1
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Zhao Z, Yang W, Zhai Y, Liang Y, Zhao Y. Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm. Front Genet 2022; 12:821996. [PMID: 35154264 PMCID: PMC8837382 DOI: 10.3389/fgene.2021.821996] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 12/13/2022] Open
Abstract
The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.
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Affiliation(s)
- Ziye Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Yixiao Zhai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yingjian Liang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Yingjian Liang, ; Yuming Zhao,
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Yingjian Liang, ; Yuming Zhao,
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2
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Xue F, Gao L, Chen T, Chen H, Zhang H, Wang T, Han Z, Gao S, Wang L, Hu Y, Tang J, Huang L, Liu G, Zhang Y. Parkinson's Disease rs117896735 Variant Regulates INPP5F Expression in Brain Tissues and Increases Risk of Alzheimer's Disease. J Alzheimers Dis 2022; 89:67-77. [PMID: 35848021 DOI: 10.3233/jad-220086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Both INPP5D and INPP5F are members of INPP5 family. INPP5F rs117896735 variant was associated with Parkinson's disease (PD) risk, and INPP5D was an Alzheimer's disease (AD) risk gene. However, it remains unclear about the roles of INPP5F rs117896735 variant in AD. OBJECTIVE We aim to investigate the roles of rs117896735 in AD. METHODS First, we conducted a candidate variant study to evaluate the association of rs117896735 variant with AD risk using the large-scale AD GWAS dataset. Second, we conducted a gene expression analysis of INPP5F to investigate the expression difference of INPP5F in different human tissues using two large-scale gene expression datasets. Third, we conducted an expression quantitative trait loci analysis to evaluate whether rs117896735 variant regulate the expression of INPP5F. Fourth, we explore the potentially differential expression of INPP5F in AD and control using multiple AD-control gene expression datasets in human brain tissues and whole blood. RESULTS We found that 1) rs117896735 A allele was associated with the increased risk of AD with OR = 1.15, 95% CI 1.005-1.315, p = 0.042; 2) rs117896735 A allele could increase INPP5F expression in multiple human tissues; 3) INPP5F showed different expression in different human tissues, especially in brain tissues; 4) INPP5F showed significant expression dysregulation in AD compared with controls in human brain tissues. CONCLUSION Conclusion: We demonstrate that PD rs117896735 variant could regulate INPP5F expression in brain tissues and increase the risk of AD. These finding may provide important information about the role of rs117896735 in AD.
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Affiliation(s)
- Feng Xue
- Department of Neurosurgery, Tianjin Hospital of ITCWM Nan Kai Hospital, Tianjin, China
| | - Luyan Gao
- Department of Neurology, Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, The Fourth Central Clinical College of Tianjin Medical University, Tianjin, China
| | - TingTing Chen
- Department of Oncology, Tianjin Hospital of ITCWM Nan Kai Hospital, Tianjin, China
| | - Hongyuan Chen
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Haihua Zhang
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Tao Wang
- Chinese Institute for Brain Research, Beijing, China
| | - Zhifa Han
- School of Medicine, School of Pharmaceutical Sciences, THU-PKU Center for Life Sciences, Tsinghua University, Beijing, China
| | - Shan Gao
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Longcai Wang
- Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiangwei Tang
- Department of Neurology, Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, The Fourth Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Lei Huang
- Department of Neurology, Tianjin Fourth Central Hospital, The Fourth Central Hospital Affiliated to Nankai University, The Fourth Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Beijing Key Laboratory of Hypoxia Translational Medicine, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan Zhang
- Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, China
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3
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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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4
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Guo Y, Cheng H, Yuan Z, Liang Z, Wang Y, Du D. Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies. Front Genet 2021; 12:801261. [PMID: 34956337 PMCID: PMC8693929 DOI: 10.3389/fgene.2021.801261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.
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Affiliation(s)
- Yingjie Guo
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Honghong Cheng
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Zhian Yuan
- Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Zhen Liang
- School of Life Science, Shanxi University, Taiyuan, China
| | - Yang Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Debing Du
- Beidahuang Industry Group General Hospital, Harbin, China
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5
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Guo Y, Wu C, Yuan Z, Wang Y, Liang Z, Wang Y, Zhang Y, Xu L. Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies. Front Cell Dev Biol 2021; 9:801113. [PMID: 34977040 PMCID: PMC8716787 DOI: 10.3389/fcell.2021.801113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 11/23/2021] [Indexed: 11/30/2022] Open
Abstract
Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.
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Affiliation(s)
- Yingjie Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Chenxi Wu
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, United States
| | - Zhian Yuan
- Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Yansu Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Zhen Liang
- School of Life Science, Shanxi University, Taiyuan, China
| | - Yang Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Yi Zhang
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Yi Zhang, ; Lei Xu,
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
- *Correspondence: Yi Zhang, ; Lei Xu,
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6
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Lin X. Genomic Variation Prediction: A Summary From Different Views. Front Cell Dev Biol 2021; 9:795883. [PMID: 34901036 PMCID: PMC8656232 DOI: 10.3389/fcell.2021.795883] [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: 10/15/2021] [Accepted: 11/11/2021] [Indexed: 12/02/2022] Open
Abstract
Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.
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Affiliation(s)
- Xiuchun Lin
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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7
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Zhao YW, Zhang S, Ding H. Recent development of machine learning methods in sumoylation sites prediction. Curr Med Chem 2021; 29:894-907. [PMID: 34525906 DOI: 10.2174/0929867328666210915112030] [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: 03/10/2021] [Revised: 07/24/2021] [Accepted: 08/07/2021] [Indexed: 11/22/2022]
Abstract
Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.
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Affiliation(s)
- Yi-Wei Zhao
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065. China
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
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8
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Zou Y, Wu H, Guo X, Peng L, Ding Y, Tang J, Guo F. MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200607173829] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive.
Objective:
In recent years, the rise of computational biology methods based on Machine Learning
(ML) has greatly improved the detection efficiency of DBPs.
Method:
In this study, the Multiple Kernel-based Fuzzy SVM Model with Support Vector Data
Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted
from the protein sequence. Secondly, multiple kernels are constructed via these sequence features.
Then, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel
Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with
Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs.
Results:
Our model is evaluated on several benchmark datasets. Compared with other methods, MKFSVM-
SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and
PDB2272 (0.5476).
Conclusion:
We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the
classifier for DNA-binding proteins identification.
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Affiliation(s)
- Yi Zou
- School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, No. 1 Kerui Road, 215009, Suzhou, China
| | - Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Li Peng
- School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, No. 1 Kerui Road, 215009, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
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9
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Guo X, Zhou W, Shi B, Wang X, Du A, Ding Y, Tang J, Guo F. An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200614172536] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Dry Weight (DW) is the lowest weight after dialysis, and patients with
lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches
have been presented to assess the dry weight of hemodialysis patients. However, these traditional
methods all depend on special instruments and professional technicians.
Objective:
In order to avoid this limitation, we need to find a machine-independent way to assess dry
weight, therefore we collected some clinical influencing characteristic data and constructed a
Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.
Methods::
In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements,
and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body
Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were
closely related to their dry weight. All these relevant data were used to enter the regression equation.
Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS)
model was proposed to predict the dry weight of hemodialysis patients.
Result:
The experimental results show that dry weight is positively correlated with BMI and HR.
And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively
correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was
1.3817.
Conclusion:
Our proposed model could serve as a viable alternative for dry weight estimation of
hemodialysis patients, thus providing a new way for clinical practice. Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients,
thus providing a new way for the clinic.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Bin Shi
- Hemodialysis Center, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Xiaohua Wang
- Department of Urology, the First Affiliated Hospital of Soochow University, 215006, Suzhou, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
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10
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Lv Y, Huang S, Zhang T, Gao B. Application of Multilayer Network Models in Bioinformatics. Front Genet 2021; 12:664860. [PMID: 33868392 PMCID: PMC8044439 DOI: 10.3389/fgene.2021.664860] [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: 02/06/2021] [Accepted: 02/26/2021] [Indexed: 11/24/2022] Open
Abstract
Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.
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Affiliation(s)
- Yuanyuan Lv
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
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11
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Wang X, Yang Y, Liu J, Wang G. The stacking strategy-based hybrid framework for identifying non-coding RNAs. Brief Bioinform 2021; 22:6165004. [PMID: 33693454 DOI: 10.1093/bib/bbab023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/16/2021] [Indexed: 12/12/2022] Open
Abstract
With the development of next-generation sequencing technology, a large number of transcripts need to be analyzed, and it has been a challenge to distinguish non-coding ribonucleic acid (RNAs) (ncRNAs) from coding RNAs. And for non-model organisms, due to the lack of transcriptional data, many existing methods cannot identify them. Therefore, in addition to using deoxyribonucleic acid-based and RNA-based features, we also proposed a hybrid framework based on the stacking strategy to identify ncRNAs, and we innovatively added eight features based on predicted peptides. The proposed framework was based on stacking two-layer classifier which combined random forest (RF), LightGBM, XGBoost and logistic regression (LR) models. We used this framework to build two types of models. For cross-species ncRNAs identification model, we tested it on six different species: human, mouse, zebrafish, fruit fly, worm and Arabidopsis. Compared with other tools, our model was the best in datasets of Arabidopsis, worm and zebrafish with the accuracy of 98.36%, 99.65% and 94.12%. For performance metrics analysis, the datasets of the six species were considered as a whole set, and the sensitivity, accuracy, precision and F1 values of our model were the best. For the plant-specific ncRNAs identification model, the average values of the six metrics of the two experiments were all greater than 95%, which demonstrated it can be used to identify ncRNAs in plants. The above indicates that the hybrid framework we designed is universal between animals and plants and has significant advantages in the identification of cross-species ncRNAs.
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Affiliation(s)
- Xin Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Yang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jian Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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12
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Abstract
The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.
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13
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Zhang S, Zhang C, Du J, Zhang R, Yang S, Li B, Wang P, Deng W. Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures. Front Cell Dev Biol 2021; 9:605977. [PMID: 33644044 PMCID: PMC7905047 DOI: 10.3389/fcell.2021.605977] [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: 09/14/2020] [Accepted: 01/07/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.
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Affiliation(s)
- Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
| | - Cheng Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
| | - Jinke Du
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Rui Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shixiong Yang
- Central Laboratory, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, China
| | - Bo Li
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Wensheng Deng
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, China
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14
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Recent Advances in Predicting Protein S-Nitrosylation Sites. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5542224. [PMID: 33628788 PMCID: PMC7892234 DOI: 10.1155/2021/5542224] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 01/09/2023]
Abstract
Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.
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15
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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L 2,1-Norm. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6627650. [PMID: 33628794 PMCID: PMC7880720 DOI: 10.1155/2021/6627650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 11/28/2022]
Abstract
Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient's dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.
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16
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Hu Y, Sun JY, Zhang Y, Zhang H, Gao S, Wang T, Han Z, Wang L, Sun BL, Liu G. rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues. BMC Med 2021; 19:11. [PMID: 33461566 PMCID: PMC7814705 DOI: 10.1186/s12916-020-01883-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 12/08/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND It has been well established that the TMEM106B gene rs1990622 variant was a frontotemporal dementia (FTD) risk factor. Until recently, growing evidence highlights the role of TMEM106B in Alzheimer's disease (AD). However, it remains largely unclear about the role of rs1990622 variant in AD. METHODS Here, we conducted comprehensive analyses including genetic association study, gene expression analysis, eQTLs analysis, and colocalization analysis. In stage 1, we conducted a genetic association analysis of rs1990622 using large-scale genome-wide association study (GWAS) datasets from International Genomics of Alzheimer's Project (21,982 AD and 41,944 cognitively normal controls) and UK Biobank (314,278 participants). In stage 2, we performed a gene expression analysis of TMEM106B in 49 different human tissues using the gene expression data in GTEx. In stage 3, we performed an expression quantitative trait loci (eQTLs) analysis using multiple datasets from UKBEC, GTEx, and Mayo RNAseq Study. In stage 4, we performed a colocalization analysis to provide evidence of the AD GWAS and eQTLs pair influencing both AD and the TMEM106B expression at a particular region. RESULTS We found (1) rs1990622 variant T allele contributed to AD risk. A sex-specific analysis in UK Biobank further indicated that rs1990622 T allele only contributed to increased AD risk in females, but not in males; (2) TMEM106B showed different expression in different human brain tissues especially high expression in cerebellum; (3) rs1990622 variant could regulate the expression of TMEM106B in human brain tissues, which vary considerably in different disease statuses, the mean ages at death, the percents of females, and the different descents of the selected donors; (4) colocalization analysis provided suggestive evidence that the same variant contributed to AD risk and TMEM106B expression in cerebellum. CONCLUSION Our comprehensive analyses highlighted the role of FTD rs1990622 variant in AD risk. This cross-disease approach may delineate disease-specific and common features, which will be important for both diagnostic and therapeutic development purposes. Meanwhile, these findings highlight the importance to better understand TMEM106B function and dysfunction in the context of normal aging and neurodegenerative diseases.
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Affiliation(s)
- Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150080, China
| | - Jing-Yi Sun
- Shandong Provincial Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250021, China
| | - Yan Zhang
- Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Haihua Zhang
- Beijing Institute for Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Shan Gao
- Beijing Institute for Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Tao Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
| | - Zhifa Han
- School of Medicine, School of Pharmaceutical Sciences, THU-PKU Center for Life Sciences, Tsinghua University, Beijing, China.,State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.,Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Longcai Wang
- Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China
| | - Bao-Liang Sun
- Key Laboratory of Cerebral Microcirculation in Universities of Shandong; Department of Neurology, Second Affiliated Hospital; Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Guiyou Liu
- Beijing Institute for Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China. .,Chinese Institute for Brain Research, Beijing, China. .,Key Laboratory of Cerebral Microcirculation in Universities of Shandong; Department of Neurology, Second Affiliated Hospital; Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China. .,National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China. .,Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
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17
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Hu Y, Zhang H, Liu B, Gao S, Wang T, Han Z, Ji X, Liu G. rs34331204 regulates TSPAN13 expression and contributes to Alzheimer's disease with sex differences. Brain 2020; 143:e95. [PMID: 33175954 PMCID: PMC7719023 DOI: 10.1093/brain/awaa302] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Haihua Zhang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Bian Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Shan Gao
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China
| | - Tao Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Zhifa Han
- School of Medicine, School of Pharmaceutical Sciences, THU-PKU Center for Life Sciences, Tsinghua University, Beijing, China
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
- Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Xunming Ji
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Guiyou Liu
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, 100069, China
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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18
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rs1769793 variant reduces EGLN1 expression in skeletal muscle and hippocampus and contributes to high aerobic capacity in hypoxia. Proc Natl Acad Sci U S A 2020; 117:29283-29285. [PMID: 33109725 DOI: 10.1073/pnas.2010073117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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19
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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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20
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Örd T, Puurand T, Örd D, Annilo T, Möls M, Remm M, Örd T. A human-specific VNTR in the TRIB3 promoter causes gene expression variation between individuals. PLoS Genet 2020; 16:e1008981. [PMID: 32745133 PMCID: PMC7425993 DOI: 10.1371/journal.pgen.1008981] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 08/13/2020] [Accepted: 07/06/2020] [Indexed: 02/07/2023] Open
Abstract
Tribbles homolog 3 (TRIB3) is pseudokinase involved in intracellular regulatory processes and has been implicated in several diseases. In this article, we report that human TRIB3 promoter contains a 33-bp variable number tandem repeat (VNTR) and characterize the heterogeneity and function of this genetic element. Analysis of human populations around the world uncovered the existence of alleles ranging from 1 to 5 copies of the repeat, with 2-, 3- and 5-copy alleles being the most common but displaying considerable geographical differences in frequency. The repeated sequence overlaps a C/EBP-ATF transcriptional regulatory element and is highly conserved, but not repeated, in various mammalian species, including great apes. The repeat is however evident in Neanderthal and Denisovan genomes. Reporter plasmid experiments in human cell culture reveal that an increased copy number of the TRIB3 promoter 33-bp repeat results in increased transcriptional activity. In line with this, analysis of whole genome sequencing and RNA-Seq data from human cohorts demonstrates that the copy number of TRIB3 promoter 33-bp repeats is positively correlated with TRIB3 mRNA expression level in many tissues throughout the body. Moreover, the copy number of the TRIB3 33-bp repeat appears to be linked to known TRIB3 eQTL SNPs as well as TRIB3 SNPs reported in genetic association studies. Taken together, the results indicate that the promoter 33-bp VNTR constitutes a causal variant for TRIB3 expression variation between individuals and could underlie the results of SNP-based genetic studies.
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Affiliation(s)
- Tiit Örd
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tarmo Puurand
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Daima Örd
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tarmo Annilo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Märt Möls
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Maido Remm
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Tõnis Örd
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
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21
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Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features. Genomics 2020; 112:4342-4347. [PMID: 32721444 DOI: 10.1016/j.ygeno.2020.07.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 07/18/2020] [Accepted: 07/22/2020] [Indexed: 12/14/2022]
Abstract
N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.
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22
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He F, Sun B, Li L, Liu M, Lin W, Liu L, Sun Y, Luo Y, Wu L, Lu L, Zhang W, Zhou Z. TRIB3 rs6037475 is a potential biomarker for predicting felodipine drug response in Chinese patients with hypertension. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:437. [PMID: 32395481 PMCID: PMC7210142 DOI: 10.21037/atm.2020.03.176] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Background Our previous studies have found that single nucleotide polymorphisms (SNPs) of tribbles homolog 3 (TRIB3) are related to the hypotensive effects of calcium-channel blockers (CCBs) and angiotensin-converting enzyme (ACE) inhibitors. In this study, we aimed at exploring and validating the effect of TRIB3 polymorphism on antihypertensive drugs responses. Methods A total of 830 hypertensive patients, who were administered with open-labeled hydrochlorothiazide (12.5 mg once daily) and randomly assigned to off-labeled felodipine (5 mg) or a matched placebo combination treatment (1:1), were selected from the Felodipine Event Reduction (FEVER) study. A strategy of screening 259 samples and validating the remaining 531 samples was implemented. Four functional SNPs were selected (rs2295490, rs11470129, rs4815567 and rs6037475 in TRIB3). A mixed linear model was performed to analyze the effects of TRIB3 SNPs on antihypertensive drugs responses. Results We found that TRIB3 rs6037475 CC genotype was associated with a reduction of diastolic blood pressure (DBP) (P=6.3×10−3) in the felodipine treatment group of screening set, and was also associated with a reduction of systolic blood pressure (SBP) (P=0.021), DBP (P=6.0×10−3) and mean arterial pressure (MAP) (P=0.021) in the felodipine treatment group of the validation set. As for the reductions influenced by the rs2295490, rs11470129 and rs4815567 genetic variations, however, the adjusted P-value did not reach statistical significance. Combined screening and validation set analysis found that patients with TRIB3 rs6037475 CC genotype had a significant higher mean SBP, DBP and MAP than those with TT genotype in the felodipine treatment group (CC vs. TT −10.2±0.74 vs. −17.8±0.21, P=7.8×10−3; −4.6±0.50 vs. −10.2±0.23, P=3.0×10−4; −6.5±0.54 vs. −12.7±0.14, P=3.0×10−4, respectively). Conclusions These results suggest that TRIB3 rs6037475 genetic variation can be useful as a bio-marker for predicting felodipine drug response in Chinese patients with hypertension.
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Affiliation(s)
- Fazhong He
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Bao Sun
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410078, China
| | - Ling Li
- Department of Pharmacy, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China
| | - Mouze Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410078, China
| | - Weijie Lin
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Lin Liu
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Yinxiang Sun
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Yuhong Luo
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Lin Wu
- Department II of Thoracic Medicine, Hunan Cancer Hospital, Changsha 519000, China
| | - Ligong Lu
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410078, China
| | - Zhiling Zhou
- Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China
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23
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Dao FY, Lv H, Yang YH, Zulfiqar H, Gao H, Lin H. Computational identification of N6-methyladenosine sites in multiple tissues of mammals. Comput Struct Biotechnol J 2020; 18:1084-1091. [PMID: 32435427 PMCID: PMC7229270 DOI: 10.1016/j.csbj.2020.04.015] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.
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Affiliation(s)
| | | | - Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Gao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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24
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Dong H, Zhou W, Wang P, Zuo E, Ying X, Chai S, Fei T, Jin L, Chen C, Ma G, Liu H. Comprehensive Analysis of the Genetic and Epigenetic Mechanisms of Osteoporosis and Bone Mineral Density. Front Cell Dev Biol 2020; 8:194. [PMID: 32269995 PMCID: PMC7109267 DOI: 10.3389/fcell.2020.00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 01/11/2023] Open
Abstract
Osteoporosis is a skeletal disorder characterized by a systemic impairment of bone mineral density (BMD). Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for osteoporosis and BMD. However, the vast majority of susceptibility loci are located in non-coding regions of the genome and provide limited information about the genetic mechanisms of osteoporosis. Herein we performed a comprehensive functional analysis to investigate the genetic and epigenetic mechanisms of osteoporosis and BMD. BMD and osteoporosis are found to share many common susceptibility loci, and the corresponding susceptibility genes are significantly enriched in bone-related biological pathways. The regulatory element enrichment analysis indicated that BMD and osteoporosis susceptibility loci are significantly enriched in 5′UTR and DNase I hypersensitive sites (DHSs) of peripheral blood immune cells. By integrating GWAS and expression Quantitative Trait Locus (eQTL) data, we found that 15 protein-coding genes are regulated by the osteoporosis and BMD susceptibility loci. Our analysis provides new clues for a better understanding of the pathogenic mechanisms and offers potential therapeutic targets for osteoporosis.
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Affiliation(s)
- Hui Dong
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China.,Department of Stomatology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Enjun Zuo
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiaoxia Ying
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Songling Chai
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Tao Fei
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Laidi Jin
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Chen Chen
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Guowu Ma
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Huiying Liu
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
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[Tribbles pseudokinase 3 inhibits the adipogenic differentiation of human adipose-derived mesenchymal stem cells]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2020; 52. [PMID: 32071456 PMCID: PMC7439058 DOI: 10.19723/j.issn.1671-167x.2020.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To identify the role of Tribbles pseudokinase 3 (TRIB3) during the process of adipogenic differentiation of human adipose-derived mesenchymal stem cells (hASCs), and to provide a new target and a novel idea for the application of hASCs in adipose tissue engineering and soft tissue regeneration. METHODS TRIB3-knockdown hASCs (shTRIB3) and TRIB3-overexpression hASCs (TRIB3-over) were established using lentivirus transfection technique. The transfection effect was estimated by the visible presence of green fluorescence as the expression of green fluorescent protein (GFP) in the transfected hASCs. The lentiviral transfection efficiency was examined by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. After adipogenic induction, Oil Red staining and quantification, as well as qRT-PCR about several specific adipogenic markers were used to evaluate the adipogenic differentiation ability of hASCs. RESULTS In TRIB3-knockdown hASCs, the TRIB3 mRNA expression level decreased by about 84.3% compared with the control group (P<0.01), and the TRIB3 protein level also showed obvious reduction. Oppositely, in TRIB3-overexpression hASCs, the TRIB3 mRNA expression level increased by approximately 160% compared with the control group (P<0.01), and the TRIB3 protein level also showed a significant increase. These results indicated a successful construction of TRIB3-knockdown hASCs and TRIB3-overexpression hASCs. The Oil Red staining results showed that the down-regulation of TRIB3 significantly promoted lipid droplets formation in hASCs, consistent with Oil Red quantification. On the other hand, the up-regulation of TRIB3 suppressed lipid droplets formation in hASCs, consistent with Oil Red quantification. After adipogenic induction, adipogenesis-related genes, including peroxisome proliferator-activated receptor γ (PPARγ), cluster of differentiation 36 (CD36) and CCAAT/enhancer binding protein α (C/EBPα), were increased significantly in TRIB3-knockdown hASCs compared with the control group (P<0.01). Oppositely, PPARγ, CD36 and lipoprotein lipase (LPL) were significantly decreased in TRIB3-overexpression hASCs compared with the control group (P<0.01). CONCLUSION TRIB3 inhibited the adipogenic differentiation of hASCs. Knockdown of TRIB3 promoted the ability of adipogenesis of hASCs, while overexpression of TRIB3 inhibited the adipogenic differentiation of hASCs. Considering the important role of PPARγ in the adipogenis process, the molecular mechanism of the regulatory function of TRIB3 may be related with PPARγ signal pathway.
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Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020; 9:E326. [PMID: 32019269 PMCID: PMC7072524 DOI: 10.3390/cells9020326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China;
| | - Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
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Zhang H, Wang T, Han Z, Wang L, Zhang Y, Wang L, Liu G. Impact of Vitamin D Binding Protein Levels on Alzheimer's Disease: A Mendelian Randomization Study. J Alzheimers Dis 2020; 74:991-998. [PMID: 32116251 DOI: 10.3233/jad-191051] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Until now, observational studies, randomized controlled trials (RCTs), and Mendelian randomization (MR) studies have explored the impact of vitamin D on Alzheimer's disease (AD), and reported inconsistent findings. In MR studies, the sensitivity analysis by removing GC rs2282679 variant highlighted no association of 25OHD levels with AD risk, which indicates that vitamin D-binding protein (DBP) encoded by GC may have distinct effects on AD risk. Here, we aim to clarify this assumption. We selected the GC rs2282679 variant associated with DBP levels (p = 3.30E-76) as the instrumental variable, and extracted the summary statistics of rs2282679 variant in multiple AD GWAS datasets from IGAP, Complex Trait Genetics (CTG) lab, and UK Biobank. We then performed a MR study to investigate the causal association between DBP levels and AD. In IGAP, MR analysis showed that the genetically DBP levels (per 1 standard deviation (SD) increase 50 mg/L) were significantly associated with reduced AD risk (OR = 0.63, 95% CI: 0.45-0.89, p = 0.009). Importantly, the estimates from two sensitivity analyses were consistent with the main estimate in terms of direction and magnitude. Meanwhile, we found no causal association between DBP levels and other four AD phenotypes in CTG lab and UK Biobank. In summary, we highlight the role of DBP levels in AD risk, and provide strong support evidence that DBP may be the therapeutic agent for the treatment of AD. Meanwhile, our findings clarify the assumption that DBP may drive the observed relationship between 25OHD levels and AD.
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Affiliation(s)
- Haihua Zhang
- National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Tao Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Zhifa Han
- School of Medicine, School of Pharmaceutical Sciences, THU-PKU Center for Life Sciences, Tsinghua University, Beijing, China
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
- Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Longcai Wang
- Department of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Yan Zhang
- Department of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Lijun Wang
- Department of Encephalopathy, Shenzhen Hospital of Guangzhou University of Chinese Medicine (Futian), Shenzhen, China
| | - Guiyou Liu
- National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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28
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Taxonomy dimension reduction for colorectal cancer prediction. Comput Biol Chem 2019; 83:107160. [DOI: 10.1016/j.compbiolchem.2019.107160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/02/2019] [Accepted: 11/04/2019] [Indexed: 02/01/2023]
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Zhao T, Wang D, Hu Y, Zhang N, Zang T, Wang Y. Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering. Curr Gene Ther 2019; 19:216-223. [DOI: 10.2174/1566523219666190924113737] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/05/2019] [Accepted: 06/12/2019] [Indexed: 01/14/2023]
Abstract
Background:
More and more scholars are trying to use it as a specific biomarker for Alzheimer’s
Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that
miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early
events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of
AD, and may also be involved in the disease through some specific molecular mechanisms.
Objective:
Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early
diagnosis.
Materials and Methods:
We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein
interaction network is used to find more AD-related genes by known AD-related genes. Then,
each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each
miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not
generate negative samples randomly with using classification method to identify AD-related miRNAs.
Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers
and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers).
Results and Conclusion:
We identified 257 novel AD-related miRNAs and compare our method with
SVM which is applied by generating negative samples. The AUC of our method is much higher than
SVM and we did case studies to prove that our results are reliable.
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Affiliation(s)
- Tianyi Zhao
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Donghua Wang
- Department of General Surgery, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yang Hu
- School of life Science and Tenchnology, Harbin Institute of Technology, Harbin, China
| | - Ningyi Zhang
- Department of Computer 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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Liu G, Zhang H, Liu B, Ji X. Rs2293871 regulates HTRA1 expression and affects cerebral small vessel stroke and Alzheimer's disease. Brain 2019; 142:e61. [PMID: 31603204 PMCID: PMC6821345 DOI: 10.1093/brain/awz305] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Guiyou Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Haihua Zhang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Bian Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xunming Ji
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
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Yang Y, Wang X, Ju W, Sun L, Zhang H. Genetic and Expression Analysis of COPI Genes and Alzheimer's Disease Susceptibility. Front Genet 2019; 10:866. [PMID: 31608112 PMCID: PMC6761859 DOI: 10.3389/fgene.2019.00866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 08/19/2019] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly and the leading cause of dementia in humans. Evidence shows that cellular trafficking and recycling machineries are associated with AD risk. A recent study found that the coat protein complex I (COPI)-dependent trafficking in vivo could significantly reduce amyloid plaques in the cortex and hippocampus of neurological in the AD mouse models and identified 12 single-nucleotide polymorphisms in COPI genes to be significantly associated with increased AD risk using 6,795 samples. Here, we used a large-scale GWAS dataset to investigate the potential association between the COPI genes and AD susceptibility by both SNP and gene-based tests. The results showed that only rs9898218 was associated with AD risk with P = 0.017. We further conducted an expression quantitative trait loci (eQTLs) analysis and found that rs9898218 G allele was associated with increased COPZ2 expression in cerebellar cortex with P = 0.0184. Importantly, the eQTLs analysis in whole blood further indicated that 11 of these 12 genetic variants could significantly regulate the expression of COPI genes. Hence, these findings may contribute to understand the association between COPI genes and AD susceptibility.
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Affiliation(s)
- Yu Yang
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, China
| | - Xu Wang
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, China
| | - Weina Ju
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, China
| | - Li Sun
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, China
| | - Haining Zhang
- Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, China
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Zhang Y, Yu F, Bao S, Sun J. Systematic Characterization of Circular RNA-Associated CeRNA Network Identified Novel circRNA Biomarkers in Alzheimer's Disease. Front Bioeng Biotechnol 2019; 7:222. [PMID: 31572720 PMCID: PMC6749152 DOI: 10.3389/fbioe.2019.00222] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 08/29/2019] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD), a degenerative disease of the central nervous system, is the most common form of dementia in old age. The complexity and behavior of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network remained poorly characterized in AD. The aim of this study was to elucidate the regulatory networks of dysregulated circRNAs from ceRNA view and identify potential risk circRNAs involved in AD pathogenesis. Consistent differentially expressed genes (CDEGs) were obtained using meta-analysis for multiple microarrays, and differentially expressed miRNAs (DEmiRs) were identified using empirical Bayes method. The circRNA-associated ceRNA network (cirCeNET) was constructed based on “ceRNA hypothesis” using an integrated system biology method. A total of 1,872 CDEGs and 48 DEmiRs were screened across different datasets. By mapping CDEGs and DEmiRs into the cirCeNET, an AD-related circRNA-associated ceRNA network (ADcirCeNET) was constructed, including 3,907 edges and 1,407 nodes (276 circRNAs, 14 miRNAs and 1,117 mRNAs). By prioritizing AD risk circRNA-associated ceRNAs, we found that the circRNA KIAA1586 occurred most frequently in the AD risk circRNA-associated ceRNAs and function as a ceRNA that operates by competitively binding three known AD-risk miRNAs. In silico functional analysis suggested that circRNA KIAA1586-related ceRNA network was significantly enriched in known AD-associated biological processes. Our study provided a global view and systematic dissection of circRNA-associated ceRNA network. The identified circRNA KIAA1586 may be a key risk factor involved in AD pathogenesis.
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Affiliation(s)
- Yan Zhang
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, China
| | - Fulong Yu
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, China
| | - Siqi Bao
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, China
| | - Jie Sun
- School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, China
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Liu G, Hu Y, Jiang Q. Population Difference and Disease Status Affect the Association Between Genetic Variants and Gene Expression. Gastroenterology 2019; 157:894-896. [PMID: 31228445 DOI: 10.1053/j.gastro.2019.01.278] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 01/07/2019] [Indexed: 12/02/2022]
Affiliation(s)
- Guiyou Liu
- Department of Neurology, Xuanwu Hospital and Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China and School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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Xu L, Liang G, Liao C, Chen GD, Chang CC. k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification. Front Genet 2019; 10:33. [PMID: 30809242 PMCID: PMC6379451 DOI: 10.3389/fgene.2019.00033] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/17/2019] [Indexed: 11/18/2022] Open
Abstract
In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| | - Gin-Den Chen
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan
- IT Office, Chung Shan Medical University Hospital, Taichung, Taiwan
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Genetic variant rs17185536 regulates SIM1 gene expression in human brain hypothalamus. Proc Natl Acad Sci U S A 2019; 116:3347-3348. [PMID: 30755538 DOI: 10.1073/pnas.1821550116] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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