set Enrichment Analysis (GSEA) was applied to reveal potential mechanisms of candidate biomarkers in ccRCC. Oncomine database and The Human Protein Atlas were used to verify the expression of candidate biomarkers online. In addition, qRT-PCR, Enzyme linked immunosorbent assay (ELISA) and Immunohistochemistry (IHC) assays were performed to validate the expression level of candidate biomarkers in ccRCC cells and tissues. RESULTS A total of 771 genes were identified as DEGs. GO function analysis showed that DEGs were mostly enriched in excretion, apical part of cell and monovalent inorganic cation transmembrane transporter activity. KEGG pathway analysis demonstrated that DEGs were mostly involved in Neuroactive ligand-receptor interaction. After utilizing PPI network and WGCNA, nine genes (IFNG, CXCR3, PMCH, CD2, FASLG, CXCL13, CD8A, CD3D and GZMA) were identified as the hub genes. Moreover, survival analysis exhibited that high expression of CXCL13 predicted poor survival in both overall survival (OS) and disease free survival (DFS). The ROC curves indicated that CXCL13 could distinguish ccRCC samples from normal kidney samples. High expression of CXCL13 group was mostly associated with RB and MEL18 pathways by GSEA. Furthermore, qRT-PCR, ELISA and IHC results showed that the expression of CXCL13 was elevated in ccRCC. CONCLUSIONS Our study illustrated that CXCL13 had good diagnostic and prognostic value, which may become a candidate biomarker and therapeutic target for ccRCC.
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Affiliation(s)
- Tianbo Xu
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Hailong Ruan
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Zhengshuai Song
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Qi Cao
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Keshan Wang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Lin Bao
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Di Liu
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Junwei Tong
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Hongmei Yang
- Department of Pathogenic Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Ke Chen
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Xiaoping Zhang
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
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Yu B, Jin Y, Shen Y, Yang Y, Wang G, Zhu H, Yu Y, Wang J. Loss of homeoprotein Msx1 and Msx2 leading to athletic and kinematic impairment related to the increasing neural excitability of neurons in aberrant neocortex in mice. Biochem Biophys Res Commun 2019; 516:229-235. [PMID: 31221479 DOI: 10.1016/j.bbrc.2019.05.170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 05/29/2019] [Indexed: 10/26/2022]
Abstract
Although homeoproteins Msx1 and Msx2, the cell-specific transcription regulators, have been proven to play multiple roles in the embryogenesis of bone, muscle and tooth, the functions and mechanisms of Msx1 and Msx2 in the development of the central nervous system of mice after birth are not clear because of the death of Msx1 and Msx1/2 germline-deleted embryo at late gestation of mouse. In current research, Nestin-Cre mice was introduced to generate the central nervous system-specific knockout mice (Nestin-Cre;Msx1,Msx2fl/fl). We found that besides the falling of the body mass and the brain volume, the cortical tissue sections and staining showed the decreasing thickness of layer II-IV and declining number of vertebral cells in layer V resulting from Msx1/2 deletion. In addition, electrophysiological tests revealed the aberrant action potential parameters of deep pyramidal neurons in Nestin-Cre;Msx1,2 fl/fl mice, which may be related with the ethology impairment displayed in further experiments. We discovered Nestin-Cre;Msx1,2 fl/fl mice had severe impairment in their athletic ability and kinematic learning ability in rotate test, and exhibited hyperactivity in open-field test. Above all, our results revealed that deletion of homeoproteins Msx1 and Msx2 could lead to behavioral disorders and suggested that Msx1 and Msx2 played a crucial role in regulating the development and function of the neocortex. In addition, our current research provided a new mouse model for understanding the pathogenesis of human central nervous system disease.
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Affiliation(s)
- Bin Yu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center of Genetics, and Development, School of Life Sciences and Zhongshan Hospital, Fudan University, Shanghai, 200438, China
| | - Yuqing Jin
- State Key Laboratory of Medical Neurobiology, School of Life Science and Human Phenome Institute, Institute of Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yi Shen
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center of Genetics, and Development, School of Life Sciences and Zhongshan Hospital, Fudan University, Shanghai, 200438, China
| | - Yenan Yang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center of Genetics, and Development, School of Life Sciences and Zhongshan Hospital, Fudan University, Shanghai, 200438, China
| | - Gang Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center of Genetics, and Development, School of Life Sciences and Zhongshan Hospital, Fudan University, Shanghai, 200438, China
| | - Haiying Zhu
- Department of Cell Biology, Second Military Medical University, Shanghai, China.
| | - Yuguo Yu
- State Key Laboratory of Medical Neurobiology, School of Life Science and Human Phenome Institute, Institute of Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Jingqiang Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center of Genetics, and Development, School of Life Sciences and Zhongshan Hospital, Fudan University, Shanghai, 200438, China.
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