1
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O’Connor LM, O’Connor BA, Zeng J, Lo CH. Data Mining of Microarray Datasets in Translational Neuroscience. Brain Sci 2023; 13:1318. [PMID: 37759919 PMCID: PMC10527016 DOI: 10.3390/brainsci13091318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/04/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.
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Affiliation(s)
- Lance M. O’Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Blake A. O’Connor
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA;
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore;
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore;
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2
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Ren Z, Chu C, Pang Y, Cai H, Jia L. A Group of Long Non-coding RNAs in Blood Acts as a Specific Biomarker of Alzheimer's Disease. Mol Neurobiol 2023; 60:566-575. [PMID: 36327022 DOI: 10.1007/s12035-022-03105-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022]
Abstract
Long non-coding RNAs (lncRNAs) have been identified to be involved in the pathogenesis of Alzheimer's disease (AD). In this study, we evaluated whether lncRNAs can be used to discriminate AD patients from controls and patients with other dementias, such as vascular, Parkinson's disease, behavioral variant frontotemporal, and dementia with Lewy body. In this study, we used three datasets to measure the blood lncRNA levels. A pilot study (dataset 1, n = 40; controls, 20; AD, 20) was used to screen for differentially expressed lncRNAs. Dataset 2 (n = 174; controls, 86; AD, 88) was used to identify a lncRNA panel for the diagnostic model. Dataset 3 (n = 333; control, 60; AD, 54; vascular dementia, 53; Parkinson's disease dementia, 55; behavioral variant frontotemporal dementia, 56; and dementia with Lewy body, 55) was used to validate the diagnostic model. In dataset 1, 12 upregulated and 15 downregulated lncRNAs were identified. In dataset 2, a panel of seven lncRNAs was found to have the ability to differentiate AD patients from controls. Finally, this panel was applied to dataset 3 to successfully distinguish AD from other dementias. This study proposes a panel of seven lncRNAs as specific and promising biomarker for AD diagnosis.
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Affiliation(s)
- Ziye Ren
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun St., Beijing, China
| | - Changbiao Chu
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun St., Beijing, China
| | - Yana Pang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun St., Beijing, China
| | - Huimin Cai
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun St., Beijing, China
| | - Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun St., Beijing, China.
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3
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Li C, Zhang Y, Liang J, Wu C, Zou X. Assessing the Association Between Lead Pollution and Risk of Alzheimer’s Disease by Integrating Multigenomics. Front Neurosci 2022; 16:880105. [PMID: 35937890 PMCID: PMC9353516 DOI: 10.3389/fnins.2022.880105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/30/2022] [Indexed: 12/04/2022] Open
Abstract
Alzheimer’s disease (AD) is a life-threatening neurodegenerative disease of the elderly. In recent observations, exposure to heavy metals environmental may increase the risk of AD. However, there are few studies on the causal relationship between heavy metal exposure and AD. In this study, we integrated two large-scale summaries of AD genome-wide association study (GWAS) datasets and a blood lead level GWAS dataset and performed the two-sample Mendelian randomization analysis to assess the causality of blood lead level and AD risk. The results showed that there is a significantly positive causality between blood lead level and AD risk both in the inverse-variance weighted (IVW) model and the weighted median estimator (WME) model. An independent additional verification also reached a consistent conclusion. These findings further confirm the conclusions of previous studies and improve the understanding of the relationship between AD pathogenesis and the toxicity of lead in environmental pollution.
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Affiliation(s)
- Chunying Li
- Institute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China
- Department of Environmental Engineering, Chongqing Vocational College of Resources and Environmental Protection, Chongqing, China
| | - Yuwei Zhang
- Institute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China
| | - Jiandong Liang
- Basic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Changyan Wu
- Basic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Xiao Zou
- Institute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China
- *Correspondence: Xiao Zou,
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4
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Fathi S, Ahmadi M, Dehnad A. Early diagnosis of Alzheimer's disease based on deep learning: A systematic review. Comput Biol Med 2022; 146:105634. [DOI: 10.1016/j.compbiomed.2022.105634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 04/25/2022] [Accepted: 04/25/2022] [Indexed: 11/03/2022]
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5
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Varesi A, Carrara A, Pires VG, Floris V, Pierella E, Savioli G, Prasad S, Esposito C, Ricevuti G, Chirumbolo S, Pascale A. Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview. Cells 2022; 11:1367. [PMID: 35456047 PMCID: PMC9044750 DOI: 10.3390/cells11081367] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 01/10/2023] Open
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.
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Affiliation(s)
- Angelica Varesi
- Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy
- Almo Collegio Borromeo, 27100 Pavia, Italy
| | - Adelaide Carrara
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (A.C.); (V.F.)
| | - Vitor Gomes Pires
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA;
| | - Valentina Floris
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (A.C.); (V.F.)
| | - Elisa Pierella
- School of Medicine, Faculty of Clinical and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK;
| | - Gabriele Savioli
- Emergency Department, IRCCS Policlinico San Matteo, 27100 Pavia, Italy;
| | - Sakshi Prasad
- Faculty of Medicine, National Pirogov Memorial Medical University, 21018 Vinnytsya, Ukraine;
| | - Ciro Esposito
- Unit of Nephrology and Dialysis, ICS Maugeri, University of Pavia, 27100 Pavia, Italy;
| | - Giovanni Ricevuti
- Department of Drug Sciences, University of Pavia, 27100 Pavia, Italy
| | - Salvatore Chirumbolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy;
| | - Alessia Pascale
- Department of Drug Sciences, Section of Pharmacology, University of Pavia, 27100 Pavia, Italy;
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6
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Policarpo R, d’Ydewalle C. Missing lnc(RNAs) in Alzheimer's Disease? Genes (Basel) 2021; 13:39. [PMID: 35052379 PMCID: PMC8774680 DOI: 10.3390/genes13010039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 12/26/2022] Open
Abstract
With the ongoing demographic shift towards increasingly elderly populations, it is estimated that approximately 150 million people will live with Alzheimer's disease (AD) by 2050. By then, AD will be one of the most burdensome diseases of this and potentially next centuries. Although its exact etiology remains elusive, both environmental and genetic factors play crucial roles in the mechanisms underlying AD neuropathology. Genome-wide association studies (GWAS) identified genetic variants associated with AD susceptibility in more than 40 different genomic loci. Most of these disease-associated variants reside in non-coding regions of the genome. In recent years, it has become clear that functionally active transcripts arise from these non-coding loci. One type of non-coding transcript, referred to as long non-coding RNAs (lncRNAs), gained significant attention due to their multiple roles in neurodevelopment, brain homeostasis, aging, and their dysregulation or dysfunction in neurological diseases including in AD. Here, we will summarize the current knowledge regarding genetic variations, expression profiles, as well as potential functions, diagnostic or therapeutic roles of lncRNAs in AD. We postulate that lncRNAs may represent the missing link in AD pathology and that unraveling their role may open avenues to better AD treatments.
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Affiliation(s)
- Rafaela Policarpo
- VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium;
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
- Neuroscience Discovery, Janssen Research & Development, Janssen Pharmaceutica N.V., 2340 Beerse, Belgium
| | - Constantin d’Ydewalle
- Neuroscience Discovery, Janssen Research & Development, Janssen Pharmaceutica N.V., 2340 Beerse, Belgium
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7
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Wang J, Yang L, Li Y, Wang XB, Yang W, Liu F, Wu XB. Aberrant hypermethylation induced downregulation of antisense lncRNA STXBP5-AS1 and its sense gene STXBP5 correlate with tumorigenesis of glioma. Life Sci 2021; 278:119590. [PMID: 33965377 DOI: 10.1016/j.lfs.2021.119590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 10/21/2022]
Abstract
AIMS The expression of antisense lncRNA STXBP5-AS1 and its sense gene STXBP5 were found to be downregulated in glioma by RNA sequencing; however, the function and mechanism of both two genes in the development of glioma have not been studied. MATERIALS AND METHODS QRT-PCR and western blot were used to determine the transcriptional and translational levels of moleculars. MSP and BSP assays were used to evaluate the methylation status of promoter CpG island. MTT, EdU, flow cytometry, and transwell assays were used to reveal biological effects. The in vivo mice model was used to validate the role of target genes in tumorigenesis. KEY FINDINGS The mRNA and protein expression of STXBP5 was significantly downregulated in glioma tissues and positively correlated with prognosis. STXBP5-AS1 was downregulated in glioma cells and tissues, and associated with tumor size and clinical stages. Both of two genes were significantly restored in cells treatment with 5-Aza. The promoter CpG island of STXBP5/STXBP5-AS1 was hypermethylated in glioma cells, but partially methylated in NHA cells. We found that promoter methylation frequency was significantly higher in glioma tissues. Functionally, overexpression of STXBP5 and STXBP5-AS1 inhibited cell proliferation, migration, and invasion and promoted apoptosis in vitro, whereas depletion of STXBP5 and STXBP5-AS1 showed opposite effects. Both the mRNA and protein expression of STXBP5 were positively regulated by STXBP5-AS1. Ectopic expression of STXBP5 and STXBP5-AS1 suppressed tumor formation in vivo. SIGNIFICANCE Our findings suggested that epigenetically silenced STXBP5-AS1 and STXBP5 might act as novel tumor suppressors of glioma.
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Affiliation(s)
- Jing Wang
- Department of Laboratory Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250000, China
| | - Lei Yang
- Department of Medical Engineering, Shandong Provincial Hospital Affiliated to Shandong First Medical University; Jinan, Shandong 250000, China
| | - Yuan Li
- Department of Laboratory Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250000, China
| | - Xing-Bang Wang
- Department of Neurology, Qilu Hospital of Shandong University, Jinan, Shandong 250000, China
| | - Wei Yang
- Department of Neurological Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250000, China
| | - Fang Liu
- Department of Laboratory Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250000, China
| | - Xiao-Ben Wu
- Department of Laboratory Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250000, China.
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8
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López-Jiménez E, Andrés-León E. The Implications of ncRNAs in the Development of Human Diseases. Noncoding RNA 2021; 7:17. [PMID: 33668203 PMCID: PMC8006041 DOI: 10.3390/ncrna7010017] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/14/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
The mammalian genome comprehends a small minority of genes that encode for proteins (barely 2% of the total genome in humans) and an immense majority of genes that are transcribed into RNA but not encoded for proteins (ncRNAs). These non-coding genes are intimately related to the expression regulation of protein-coding genes. The ncRNAs subtypes differ in their size, so there are long non-coding genes (lncRNAs) and other smaller ones, like microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Due to their important role in the maintenance of cellular functioning, any deregulation of the expression profiles of these ncRNAs can dissemble in the development of different types of diseases. Among them, we can highlight some of high incidence in the population, such as cancer, neurodegenerative, or cardiovascular disorders. In addition, thanks to the enormous advances in the field of medical genomics, these same ncRNAs are starting to be used as possible drugs, approved by the FDA, as an effective treatment for diseases.
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Affiliation(s)
- Elena López-Jiménez
- Centre for Haematology, Immunology and Inflammation Department, Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Eduardo Andrés-León
- Unidad de Bioinformática, Instituto de Parasitología y Biomedicina “López-Neyra”, Consejo Superior de Investigaciones Científicas, 18016 Granada, Spain
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9
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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10
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Hong J, Luo Y, Zhang Y, Ying J, Xue W, Xie T, Tao L, Zhu F. Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning. Brief Bioinform 2019; 21:1437-1447. [PMID: 31504150 PMCID: PMC7412958 DOI: 10.1093/bib/bbz081] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 05/27/2019] [Accepted: 06/10/2019] [Indexed: 11/12/2022] Open
Abstract
Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.
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Affiliation(s)
- Jiajun Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Junbiao Ying
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Feng Zhu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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11
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Yang QX, Wang YX, Li FC, Zhang S, Luo YC, Li Y, Tang J, Li B, Chen YZ, Xue WW, Zhu F. Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility. CNS Neurosci Ther 2019; 25:1054-1063. [PMID: 31350824 PMCID: PMC6698965 DOI: 10.1111/cns.13196] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/27/2019] [Accepted: 07/03/2019] [Indexed: 12/15/2022] Open
Abstract
Aims As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ. Methods In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted. Results Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ. Conclusion A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.
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Affiliation(s)
- Qing-Xia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yun-Xia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng-Cheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yong-Chao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yu-Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Wei-Wei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
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12
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Tang J, Wang Y, Fu J, Zhou Y, Luo Y, Zhang Y, Li B, Yang Q, Xue W, Lou Y, Qiu Y, Zhu F. A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies. Brief Bioinform 2019; 21:1378-1390. [DOI: 10.1093/bib/bbz061] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 04/14/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Department of Bioinformatics, Chongqing Medical University, Chongqing, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
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Yang Q, Li B, Tang J, Cui X, Wang Y, Li X, Hu J, Chen Y, Xue W, Lou Y, Qiu Y, Zhu F. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief Bioinform 2019; 21:1058-1068. [DOI: 10.1093/bib/bbz049] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 03/11/2019] [Accepted: 03/30/2019] [Indexed: 12/16/2022] Open
Abstract
Abstract
The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.
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Affiliation(s)
- Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Bo Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yunxia Wang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xiaofeng Li
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jie Hu
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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