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Wu Z, Shou L, Wang J, Huang T, Xu X. The Methylation Pattern for Knee and Hip Osteoarthritis. Front Cell Dev Biol 2020; 8:602024. [PMID: 33240895 PMCID: PMC7677303 DOI: 10.3389/fcell.2020.602024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 01/08/2023] Open
Abstract
Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.
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Affiliation(s)
- Zhen Wu
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Lu Shou
- Departmemt of Pneumology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Jian Wang
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Xinwei Xu
- Departmemt of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China
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2
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Cheng Q, Li J, Fan F, Cao H, Dai ZY, Wang ZY, Feng SS. Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing. Front Bioeng Biotechnol 2020; 8:167. [PMID: 32195242 PMCID: PMC7066068 DOI: 10.3389/fbioe.2020.00167] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 02/19/2020] [Indexed: 12/16/2022] Open
Abstract
Glioblastoma (GBM) is one of the most common and aggressive primary adult brain tumors. Tumor heterogeneity poses a great challenge to the treatment of GBM, which is determined by both heterogeneous GBM cells and a complex tumor microenvironment. Single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of great deal of individual cells to be assayed in an unbiased manner and has been applied in head and neck cancer, breast cancer, blood disease, and so on. In this study, based on the scRNA-seq results of infiltrating neoplastic cells in GBM, computational methods were applied to screen core biomarkers that can distinguish the discrepancy between GBM tumor and pericarcinomatous environment. The gene expression profiles of GBM from 2343 tumor cells and 1246 periphery cells were analyzed by maximum relevance minimum redundancy (mRMR). Upon further analysis of the feature lists yielded by the mRMR method, 31 important genes were extracted that may be essential biomarkers for GBM tumor cells. Besides, an optimal classification model using a support vector machine (SVM) algorithm as the classifier was also built. Our results provided insights of GBM mechanisms and may be useful for GBM diagnosis and therapy.
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Affiliation(s)
- Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Li
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Fan Fan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Cao
- Department of Psychiatry, The Second People's Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Zi-Yu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Ze-Yu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Song-Shan Feng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
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3
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Zhu Y, Jia C, Li F, Song J. Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Anal Biochem 2020; 593:113592. [DOI: 10.1016/j.ab.2020.113592] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/14/2020] [Accepted: 01/17/2020] [Indexed: 12/13/2022]
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4
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Liu M, Liu G. Prediction of Citrullination Sites on the Basis of mRMR Method and SNN. Comb Chem High Throughput Screen 2019; 22:705-715. [PMID: 31782357 DOI: 10.2174/1386207322666191129113508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/02/2019] [Accepted: 10/25/2019] [Indexed: 01/31/2023]
Abstract
BACKGROUND Citrullination, an important post-translational modification of proteins, alters the molecular weight and electrostatic charge of the protein side chains. Citrulline, in protein sequences, is catalyzed by a class of Peptidyl Arginine Deiminases (PADs). Dependent on Ca2+, PADs include five isozymes: PAD 1, 2, 3, 4/5, and 6. Citrullinated proteins have been identified in many biological and pathological processes. Among them, abnormal protein citrullination modification can lead to serious human diseases, including multiple sclerosis and rheumatoid arthritis. OBJECTIVE It is important to identify the citrullination sites in protein sequences. The accurate identification of citrullination sites may contribute to the studies on the molecular functions and pathological mechanisms of related diseases. METHODS AND RESULTS In this study, after an encoded training set (containing 116 positive and 348 negative samples) into the feature matrix, the mRMR method was used to analyze the 941- dimensional features which were sorted on the basis of their importance. Then, a predictive model based on a self-normalizing neural network (SNN) was proposed to predict the citrullination sites in protein sequences. Incremental Feature Selection (IFS) and 10-fold cross-validation were used as the model evaluation method. Three classical machine learning models, namely random forest, support vector machine, and k-nearest neighbor algorithm, were selected and compared with the SNN prediction model using the same evaluation methods. SNN may be the best tool for citrullination site prediction. The maximum value of the Matthews Correlation Coefficient (MCC) reached 0.672404 on the basis of the optimal classifier of SNN. CONCLUSION The results showed that the SNN-based prediction methods performed better when evaluated by some common metrics, such as MCC, accuracy, and F1-Measure. SNN prediction model also achieved a better balance in the classification and recognition of positive and negative samples from datasets compared with the other three models.
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Affiliation(s)
- Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Guangzhong Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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5
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Yao Y, Gu Y, Yang M, Cao D, Wu F. The Gene Expression Biomarkers for Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease. Front Genet 2019; 10:1154. [PMID: 31824564 PMCID: PMC6879656 DOI: 10.3389/fgene.2019.01154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 10/22/2019] [Indexed: 01/01/2023] Open
Abstract
COPD (chronic obstructive pulmonary disease) and ILD (interstitial lung disease) are two common respiratory diseases. They share similar clinical traits but require different therapeutic treatments. Identifying the biomarkers that are differentially expressed between them will not only help the diagnosis of COPD and ILD, but also provide candidate drug targets that may facilitate the development of new treatment for COPD and ILD. Due to the irreversible complex pathological changes of COPD, there are very limited therapeutic options for COPD patients. In this study, we analyzed the gene expression profiles of two datasets: one training dataset that includes 144 COPD patients and 194 ILD patients, and one test dataset that includes 75 COPD patients and 61 ILD patients. Advanced feature selection methods, mRMR (minimal Redundancy Maximal Relevance) and incremental feature selection (IFS), were applied to identify the 38-gene biomarker. An SVM (support vector machine) classifier was built based on the 38-gene biomarker. Its accuracy, sensitivity, and specificity on training dataset evaluated by leave one out cross-validation were 0.905, 0.896, and 0.912, respectively. And on independent test dataset, the accuracy, sensitivity, and specificity on were as great as and were 0.904, 0.933, and 0.869, respectively. The biological function analysis of the 38 genes indicated that many of them can be potential treatment targets that may benefit COPD and ILD patients.
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Affiliation(s)
- Yangwei Yao
- Department of Pulmonary and Critical Care Medicine, The Second Hospital of Jiaxing, Jiaxing, China
| | - Yangyang Gu
- Department of Pulmonary and Critical Care Medicine, The Second Hospital of Jiaxing, Jiaxing, China
| | - Meng Yang
- Department of Pulmonary and Critical Care Medicine, The Second Hospital of Jiaxing, Jiaxing, China
| | - Dakui Cao
- Department of Pulmonary and Critical Care Medicine, The Second Hospital of Jiaxing, Jiaxing, China
| | - Fengjie Wu
- Department of Pulmonary and Critical Care Medicine, The Second Hospital of Jiaxing, Jiaxing, China
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6
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Chen L, Li D, Shao Y, Wang H, Liu Y, Zhang Y. Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine. Front Genet 2019; 10:1146. [PMID: 31803234 PMCID: PMC6872643 DOI: 10.3389/fgene.2019.01146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.
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Affiliation(s)
- Lijuan Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Daojie Li
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Ye Shao
- School of Medicine, Huaqiao University, Quanzhou, China
| | - Hui Wang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Yuqing Liu
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| | - Yunhua Zhang
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
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7
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Pan X, Zeng T, Yuan F, Zhang YH, Chen L, Zhu L, Wan S, Huang T, Cai YD. Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas. Front Bioeng Biotechnol 2019; 7:339. [PMID: 31803734 PMCID: PMC6871504 DOI: 10.3389/fbioe.2019.00339] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 10/30/2019] [Indexed: 02/05/2023] Open
Abstract
Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.
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Affiliation(s)
- XiaoYong Pan
- School of Life Sciences, Shanghai University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.,IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Fei Yuan
- Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China
| | - Yu-Hang Zhang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China.,Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, China
| | - LiuCun Zhu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - SiBao Wan
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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8
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Zhang GL, Pan LL, Huang T, Wang JH. The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing. J Cancer 2019; 10:5883-5890. [PMID: 31737124 PMCID: PMC6843882 DOI: 10.7150/jca.32267] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 06/17/2019] [Indexed: 12/29/2022] Open
Abstract
The previous cancer studies were difficult to reproduce since the tumor tissues were analyzed directly. But the tumor tissues were actually a mixture of different cancer cells. The transcriptome of single-cell was much robust than the transcriptome of a mixed tissue. The single-cell transcriptome had much smaller variance. In this study, we analyzed the single-cell transcriptome of 272 colorectal cancer (CRC) epithelial cells and 160 normal epithelial cells and identified 342 discriminative transcripts using advanced machine learning methods. The most discriminative transcripts were LGALS4, PHGR1, C15orf48, HEPACAM2, PERP, FABP1, FCGBP, MT1G, TSPAN1 and CKB. We further clustered the 342 transcripts into two categories. The upregulated transcripts in CRC epithelial cells were significantly enriched in Ribosome, Protein processing in endoplasmic reticulum, Antigen processing and presentation and p53 signaling pathway. The downregulated transcripts in CRC epithelial cells were significantly enriched in Mineral absorption, Aldosterone-regulated sodium reabsorption and Oxidative phosphorylation pathways. The biological analysis of the discriminative transcripts revealed the possible mechanism of colorectal cancer.
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Affiliation(s)
- Guo-Liang Zhang
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - Le-Lin Pan
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jin-Hai Wang
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
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Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway. BIOMED RESEARCH INTERNATIONAL 2019; 2019:4963289. [PMID: 31396531 PMCID: PMC6668538 DOI: 10.1155/2019/4963289] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 06/04/2019] [Accepted: 06/26/2019] [Indexed: 12/19/2022]
Abstract
Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
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10
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Wang S, Li J, Sun X, Zhang YH, Huang T, Cai Y. Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm. Comb Chem High Throughput Screen 2018; 23:304-312. [PMID: 30588879 DOI: 10.2174/1386207322666181227144318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 09/03/2018] [Accepted: 12/04/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. OBJECTIVE In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. METHOD Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. RESULTS An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. CONCLUSION Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.
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Affiliation(s)
- ShaoPeng Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - JiaRui Li
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Xijun Sun
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yudong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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11
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Sheng M, Dong Z, Xie Y. Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer. Onco Targets Ther 2018; 11:8143-8151. [PMID: 30532555 PMCID: PMC6241732 DOI: 10.2147/ott.s177384] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention. METHODS In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods. RESULTS A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression. CONCLUSION The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.
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Affiliation(s)
- Meiling Sheng
- Department of Respiration, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China
| | - Zhaohui Dong
- Department of Intensive Care Unit, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China
| | - Yanping Xie
- Department of Respiratory Medicine, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China,
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12
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The early detection of asthma based on blood gene expression. Mol Biol Rep 2018; 46:217-223. [PMID: 30421126 DOI: 10.1007/s11033-018-4463-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/01/2018] [Indexed: 01/10/2023]
Abstract
Asthma is a complex heterogeneous disorder with hereditary tendency and the most widely used therapy is inhalation of anti-inflammatory corticosteroids. But it has systemic side effects. If the chronic inflammation can be detected in early stage, the dosage of corticosteroids will be low and the side effects can be avoided. Therefore, to discover the early stage blood biomarkers for asthma, we analyzed the gene expression profiles in the blood of 77 moderate asthma patients and 87 healthy controls. With advanced feature selection methods, minimal Redundancy Maximal Relevance and Incremental Feature Selection, we identified 31 genes, such as MYD88, ZFP36, CCR3 and CYP3A5, as the optimal asthma biomarker. The sensitivity, specificity and accuracy of the 31-gene Support Vector Machine predictor evaluated with Leave-One-Out Cross Validation were 0.870, 0.816 and 0.841, respectively. Through literature survey, many biomarker genes have asthma associated functions. Our results not only provided the easy-to-apply blood gene expression biomarkers for early detection of asthma, but also an explainable qualitative model with biological significance.
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13
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Lin H, Qiu X, Zhang B, Zhang J. Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy. Onco Targets Ther 2018; 11:5943-5955. [PMID: 30271178 PMCID: PMC6149834 DOI: 10.2147/ott.s167656] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Colorectal cancer is a malignant tumor with high death rate. Chemotherapy, radiotherapy and surgery are the three common treatments of colorectal cancer. For early colorectal cancer patients, postoperative adjuvant chemotherapy can reduce the risk of recurrence. For advanced colorectal cancer patients, palliative chemotherapy can significantly improve the life quality of patients and prolong survival. FOLFOX is one of the mainstream chemotherapies in colorectal cancer, however, its response rate is only about 50%. Methods To systematically investigate why some of the colorectal cancer patients have response to FOLFOX therapy while others do not, we searched all publicly available database and combined three gene expression datasets of colorectal cancer patients with FOLFOX therapy. With advanced minimal redundancy maximal relevance and incremental feature selection method, we identified the biomarker genes. Results A Support Vector Machine-based classifier was constructed to predict the response of colorectal cancer patients to FOLFOX therapy. Its accuracy, sensitivity and specificity were 0.854, 0.845 and 0.863, respectively. Conclusion The biological analysis of representative biomarker genes suggested that apoptosis and inflammation signaling pathways were essential for the response of colorectal cancer patients to FOLFOX chemotherapy.
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Affiliation(s)
- Hengjun Lin
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Xueke Qiu
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Bo Zhang
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Jichao Zhang
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
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14
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Cai L, Huang T, Su J, Zhang X, Chen W, Zhang F, He L, Chou KC. Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia. MOLECULAR THERAPY. NUCLEIC ACIDS 2018; 12:433-442. [PMID: 30195780 PMCID: PMC6041437 DOI: 10.1016/j.omtn.2018.05.026] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 05/19/2018] [Accepted: 05/30/2018] [Indexed: 12/21/2022]
Abstract
Schizophrenia (SCZ) is a devastating genetic mental disorder. Identification of the SCZ risk genes in brains is helpful to understand this disease. Thus, we first used the minimum Redundancy-Maximum Relevance (mRMR) approach to integrate the genome-wide sequence analysis results on SCZ and the expression quantitative trait locus (eQTL) data from ten brain tissues to identify the genes related to SCZ. Second, we adopted the variance inflation factor regression algorithm to identify their interacting genes in brains. Third, using multiple analysis methods, we explored and validated their roles. By means of the aforementioned procedures, we have found that (1) the cerebellum may play a crucial role in the pathogenesis of SCZ and (2) ITIH4 may be utilized as a clinical biomarker for the diagnosis of SCZ. These interesting findings may stimulate novel strategy for developing new drugs against SCZ. It has not escaped our notice that the approach reported here is of use for studying many other genome diseases as well.
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Affiliation(s)
- Lei Cai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Gordon Life Science Institute, Boston, MA 02478, USA; Shanghai Center for Women and Children's Health, Shanghai 200062, China.
| | - Tao Huang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jingjing Su
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China
| | - Xinxin Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China
| | - Wenzhong Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China
| | - Fuquan Zhang
- Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi 214015, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Shanghai Center for Women and Children's Health, Shanghai 200062, China.
| | - Kuo-Chen Chou
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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15
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Li J, Lan CN, Kong Y, Feng SS, Huang T. Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods. Front Genet 2018; 9:246. [PMID: 30214455 PMCID: PMC6125376 DOI: 10.3389/fgene.2018.00246] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 06/22/2018] [Indexed: 12/15/2022] Open
Abstract
Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.
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Affiliation(s)
- Jing Li
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chun-Na Lan
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ying Kong
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Song-Shan Feng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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16
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Zhang TM, Huang T, Wang RF. Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer. Oncol Lett 2018; 16:1736-1746. [PMID: 30008861 PMCID: PMC6036478 DOI: 10.3892/ol.2018.8860] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 05/22/2018] [Indexed: 12/20/2022] Open
Abstract
Colorectal cancer is a severe cancer associated with a high prevalence and fatality rate. There are three major mechanisms for colorectal cancer: (1) Chromosome instability (CIN), (2) CpG island methylator phenotype (CIMP) and (3) mismatch repair (MMR), of which CIN is the most common type. However, these subtypes are not exclusive and overlap. To investigate their biological mechanisms and cross talk, the gene expression profiles of 585 colorectal cancer patients with CIN, CIMP and MMR status records were collected. By comparing the CIN+ and CIN-samples, CIMP+ and CIMP-samples, MMR+ and MMR-samples with minimal redundancy maximal relevance (mRMR) and incremental feature selection (IFS) methods, the CIN, CIMP and MMR associated genes were selected. Unfortunately, there was little direct overlap among them. To investigate their indirect interactions, downstream genes of CIN, CIMP and MMR were identified using the random walk with restart (RWR) method and a greater overlap of downstream genes was indicated. The common downstream genes were involved in biosynthetic and metabolic pathways. These findings were consistent with the clinical observation of wide range metabolite aberrations in colorectal cancer. To conclude, the present study gave a gene level explanation of CIN, CIMP and MMR, but also showed the network level cross talk of CIN, CIMP and MMR. The common genes of CIN, CIMP and MMR may be useful for cross-subtype general colorectal cancer drug development.
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Affiliation(s)
- Tian-Ming Zhang
- Department of Colorectal and Anal Surgery, Jinhua Hospital of Zhejiang University, Jinhua, Zhejiang 321000, P.R. China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P.R. China
| | - Rong-Fei Wang
- Department of Colorectal and Anal Surgery, Jinhua People's Hospital, Jinhua, Zhejiang 321000, P.R. China
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17
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Wang S, Wang D, Li J, Huang T, Cai YD. Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods. Mol Omics 2018; 14:64-73. [DOI: 10.1039/c7mo00030h] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Several machine learning algorithms were adopted to investigate cleavage sites in a signal peptide. An optimal dagging based classifier was constructed and 870 important features were deemed to be important for this classifier.
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Affiliation(s)
- ShaoPeng Wang
- School of Life Sciences
- Shanghai University
- Shanghai 200444
- People's Republic of China
| | - Deling Wang
- Department of Medical Imaging
- Sun Yat-sen University Cancer Center
- State Key Laboratory of Oncology in South China
- Collaborative Innovation Center for Cancer Medicine
- Guangzhou
| | - JiaRui Li
- School of Life Sciences
- Shanghai University
- Shanghai 200444
- People's Republic of China
| | - Tao Huang
- Institute of Health Sciences
- Shanghai Institutes for Biological Sciences
- Chinese Academy of Sciences
- Shanghai 200031
- People's Republic of China
| | - Yu-Dong Cai
- School of Life Sciences
- Shanghai University
- Shanghai 200444
- People's Republic of China
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18
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Deng L, Xu X, Liu H. PredCSO: an ensemble method for the prediction of S-sulfenylation sites in proteins. Mol Omics 2018; 14:257-265. [DOI: 10.1039/c8mo00089a] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Predicting S-sulfenylation sites in proteins based on sequence and structural features by building an ensemble model by gradient tree boosting.
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Affiliation(s)
- Lei Deng
- School of Software, Central South University
- Changsha
- China
| | - Xiaojie Xu
- School of Software, Central South University
- Changsha
- China
| | - Hui Liu
- School of Software, Central South University
- Changsha
- China
- Lab of Information Management, Changzhou University
- Jiangsu
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19
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Chen L, Zhang YH, Zheng M, Huang T, Cai YD. Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol Genet Genomics 2016; 291:2065-2079. [PMID: 27530612 DOI: 10.1007/s00438-016-1240-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Accepted: 08/09/2016] [Indexed: 12/13/2022]
Abstract
Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Mingyue Zheng
- Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Shanghai, 201203, People's Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, People's Republic of China.
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20
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Liu L, Chen L, Zhang YH, Wei L, Cheng S, Kong X, Zheng M, Huang T, Cai YD. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection. J Biomol Struct Dyn 2016; 35:312-329. [PMID: 26750516 DOI: 10.1080/07391102.2016.1138142] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
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Affiliation(s)
- Lili Liu
- a Intelligence Research Department, Information Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China
| | - Lei Chen
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Yu-Hang Zhang
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Lai Wei
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Shiwen Cheng
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Xiangyin Kong
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Mingyue Zheng
- d State Key Laboratory of Drug Research, Drug Discovery and Design Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China
| | - Tao Huang
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Yu-Dong Cai
- e School of Life Sciences, Shanghai University , Shanghai 200444 , P. R. China
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21
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Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8351204. [PMID: 26955638 PMCID: PMC4756144 DOI: 10.1155/2016/8351204] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 01/05/2016] [Indexed: 12/02/2022]
Abstract
The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.
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22
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Huang T, Shu Y, Cai YD. Genetic differences among ethnic groups. BMC Genomics 2015; 16:1093. [PMID: 26690364 PMCID: PMC4687076 DOI: 10.1186/s12864-015-2328-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 12/15/2015] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Many differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA (Principal Component Analysis) plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations. RESULTS We applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO (sequential minimal optimization) model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895. CONCLUSIONS In-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.
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Affiliation(s)
- Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, P. R. China.
| | - Yang Shu
- Sate Key Laboratory of Biotherapy, Sichuan University, Sichuan, 610041, P. R. China.
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, 200444, P. R. China.
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