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Xue W, He W, Yan M, Zhao H, Pi J. Exploring Shared Biomarkers of Myocardial Infarction and Alzheimer's Disease via Single-Cell/Nucleus Sequencing and Bioinformatics Analysis. J Alzheimers Dis 2023; 96:705-723. [PMID: 37840493 PMCID: PMC10657707 DOI: 10.3233/jad-230559] [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: 09/04/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Patients are at increased risk of dementia, including Alzheimer's disease (AD), after myocardial infarction (MI), but the biological link between MI and AD is unclear. OBJECTIVE To understand the association between the pathogenesis of MI and AD and identify common biomarkers of both diseases. METHODS Using public databases, we identified common biomarkers of MI and AD. Least absolute shrinkage and selection operator (LASSO) regression and protein-protein interaction (PPI) network were performed to further screen hub biomarkers. Functional enrichment analyses were performed on the hub biomarkers. Single-cell/nucleus analysis was utilized to further analyze the hub biomarkers at the cellular level in carotid atherosclerosis and AD datasets. Motif enrichment analysis was used to screen key transcription factors. RESULTS 26 common differentially expressed genes were screened between MI and AD. Function enrichment analyses showed that these differentially expressed genes were mainly associated with inflammatory pathways. A key gene, Regulator of G-protein Signaling 1 (RGS1), was obtained by LASSO regression and PPI network. RGS1 was confirmed to mainly express in macrophages and microglia according to single-cell/nucleus analysis. The difference in expression of RGS1 in macrophages and microglia between disease groups and controls was statistically significant (p < 0.0001). The expression of RGS1 in the disease groups was upregulated with the differentiation of macrophages and microglia. RelA was a key transcription factor regulating RGS1. CONCLUSION Macrophages and microglia are involved in the inflammatory response of MI and AD. RGS1 may be a key biomarker in this process.
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Affiliation(s)
- Weiqi Xue
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Weifeng He
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Mengyuan Yan
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Huanyi Zhao
- First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Jianbin Pi
- Department of Cardiovascular Disease, The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Danielsson H, Tebani A, Zhong W, Fagerberg L, Brusselaers N, Hård AL, Uhlén M, Hellström A. Blood protein profiles related to preterm birth and retinopathy of prematurity. Pediatr Res 2022; 91:937-946. [PMID: 33895781 PMCID: PMC9064798 DOI: 10.1038/s41390-021-01528-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/25/2021] [Accepted: 03/30/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Nearly one in ten children is born preterm. The degree of immaturity is a determinant of the infant's health. Extremely preterm infants have higher morbidity and mortality than term infants. One disease affecting extremely preterm infants is retinopathy of prematurity (ROP), a multifactorial neurovascular disease that can lead to retinal detachment and blindness. The advances in omics technology have opened up possibilities to study protein expressions thoroughly with clinical accuracy, here used to increase the understanding of protein expression in relation to immaturity and ROP. METHODS Longitudinal serum protein profiles the first months after birth in 14 extremely preterm infants were integrated with perinatal and ROP data. In total, 448 unique protein targets were analyzed using Proximity Extension Assays. RESULTS We found 20 serum proteins associated with gestational age and/or ROP functioning within mainly angiogenesis, hematopoiesis, bone regulation, immune function, and lipid metabolism. Infants with severe ROP had persistent lower levels of several identified proteins during the first postnatal months. CONCLUSIONS The study contributes to the understanding of the relationship between longitudinal serum protein levels and immaturity and abnormal retinal neurovascular development. This is essential for understanding pathophysiological mechanisms and to optimize diagnosis, treatment and prevention for ROP. IMPACT Longitudinal protein profiles of 14 extremely preterm infants were analyzed using a novel multiplex protein analysis platform combined with perinatal data. Proteins associated with gestational age at birth and the neurovascular disease ROP were identified. Among infants with ROP, longitudinal levels of the identified proteins remained largely unchanged during the first postnatal months. The main functions of the proteins identified were angiogenesis, hematopoiesis, immune function, bone regulation, lipid metabolism, and central nervous system development. The study contributes to the understanding of longitudinal serum protein patterns related to gestational age and their association with abnormal retinal neuro-vascular development.
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Affiliation(s)
- Hanna Danielsson
- grid.4714.60000 0004 1937 0626Department of Microbiology, Tumor and Cell Biology, Centre for Translational Microbiome Research, Karolinska Institutet, Stockholm, Sweden ,grid.416648.90000 0000 8986 2221Sach’s Children’s and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Abdellah Tebani
- grid.5037.10000000121581746Science for Life Laboratory, Department of Protein Science, KTH—Royal Institute of Technology, Stockholm, Sweden ,grid.41724.340000 0001 2296 5231Department of Metabolic Biochemistry, Rouen University Hospital, Rouen, France ,grid.41724.340000 0001 2296 5231Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, Rouen, France
| | - Wen Zhong
- grid.5037.10000000121581746Science for Life Laboratory, Department of Protein Science, KTH—Royal Institute of Technology, Stockholm, Sweden
| | - Linn Fagerberg
- grid.5037.10000000121581746Science for Life Laboratory, Department of Protein Science, KTH—Royal Institute of Technology, Stockholm, Sweden
| | - Nele Brusselaers
- grid.4714.60000 0004 1937 0626Department of Microbiology, Tumor and Cell Biology, Centre for Translational Microbiome Research, Karolinska Institutet, Stockholm, Sweden ,grid.5284.b0000 0001 0790 3681Global Health Institute, Antwerp University, Antwerp, Belgium ,grid.5342.00000 0001 2069 7798Department of Head and Skin, Ghent University, Ghent, Belgium
| | - Anna-Lena Hård
- grid.1649.a000000009445082XThe Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mathias Uhlén
- grid.5037.10000000121581746Science for Life Laboratory, Department of Protein Science, KTH—Royal Institute of Technology, Stockholm, Sweden
| | - Ann Hellström
- The Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
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4
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Wu X, Peng C, Nelson PT, Cheng Q. Random forest-integrated analysis in AD and LATE brain transcriptome-wide data to identify disease-specific gene expression. PLoS One 2021; 16:e0256648. [PMID: 34492068 PMCID: PMC8423259 DOI: 10.1371/journal.pone.0256648] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects thinking, memory, and behavior. Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently identified common neurodegenerative disease that mimics the clinical symptoms of AD. The development of drugs to prevent or treat these neurodegenerative diseases has been slow, partly because the genes associated with these diseases are incompletely understood. A notable hindrance from data analysis perspective is that, usually, the clinical samples for patients and controls are highly imbalanced, thus rendering it challenging to apply most existing machine learning algorithms to directly analyze such datasets. Meeting this data analysis challenge is critical, as more specific disease-associated gene identification may enable new insights into underlying disease-driving mechanisms and help find biomarkers and, in turn, improve prospects for effective treatment strategies. In order to detect disease-associated genes based on imbalanced transcriptome-wide data, we proposed an integrated multiple random forests (IMRF) algorithm. IMRF is effective in differentiating putative genes associated with subjects having LATE and/or AD from controls based on transcriptome-wide data, thereby enabling effective discrimination between these samples. Various forms of validations, such as cross-domain verification of our method over other datasets, improved and competitive classification performance by using identified genes, effectiveness of testing data with a classifier that is completely independent from decision trees and random forests, and relationships with prior AD and LATE studies on the genes linked to neurodegeneration, all testify to the effectiveness of IMRF in identifying genes with altered expression in LATE and/or AD. We conclude that IMRF, as an effective feature selection algorithm for imbalanced data, is promising to facilitate the development of new gene biomarkers as well as targets for effective strategies of disease prevention and treatment.
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Affiliation(s)
- Xinxing Wu
- University of Kentucky, Lexington, Kentucky, United States of America
| | - Chong Peng
- Qingdao University, Qingdao, Shandong, China
| | - Peter T. Nelson
- University of Kentucky, Lexington, Kentucky, United States of America
| | - Qiang Cheng
- University of Kentucky, Lexington, Kentucky, United States of America
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5
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Onaolapo OJ, Onaolapo AY, Olowe OA, Udoh MO, Udoh DO, Nathaniel TI. Melatonin and Melatonergic Influence on Neuronal Transcription Factors: Implications for the Development of Novel Therapies for Neurodegenerative Disorders. Curr Neuropharmacol 2021; 18:563-577. [PMID: 31885352 PMCID: PMC7457420 DOI: 10.2174/1570159x18666191230114339] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 12/16/2019] [Accepted: 12/28/2019] [Indexed: 01/04/2023] Open
Abstract
Melatonin is a multifunctional signalling molecule that is secreted by the mammalian pineal gland, and also found in a number of organisms including plants and bacteria. Research has continued to uncover an ever-increasing number of processes in which melatonin is known to play crucial roles in mammals. Amongst these functions is its contribution to cell multiplication, differentiation and survival in the brain. Experimental studies show that melatonin can achieve these functions by influencing transcription factors which control neuronal and glial gene expression. Since neuronal survival and differentiation are processes that are important determinants of the pathogenesis, course and outcome of neurodegenerative disorders; the known and potential influences of melatonin on neuronal and glial transcription factors are worthy of constant examination. In this review, relevant scientific literature on the role of melatonin in preventing or altering the course and outcome of neurodegenerative disorders, by focusing on melatonin's influence on transcription factors is examined. A number of transcription factors whose functions can be influenced by melatonin in neurodegenerative disease models have also been highlighted. Finally, the therapeutic implications of melatonin's influences have also been discussed and the potential limitations to its applications have been highlighted.
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Affiliation(s)
- Olakunle J. Onaolapo
- Behavioural Neuroscience/Neuropharmacology Unit, Department of Pharmacology, Ladoke Akintola University of Technology, Osogbo, Osun State, Nigeria
| | - Adejoke Y. Onaolapo
- Behavioural Neuroscience/Neurobiology Unit, Department of Anatomy, Ladoke Akintola University of Technology, Ogbomosho, Oyo State, Nigeria
| | - Olugbenga A. Olowe
- Molecular Bacteriology and Immunology Unit, Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Osogbo, Osun State, Nigeria
| | - Mojisola O. Udoh
- Department of Pathology, University of Benin Teaching Hospital, Benin City, Nigeria
| | - David O. Udoh
- Division of Neurological Surgery, Department of Surgery, University of Benin Teaching Hospital, Benin City, Edo State, Nigeria
| | - Thomas I. Nathaniel
- University of South Carolina School of Medicine-Greenville, Greenville, South Carolina, 29605, United States
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Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Lombardi A, Tateo A, Tangaro S, Bellotti R. Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach. Phys Biol 2020; 18:016003. [PMID: 33049726 DOI: 10.1088/1478-3975/abc09a] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.
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Affiliation(s)
- A Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
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