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Rigby Dames BA, Kilili H, Charvet CJ, Díaz-Barba K, Proulx MJ, de Sousa AA, Urrutia AO. Evolutionary and genomic perspectives of brain aging and neurodegenerative diseases. PROGRESS IN BRAIN RESEARCH 2023; 275:165-215. [PMID: 36841568 PMCID: PMC11191546 DOI: 10.1016/bs.pbr.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This chapter utilizes genomic concepts and evolutionary perspectives to further understand the possible links between typical brain aging and neurodegenerative diseases, focusing on the two most prevalent of these: Alzheimer's disease and Parkinson's disease. Aging is the major risk factor for these neurodegenerative diseases. Researching the evolutionary and molecular underpinnings of aging helps to reveal elements of the typical aging process that leave individuals more vulnerable to neurodegenerative pathologies. Very little is known about the prevalence and susceptibility of neurodegenerative diseases in nonhuman species, as only a few individuals have been observed with these neuropathologies. However, several studies have investigated the evolution of lifespan, which is closely connected with brain size in mammals, and insights can be drawn from these to enrich our understanding of neurodegeneration. This chapter explores the relationship between the typical aging process and the events in neurodegeneration. First, we examined how age-related processes can increase susceptibility to neurodegenerative diseases. Second, we assessed to what extent neurodegeneration is an accelerated form of aging. We found that while at the phenotypic level both neurodegenerative diseases and the typical aging process share some characteristics, at the molecular level they show some distinctions in their profiles, such as variation in genes and gene expression. Furthermore, neurodegeneration of the brain is associated with an earlier onset of cellular, molecular, and structural age-related changes. In conclusion, a more integrative view of the aging process, both from a molecular and an evolutionary perspective, may increase our understanding of neurodegenerative diseases.
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Affiliation(s)
- Brier A Rigby Dames
- Department of Computer Science, University of Bath, Bath, United Kingdom; Department of Psychology, University of Bath, Bath, United Kingdom.
| | - Huseyin Kilili
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom
| | - Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States
| | - Karina Díaz-Barba
- Licenciatura en Ciencias Genómicas, UNAM, CP62210, Cuernavaca, México; Instituto de Ecología, UNAM, Ciudad Universitaria, CP04510, Ciudad de México, México
| | - Michael J Proulx
- Department of Psychology, University of Bath, Bath, United Kingdom
| | | | - Araxi O Urrutia
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom; Licenciatura en Ciencias Genómicas, UNAM, CP62210, Cuernavaca, México; Instituto de Ecología, UNAM, Ciudad Universitaria, CP04510, Ciudad de México, México.
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Tang H, Sun L, Huang J, Yang Z, Li C, Zhou X. The mechanism and biomarker function of Cavin-2 in lung ischemia-reperfusion injury. Comput Biol Med 2022; 151:106234. [PMID: 36335812 DOI: 10.1016/j.compbiomed.2022.106234] [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: 08/31/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Lung Ischemia Reperfusion injury(LIRI) is one of the most predominant complications of ischemic lung disease. Cavin-2 emerged as a regulator of a variety of cellular processes, including endocytosis, lipid homeostasis, signal transduction and tumorigenesis, but the function of Cavin-2 in LIRI is unknown. The purpose of this study was to determine the predictive potential of Cavin-2 in protecting lung ischemia-reperfusion injury and its corresponding mechanisms. METHODS We found the strong relationship between Cavin-2 and multiple immune-related genes by deep learning method. To reveal the mechanism of Cavin-2 in LIRI, the LIRI SD rat model was constructed to detect the expression of Cavin-2 in the lung tissue of SD rats after LIRI, and the expression of Cavin-2 in lung cell lines was also detected. The expression of IL-6, IL-10 and MDA in cells after Cavin-2 over-expression or knockdown was examined under hypoxic conditions. The expression levels of p-AKT, p-STAT3 and p-ERK1/2 were measured in over-expressing Cavin-2 cells under hypoxic-ischemia conditions, and then the corresponding blockers of AKT, STAT3 and ERK1/2 were given to verify, whether they play a protective role in LIRI. RESULTS After hypoxia, the expression of Cavin-2 in rat lung tissues was significantly increased, and the cellular activity and IL-10 in Cavin-2 over-expressing cells were significantly higher than that of the control group, while IL-6 and MDA were significantly lower than that of the control group, while the above results were reversed in Cavin-2 knockdown cells; Meanwhile, the phosphorylation levels of AKT, STAT3, and ERK1/2 were significantly increased in Cavin-2 over-expression cells after hypoxia. When AKT, STAT3, and ERK1/2 specific blockers were given, they lost their protective effect against LIRI. CONCLUSIONS Cavin-2 shows biomarker potential in protecting lung from ischemia-reperfusion injury through the survivor activating factor enhancement (SAFE) and reperfusion injury salvage kinase (RISK) pathway.
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Affiliation(s)
- Hexiao Tang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Linao Sun
- Tianjin Medical University, Tianjin, China
| | - Jingyu Huang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zetian Yang
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Changsheng Li
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
| | - Xuefeng Zhou
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
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Ghiam S, Eslahchi C, Shahpasand K, Habibi-Rezaei M, Gharaghani S. Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease. Front Aging Neurosci 2022; 14:955461. [PMID: 36092798 PMCID: PMC9451601 DOI: 10.3389/fnagi.2022.955461] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/04/2022] [Indexed: 11/17/2022] Open
Abstract
Background Recent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments. Materials and methods In this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant ncRNAs were extracted. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed, and significant miRNAs and lncRNAs were identified. Results Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research investigation. Conclusion This study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.
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Affiliation(s)
- Shokoofeh Ghiam
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid-Beheshti University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Changiz Eslahchi,
| | - Koorosh Shahpasand
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology (RI-SCBT), Tehran, Iran
| | - Mehran Habibi-Rezaei
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- *Correspondence: Sajjad Gharaghani,
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Ahmed H, Soliman H, Elmogy M. Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree. Comput Biol Med 2022; 146:105622. [PMID: 35751201 DOI: 10.1016/j.compbiomed.2022.105622] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/18/2022]
Abstract
Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.
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Affiliation(s)
- Hala Ahmed
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Hassan Soliman
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Mohammed Elmogy
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt.
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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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Guo X, Zhou W, Yu Y, Cai Y, Zhang Y, Du A, Lu Q, Ding Y, Li C. Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease. Front Physiol 2021; 12:790086. [PMID: 34966294 PMCID: PMC8711098 DOI: 10.3389/fphys.2021.790086] [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: 10/25/2021] [Accepted: 11/17/2021] [Indexed: 11/28/2022] Open
Abstract
Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yan Yu
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yinghua Cai
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yuan Zhang
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Qun Lu
- Department of Nursing, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Chao Li
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
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Ma Y, Klein HU, De Jager PL. Considerations for integrative multi-omic approaches to explore Alzheimer's disease mechanisms. Brain Pathol 2021; 30:984-991. [PMID: 32654306 DOI: 10.1111/bpa.12878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 07/07/2020] [Indexed: 12/29/2022] Open
Abstract
The past decade has seen the maturation of multiple different forms of high-dimensional molecular profiling to the point that these methods could be deployed in initially hundreds and more recently thousands of human samples. In the field of Alzheimer's disease (AD), these profiles have been applied to the target organ: the aging brain. In a growing number of cases, the same samples were profiled with multiple different approaches, yielding genetic, transcriptomic, epigenomic and proteomic data. Here, we review lessons learned so far as we move beyond quantitative trait locus (QTL) analyses which map the effect of genetic variation on molecular features to integrate multiple levels of "omic" data in an effort to identify the molecular drivers of AD. One thing is clear: no single layer of molecular or "omic" data is sufficient to capture the variance of AD or aging-related cognitive decline. Nonetheless, reproducible findings are emerging from current efforts, and there is evidence of convergence using different approaches. Thus, we are on the cusp of an acceleration of truly integrative studies as the availability of large numbers of well-characterized brain samples profiled in three or more dimensions enables the testing, comparison and refinement of analytic methods with which to dissect the molecular architecture of the aging brain.
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Affiliation(s)
- Yiyi Ma
- Center for Translational and Computational Neuroimmunology, Department of Neurology, the Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, the Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, the Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY
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Kretzschmar GC, Alencar NM, da Silva SSL, Sulzbach CD, Meissner CG, Petzl-Erler ML, Souza RLR, Boldt ABW. GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs. Front Mol Biosci 2021; 8:632314. [PMID: 34291080 PMCID: PMC8287568 DOI: 10.3389/fmolb.2021.632314] [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: 11/23/2020] [Accepted: 05/31/2021] [Indexed: 01/06/2023] Open
Abstract
Several genome-wide association studies (GWAS) have been carried out with late-onset Alzheimer's disease (LOAD), mainly in European and Asian populations. Different polymorphisms were associated, but several of them without a functional explanation. GWAS are fundamental for identifying loci associated with diseases, although they often do not point to causal polymorphisms. In this sense, functional investigations are a fundamental tool for discovering causality, although the failure of this validation does not necessarily indicate a non-causality. Furthermore, the allele frequency of associated genetic variants may vary widely between populations, requiring replication of these associations in other ethnicities. In this sense, our study sought to replicate in 150 AD patients and 114 elderly controls from the South Brazilian population 18 single-nucleotide polymorphisms (SNPs) associated with AD in European GWAS, with further functional investigation using bioinformatic tools for the associated SNPs. Of the 18 SNPs investigated, only four were associated in our population: rs769449 (APOE), rs10838725 (CELF1), rs6733839, and rs744373 (BIN1-CYP27C1). We identified 54 variants in linkage disequilibrium (LD) with the associated SNPs, most of which act as expression or splicing quantitative trait loci (eQTLs/sQTLs) in genes previously associated with AD or with a possible functional role in the disease, such as CELF1, MADD, MYBPC3, NR1H3, NUP160, SPI1, and TOMM40. Interestingly, eight of these variants are located within long non-coding RNA (lncRNA) genes that have not been previously investigated regarding AD. Some of these polymorphisms can result in changes in these lncRNAs' secondary structures, leading to either loss or gain of microRNA (miRNA)-binding sites, deregulating downstream pathways. Our pioneering work not only replicated LOAD association with polymorphisms not yet associated in the Brazilian population but also identified six possible lncRNAs that may interfere in LOAD development. The results lead us to emphasize the importance of functional exploration of associations found in large-scale association studies in different populations to base personalized and inclusive medicine in the future.
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Affiliation(s)
- Gabriela Canalli Kretzschmar
- Laboratory of Human Molecular Genetics, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Nina Moura Alencar
- Laboratory of Human Molecular Genetics, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Saritha Suellen Lopes da Silva
- Laboratory of Polymorphism and Linkage, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Carla Daniela Sulzbach
- Laboratory of Polymorphism and Linkage, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Caroline Grisbach Meissner
- Laboratory of Human Molecular Genetics, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Maria Luiza Petzl-Erler
- Laboratory of Human Molecular Genetics, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Ricardo Lehtonen R. Souza
- Laboratory of Polymorphism and Linkage, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
| | - Angelica Beate Winter Boldt
- Laboratory of Human Molecular Genetics, Postgraduate Program in Genetics, Department of Genetics, Federal University of Paraná, Curitiba, Brazil
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Zhang J, Sun M, Zhao Y, Geng G, Hu Y. Identification of Gingivitis-Related Genes Across Human Tissues Based on the Summary Mendelian Randomization. Front Cell Dev Biol 2021; 8:624766. [PMID: 34026747 PMCID: PMC8134671 DOI: 10.3389/fcell.2020.624766] [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: 11/01/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
Periodontal diseases are among the most frequent inflammatory diseases affecting children and adolescents, which affect the supporting structures of the teeth and lead to tooth loss and contribute to systemic inflammation. Gingivitis is the most common periodontal infection. Gingivitis, which is mainly caused by a substance produced by microbial plaque, systemic disorders, and genetic abnormalities in the host. Identifying gingivitis-related genes across human tissues is not only significant for understanding disease mechanisms but also disease development and clinical diagnosis. The Genome-wide association study (GWAS) a commonly used method to mine disease-related genetic variants. However, due to some factors such as linkage disequilibrium, it is difficult for GWAS to identify genes directly related to the disease. Hence, we constructed a data integration method that uses the Summary Mendelian randomization (SMR) to combine the GWAS with expression quantitative trait locus (eQTL) data to identify gingivitis-related genes. Five eQTL studies from different human tissues and one GWAS studies were referenced in this paper. This study identified several candidates SNPs and genes relate to gingivitis in tissue-specific or cross-tissue. Further, we also analyzed and explained the functions of these genes. The R program for the SMR method has been uploaded to GitHub(https://github.com/hxdde/SMR).
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Affiliation(s)
- Jiahui Zhang
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Mingai Sun
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yuanyuan Zhao
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Guannan Geng
- Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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10
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Zhu J, Liu X, Yin H, Gao Y, Yu H. Convergent lines of evidence support BIN1 as a risk gene of Alzheimer's disease. Hum Genomics 2021; 15:9. [PMID: 33516273 PMCID: PMC7847034 DOI: 10.1186/s40246-021-00307-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/18/2021] [Indexed: 11/10/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified several susceptibility loci of Alzheimer's disease (AD), which were mainly located in noncoding regions of the genome. Meanwhile, the putative biological mechanisms underlying AD susceptibility loci were still unclear. At present, identifying the functional variants of AD pathogenesis remains a major challenge. Herein, we first used summary data-based Mendelian randomization (SMR) with AD GWAS summary and expression quantitative trait loci (eQTL) data to identify variants who affects expression levels of nearby genes and contributed to the risk of AD. Using the SMR integrative analysis, we totally identified 14 SNPs significantly affected the expression level of 16 nearby genes in blood or brain tissues and contributed to the AD risk. Then, to confirm the results, we replicated the GWAS and eQTL results across multiple samples. Totally, four risk SNP (rs11682128, rs601945, rs3935067, and rs679515) were validated to be associated with AD and affected the expression level of nearby genes (BIN1, HLA-DRA, EPHA1-AS1, and CR1). Besides, our differential expression analysis showed that the BIN1 gene was significantly downregulated in the hippocampus (P = 2.0 × 10-3) and survived after multiple comparisons. These convergent lines of evidence suggest that the BIN1 gene identified by SMR has potential roles in the pathogenesis of AD. Further investigation of the roles of the BIN1 gene in the pathogenesis of AD is warranted.
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Affiliation(s)
- Jin Zhu
- Department of Psychiatry, Jining Medical University, 133 He Hua Road, Jining, 272067 Shandong China
| | - Xia Liu
- Department of Psychiatry, Jining Psychiatric Hospital, Jining, 272051 Shandong China
| | - Hongtao Yin
- Department of Neurology, Zibo Central Hospital, 54 Gongqingtuan Xi Road, Zibo, 255036 China
| | - Yan Gao
- Department of Psychiatry, Jining Medical University, 133 He Hua Road, Jining, 272067 Shandong China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, 133 He Hua Road, Jining, 272067 Shandong China
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11
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Zhan Q, Fu Y, Jiang Q, Liu B, Peng J, Wang Y. SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically. Protein Pept Lett 2020; 27:295-302. [PMID: 31385760 DOI: 10.2174/0929866526666190806143959] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 04/26/2019] [Accepted: 06/14/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. OBJECTIVE In this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically. METHODS Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs. RESULTS We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools. CONCLUSION The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.
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Affiliation(s)
- Qing Zhan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yilei Fu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Bo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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12
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Zhuang H, Zhang Y, Yang S, Cheng L, Liu SL. A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk. Curr Gene Ther 2020; 19:224-231. [PMID: 31553296 DOI: 10.2174/1566523219666190925115535] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/15/2019] [Accepted: 06/16/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations. MATERIALS AND METHODS To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs. RESULTS The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation. CONCLUSION We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.
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Affiliation(s)
- He Zhuang
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Shuo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shu-Lin Liu
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China.,Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Canada.,Department of Infectious Diseases, The First Affiliated Hospital, Harbin Medical University, Harbin, China.,Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China
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13
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Wang Y, Gao L, Lang W, Li H, Cui P, Zhang N, Jiang W. Serum Calcium Levels and Parkinson's Disease: A Mendelian Randomization Study. Front Genet 2020; 11:824. [PMID: 32849817 PMCID: PMC7431982 DOI: 10.3389/fgene.2020.00824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 07/08/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Though increasing epidemiological studies have evaluated the correlation between serum calcium contents and Parkinson's disease (PD), the results are inconsistent. At present, whether there is a causal association between serum calcium content and PD remains undetermined. OBJECTIVE AND METHODS This study was designed to explore the relationship between increased serum calcium contents and PD risk. In this present study, a Mendelian randomization trial was carried out using a large-scale serum calcium genome-wide association study (GWAS) dataset (N = 61,079, Europeans) and a large-scale PD GWAS dataset (N = 8,477, Europeans including 4,238 PD patients and 4,239 controls). Here, a total of four Mendelian randomization methods comprising weighted median, inverse-variance weighted meta-analysis (IVW), MR-Egger, and MR-PRESSO were used. RESULTS Our data concluded that genetically higher serum calcium contents were not significantly related to PD. CONCLUSION In conclusion, we provided genetic evidence that there was no direct causal relationship between serum calcium contents and PD. Hence, calcium supplementation may not result in reduced PD risk.
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Affiliation(s)
- Yanchao Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
- Department of Neurology, Affiliated Hospital of Chifeng University, Chifeng, China
| | - Luyan Gao
- Department of Neurology, The Fourth Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Wenjing Lang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - He Li
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Pan Cui
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Nan Zhang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Wei Jiang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
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14
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Liu H, Guan J, Li H, Bao Z, Wang Q, Luo X, Xue H. Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning. Front Genet 2020; 11:328. [PMID: 32373160 PMCID: PMC7186413 DOI: 10.3389/fgene.2020.00328] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 03/19/2020] [Indexed: 02/02/2023] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease for which it is difficult to find exact disease-related genes. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of multiple sclerosis. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and pay little attention to the global topological information of the whole protein-protein-interaction (PPI) network. Besides, network representation learning (NRL) has attracted a huge amount of attention in the area of network analysis because of its promising performance in node representation and many downstream tasks. In this paper, we try to introduce NRL into the task of disease-related gene prediction and propose a novel framework for identifying the disease-related genes multiple sclerosis. The proposed framework contains three main steps: capturing the topological structure of the PPI network using NRL-based methods, encoding learned features into low-dimensional space using a stacked autoencoder, and training a support vector machine (SVM) classifier to predict disease-related genes. Compared with three state-of-the-art algorithms, our proposed framework shows superior performance on the task of predicting disease-related genes of multiple sclerosis.
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Affiliation(s)
- Haijie Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Physical Medicine and Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China.,Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - He Li
- Department of Automation, College of Information Science and Engineering, Tianjin Tianshi College, Tianjin, China
| | - Zhijie Bao
- School of Textile Science and Engineering, Tiangong University, Tianjin, China
| | - Qingmei Wang
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Xun Luo
- Kerry Rehabilitation Medicine Research Institute, Shenzhen, China.,Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, China
| | - Hansheng Xue
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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15
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Wang J, Su X, Zhao L, Zhang J. Deep Reinforcement Learning for Data Association in Cell Tracking. Front Bioeng Biotechnol 2020; 8:298. [PMID: 32328484 PMCID: PMC7161216 DOI: 10.3389/fbioe.2020.00298] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/20/2020] [Indexed: 01/27/2023] Open
Abstract
Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose a deep reinforcement learning method to associate the detected targets between frames. According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network. The proposed neural network is trained using reinforcement learning to predict a distribution over the association solution. Furthermore, we design a residual convolutional neural network that results in more efficient learning. We validate our method on two applications: the multiple target tracking simulation and the ISBI cell tracking. The results demonstrate that our approach based on reinforcement learning techniques could effectively track targets following different motion patterns and show competitive results.
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Affiliation(s)
- Junjie Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiaohong Su
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Lingling Zhao
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jun Zhang
- Department of Rehabilitation, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
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16
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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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17
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Zhang D, Huo D, Xie H, Wu L, Zhang J, Liu L, Jin Q, Chen X. CHG: A Systematically Integrated Database of Cancer Hallmark Genes. Front Genet 2020; 11:29. [PMID: 32117445 PMCID: PMC7013921 DOI: 10.3389/fgene.2020.00029] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 01/09/2020] [Indexed: 12/20/2022] Open
Abstract
Background The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers. Methods We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected. Results Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data. Conclusions In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.
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Affiliation(s)
- Denan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Diwei Huo
- The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hongbo Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lingxiang Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Juan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lei Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qing Jin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiujie Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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18
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Ao C, Zhang Y, Li D, Zhao Y, Zou Q. Progress in the development of antimicrobial peptide prediction tools. Curr Protein Pept Sci 2020; 22:CPPS-EPUB-103746. [PMID: 31957609 DOI: 10.2174/1389203721666200117163802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/12/2019] [Accepted: 07/15/2019] [Indexed: 11/22/2022]
Abstract
Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.
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Affiliation(s)
- Chunyan Ao
- Institute of Fundamental and Frontier Sciences - University of Electronic Science and Technology of China Chengdu. China
| | - Yu Zhang
- Department of neurosurgery - Heilongjiang Province Land Reclamation Headquarters General Hospital Harbin. China
| | - Dapeng Li
- Department of Internal Medicine-Oncology - The Fourth Hospital in Qinhuangdao Hebei. China
| | - Yuming Zhao
- Information and Computer Engineering College - Northeast Forestry University Harbin. China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences - University of Electronic Science and Technology of China Chengdu. China
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19
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Ru X, Cao P, Li L, Zou Q. Selecting Essential MicroRNAs Using a Novel Voting Method. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:16-23. [PMID: 31479921 PMCID: PMC6727015 DOI: 10.1016/j.omtn.2019.07.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/20/2019] [Accepted: 07/08/2019] [Indexed: 02/06/2023]
Abstract
Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs.
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Affiliation(s)
- Xiaoqing Ru
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Peigang Cao
- Department of Cardiology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lihong Li
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
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20
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Abstract
BACKGROUND With the development of e-Health, it plays a more and more important role in predicting whether a doctor's answer can be accepted by a patient through online healthcare community. Unlike the previous work which focus mainly on the numerical feature, in our framework, we combine both numerical and textual information to predict the acceptance of answers. The textual information is composed of questions posted by the patients and answers posted by the doctors. To extract the textual features from them, we first trained a sentence encoder to encode a pair of question and answer into a co-dependent representation on a held-out dataset. After that,we can use it to predict the acceptance of answers by doctors. RESULTS Our experimental results on the real-world dataset demonstrate that by applying our model additional features from text can be extracted and the prediction can be more accurate. That's to say, the model which take both textual features and numerical features as input performs significantly better than model which takes numerical features only on all the four metrics (Accuracy, AUC, F1-score and Recall). CONCLUSIONS This work proposes a generic framework combining numerical features and textual features for acceptance prediction, where textual features are extracted from text based on deep learning methods firstly and can be used to achieve a better prediction results.
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Affiliation(s)
- Qianlong Liu
- School of Data Science, Fudan University, Handan Road, Shanghai, China
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Kangenbei Liao
- School of Data Science, Fudan University, Handan Road, Shanghai, China
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Kelvin Kam-fai Tsoi
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Zhongyu Wei
- School of Data Science, Fudan University, Handan Road, Shanghai, China
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21
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Hou H, Gan T, Yang Y, Zhu X, Liu S, Guo W, Hao J. Using deep reinforcement learning to speed up collective cell migration. BMC Bioinformatics 2019; 20:571. [PMID: 31760946 PMCID: PMC6876083 DOI: 10.1186/s12859-019-3126-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration. RESULTS Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems. CONCLUSION Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.
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Affiliation(s)
- Hanxu Hou
- School of Electrical Engineering & Intelligentization, Dongguan University of Technology, No.1 University Road, DongGuan, 523808 China
| | - Tian Gan
- College of Intelligence and Computing, TianJin University, No.135 Yaguan Road, TianJin, 300350 China
| | - Yaodong Yang
- College of Intelligence and Computing, TianJin University, No.135 Yaguan Road, TianJin, 300350 China
| | - Xianglei Zhu
- Automotive Data Center, CATARC, No.69 Xianfeng Road, TianJin, 300300 China
| | - Sen Liu
- Automotive Data Center, CATARC, No.69 Xianfeng Road, TianJin, 300300 China
| | - Weiming Guo
- Automotive Data Center, CATARC, No.69 Xianfeng Road, TianJin, 300300 China
| | - Jianye Hao
- School of Electrical Engineering & Intelligentization, Dongguan University of Technology, No.1 University Road, DongGuan, 523808 China
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22
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Abstract
Background Alzheimer’s disease (AD) imposes a heavy burden on society and every family. Therefore, diagnosing AD in advance and discovering new drug targets are crucial, while these could be achieved by identifying AD-related proteins. The time-consuming and money-costing biological experiment makes researchers turn to develop more advanced algorithms to identify AD-related proteins. Results Firstly, we proposed a hypothesis “similar diseases share similar related proteins”. Therefore, five similarity calculation methods are introduced to find out others diseases which are similar to AD. Then, these diseases’ related proteins could be obtained by public data set. Finally, these proteins are features of each disease and could be used to map their similarity to AD. We developed a novel method ‘LRRGD’ which combines Logistic Regression (LR) and Gradient Descent (GD) and borrows the idea of Random Forest (RF). LR is introduced to regress features to similarities. Borrowing the idea of RF, hundreds of LR models have been built by randomly selecting 40 features (proteins) each time. Here, GD is introduced to find out the optimal result. To avoid the drawback of local optimal solution, a good initial value is selected by some known AD-related proteins. Finally, 376 proteins are found to be related to AD. Conclusion Three hundred eight of three hundred seventy-six proteins are the novel proteins. Three case studies are done to prove our method’s effectiveness. These 308 proteins could give researchers a basis to do biological experiments to help treatment and diagnostic AD.
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Affiliation(s)
- Tianyi Zhao
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, China.
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23
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Zhu X, Fu B, Yang Y, Ma Y, Hao J, Chen S, Liu S, Li T, Liu S, Guo W, Liao Z. Attention-based recurrent neural network for influenza epidemic prediction. BMC Bioinformatics 2019; 20:575. [PMID: 31760945 PMCID: PMC6876090 DOI: 10.1186/s12859-019-3131-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public. RESULTS In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting. CONCLUSION We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.
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Affiliation(s)
- Xianglei Zhu
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Bofeng Fu
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Yaodong Yang
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Yu Ma
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Jianye Hao
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Siqi Chen
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Shuang Liu
- College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350 China
| | - Tiegang Li
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Sen Liu
- Automotive Data Center, China Automotive Technology & Research, Tianjin, 300300 China
| | - Weiming Guo
- Automotive Data Center, China Automotive Technology & Research, Tianjin, 300300 China
| | - Zhenyu Liao
- Pony Testing International Group, Tianjin, 300051 China
- Tianjin FoodSafety Inspection Technology Institute, Tianjin, 300300 China
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24
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Zhan Q, Wang N, Jin S, Tan R, Jiang Q, Wang Y. ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function. BMC Bioinformatics 2019; 20:573. [PMID: 31760933 PMCID: PMC6876095 DOI: 10.1186/s12859-019-3132-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment's accuracy, however, was ignored by these researches. RESULTS A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM's parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods. CONCLUSIONS We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment's accuracy.
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Affiliation(s)
- Qing Zhan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Nan Wang
- Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China
| | - Shuilin Jin
- Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China
| | - Renjie Tan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
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25
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Abstract
Protein methylation is an important and reversible post-translational modification
that regulates many biological processes in cells. It occurs mainly on lysine and arginine
residues and involves many important biological processes, including transcriptional
activity, signal transduction, and the regulation of gene expression. Protein methylation
and its regulatory enzymes are related to a variety of human diseases, so improved identification
of methylation sites is useful for designing drugs for a variety of related diseases.
In this review, we systematically summarize and analyze the tools used for the prediction
of protein methylation sites on arginine and lysine residues over the last decade.
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Affiliation(s)
- Chunyan Ao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Yuan Lin
- Department of System Integration, Sparebanken Vest, Bergen, Norway
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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26
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Zhao T, Hu Y, Zang T, Wang Y. Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes. Front Genet 2019; 10:1021. [PMID: 31708967 PMCID: PMC6824203 DOI: 10.3389/fgene.2019.01021] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 09/24/2019] [Indexed: 12/19/2022] Open
Abstract
It is estimated that the impact of related genes on the risk of Alzheimer's disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.
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Affiliation(s)
- Tianyi Zhao
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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27
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Wang Y, Nie C, Zang T, Wang Y. Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity. Front Genet 2019; 10:832. [PMID: 31572444 PMCID: PMC6751509 DOI: 10.3389/fgene.2019.00832] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 08/13/2019] [Indexed: 12/19/2022] Open
Abstract
Circular RNAs (circRNAs) are a novel class of endogenous noncoding RNAs that have well-conserved sequences. Emerging evidence has shown that circRNAs can be novel biomarkers or therapeutic targets for many diseases and play an important role in the development of various pathological conditions. Therefore, identifying potential disease-related circRNAs is helpful in improving the efficiency of finding therapeutic targets for diseases. Here, we propose a computational model (PreCDA) to predict potential circRNA-disease associations. First, we calculated the circRNA expression similarity based on circRNA expression profiles. The circRNA functional similarity is calculated based on cosine similarity, and the disease similarity is used as the dimension of each circRNA vector. The associations between circRNAs and diseases are defined based on the circRNA functional similarity and expression similarity. We constructed a disease-related circRNA association network and used a graph-based recommendation algorithm (PersonalRank) to sort candidate disease-related circRNAs. As a result, PreCDA has an average area under the receiver operating characteristic curve value of 78.15% in predicting candidate disease-related circRNAs. In addition, we discuss the factors that affect the performance of this method and find some unknown circRNAs related to diseases, with several common diseases used as case studies. These results show that PreCDA has good performance in predicting potential circRNA-disease associations and is helpful for the diagnosis and treatment of human diseases.
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Affiliation(s)
| | | | - Tianyi Zang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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28
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Peng J, Wang X, Shang X. Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data. BMC Bioinformatics 2019; 20:284. [PMID: 31182005 PMCID: PMC6557741 DOI: 10.1186/s12859-019-2769-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods. Results In this article, we try to re-construct a neural network based on Gene Ontology (GO) for dimension reduction of scRNA-seq data. By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively. Conclusions The evaluation results show that the proposed models outperform some state-of-the-art dimensionality reduction approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China.,Centre for Multidisciplinary Convergence Computing, School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaoyu Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China. .,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China.
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29
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Peng J, Guan J, Shang X. Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder. Front Genet 2019; 10:226. [PMID: 31001311 PMCID: PMC6454041 DOI: 10.3389/fgene.2019.00226] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/28/2019] [Indexed: 12/26/2022] Open
Abstract
Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.
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Affiliation(s)
| | | | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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