1
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Zhang W, Dang R, Liu H, Dai L, Liu H, Adegboro AA, Zhang Y, Li W, Peng K, Hong J, Li X. Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients. Sci Rep 2024; 14:4173. [PMID: 38378721 PMCID: PMC10879095 DOI: 10.1038/s41598-024-54643-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.
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Affiliation(s)
- Wei Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Ruiyue Dang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongyi Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Luohuan Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Hongwei Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Abraham Ayodeji Adegboro
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Yihao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Wang Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Kang Peng
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jidong Hong
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China.
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2
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Zhang N, Yang F, Zhao P, Jin N, Wu H, Liu T, Geng Q, Yang X, Cheng L. MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients. Brief Bioinform 2023; 25:bbad498. [PMID: 38171932 PMCID: PMC10782913 DOI: 10.1093/bib/bbad498] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/17/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024] Open
Abstract
N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases of glioma.
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Affiliation(s)
- Ning Zhang
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
- Neuroscience Center, Shantou University Medical College, Shantou, China
| | - Fengxia Yang
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
- Neuroscience Center, Shantou University Medical College, Shantou, China
| | - Pengfei Zhao
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
| | - Nana Jin
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
| | - Haonan Wu
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
| | - Tao Liu
- International Digital Economy Academy, Shenzhen, China
| | - Qingshan Geng
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
| | - Xiaojun Yang
- Neuroscience Center, Shantou University Medical College, Shantou, China
| | - Lixin Cheng
- Guangdong Provincial Clinical Research Center for Geriatrics, Shenzhen Clinical Research Center for Geriatrics, Shenzhen People's Hospital, Shenzhen, China
- The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University
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3
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Liu X, Hong C, Jiang Y, Li W, Chen Y, Ma Y, Zhao P, Li T, Chen H, Liu X, Cheng L. Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis. BMC Genomics 2023; 24:418. [PMID: 37488493 PMCID: PMC10364430 DOI: 10.1186/s12864-023-09460-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.
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Affiliation(s)
- Xiaojun Liu
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Chengying Hong
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Yichun Jiang
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Wei Li
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Youlian Chen
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Yonghui Ma
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Pengfei Zhao
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Tiyuan Li
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Huaisheng Chen
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
| | - Xueyan Liu
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
| | - Lixin Cheng
- Department of Critical Care, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
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4
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Shademan B, Karamad V, Nourazarian A, Masjedi S, Isazadeh A, Sogutlu F, Avcı CB. MicroRNAs as Targets for Cancer Diagnosis: Interests and Limitations. Adv Pharm Bull 2023; 13:435-445. [PMID: 37646065 PMCID: PMC10460809 DOI: 10.34172/apb.2023.047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 04/02/2022] [Accepted: 07/01/2022] [Indexed: 09/01/2023] Open
Abstract
MicroRNAs are small RNAs with ability to attach to the large number of RNA that regulate gene expression on post-transcriptional level via inhibition or degradation of specific mRNAs. MiRNAs in cells are the primary regulators of functions such as cell growth, differentiation, and apoptosis and considerably influence cell function. The expression levels of microRNAs change in human diseases, including cancer. These changes highlight their essential role in cancer pathogenesis. Ubiquitous irregular expression profiles of miRNAs have been detected in various human cancers using genome-wide identification techniques, which are emerging as novel diagnostic and prognostic cancer biomarkers of high specificity and sensitivity. The measurable miRNAs with enhanced stability in blood, tissues, and other body fluids provide a comprehensive source of miRNA-dependent biomarkers for human cancers. The leading role of miRNAs as potential biomarkers in human cancers is discussed in this article. In addition, the interests and difficulties of miRNAs as biomarkers have been explored.
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Affiliation(s)
- Behrouz Shademan
- Department of Medical Biology, Faculty of Medicine, EGE University, Izmir, Turkey
| | - Vahidreza Karamad
- Department of Medical Biology, Faculty of Medicine, EGE University, Izmir, Turkey
| | - Alireza Nourazarian
- Department of Basic Medical Sciences, Khoy University of Medical Sciences, Khoy, Iran
| | - Sepideh Masjedi
- Department of Cellular and Molecular Biology Sciences, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
| | - Alireza Isazadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fatma Sogutlu
- Department of Medical Biology, Faculty of Medicine, EGE University, Izmir, Turkey
| | - Cigir Biray Avcı
- Department of Medical Biology, Faculty of Medicine, EGE University, Izmir, Turkey
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5
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Yosef A, Shnaider E, Schneider M, Gurevich M. Heuristic normalization procedure for batch effect correction. Soft comput 2023. [DOI: 10.1007/s00500-023-08049-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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6
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Li Q, Zheng X, Xie J, Wang R, Li M, Wong MH, Leung KS, Li S, Geng Q, Cheng L. bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks. Bioinformatics 2023; 39:7066914. [PMID: 36857587 PMCID: PMC9997702 DOI: 10.1093/bioinformatics/btad109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/05/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023] Open
Abstract
MOTIVATION The confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery. RESULTS Here, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model (bacterial-viral-noninfected GPS, bvnGPS) using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953 (95% confidence interval, 0.948-0.958) and viral infection with AUC of 0.956 (0.951-0.961) in the test set (N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978-0.998) on bacterial-versus-other and an AUC of 0.994 (0.984-1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data. AVAILABILITY AND IMPLEMENTATION The codes implementing bvnGPS are available at https://github.com/Ritchiegit/bvnGPS. The construction of iPAGE algorithm and the training of neural network was conducted on Python 3.7 with Scikit-learn 0.24.1 and PyTorch 1.7. The visualization of the results was implemented on R 4.2, Python 3.7, and Matplotlib 3.3.4.
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Affiliation(s)
- Qizhi Li
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.,Great Bay University, Dongguan, China
| | - Jize Xie
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ran Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Mengyao Li
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.,Department of Applied Data Science, Hong Kong Shue Yan University, North Point, Hong Kong
| | - Shuai Li
- John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China
| | - Qingshan Geng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
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7
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Foltz SM, Greene CS, Taroni JN. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously. Commun Biol 2023; 6:222. [PMID: 36841852 PMCID: PMC9968332 DOI: 10.1038/s42003-023-04588-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 02/13/2023] [Indexed: 02/27/2023] Open
Abstract
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
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Affiliation(s)
- Steven M Foltz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Wynnewood, PA, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
| | - Jaclyn N Taroni
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Wynnewood, PA, USA.
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8
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Cheng L, Wu H, Zheng X, Zhang N, Zhao P, Wang R, Wu Q, Liu T, Yang X, Geng Q. GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion. Bioinformatics 2023; 39:6986965. [PMID: 36637205 PMCID: PMC9843586 DOI: 10.1093/bioinformatics/btac850] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/14/2022] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. AVAILABILITY AND IMPLEMENTATION Codes are available at https://github.com/Kimxbzheng/GPGPS.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lixin Cheng
- To whom correspondence should be addressed. or
| | | | - Xubin Zheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Ning Zhang
- Guangdong Provincial Key Laboratory of Infectious Disease and Molecular Immunopathology, Shantou University Medical College, Shantou 515041, China
| | - Pengfei Zhao
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
- Department of Geriatrics, Shenzhen Clinical Research Center for Aging, Shenzhen 518020, China
| | - Ran Wang
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Qiong Wu
- Hong Kong Genome Institute, Shatin, New Territories, Hong Kong
| | - Tao Liu
- International Digital Economy Academy, Shenzhen 518020, China
| | - Xiaojun Yang
- Guangdong Provincial Key Laboratory of Infectious Disease and Molecular Immunopathology, Shantou University Medical College, Shantou 515041, China
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9
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Chunikhina E, Logan P, Kovchegov Y, Yambartsev A, Mondal D, Morgun A. The C-SHIFT Algorithm for Normalizing Covariances. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:720-730. [PMID: 35167480 DOI: 10.1109/tcbb.2022.3151840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method's advantage over the classical normalization techniques. Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods. We also evaluate the performance of C-SHIFT algorithm on real biological data.
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10
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Yosef A, Shnaider E, Schneider M, Gurevich M. Normalization of Large-Scale Transcriptome Data Using Heuristic Methods. Bioinform Biol Insights 2023; 17:11779322231160397. [PMID: 37020503 PMCID: PMC10068970 DOI: 10.1177/11779322231160397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/09/2023] [Indexed: 04/03/2023] Open
Abstract
In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the discrepancy in gene expression data series, measured under different conditions. While the data from the same batch (measurements performed under the same conditions) are compatible, combining various batches into 1 data set is problematic because of incompatible measurements. Therefore, it is necessary to perform correction of the combined data (normalization), before performing biological analysis. There are numerous methods attempting to correct data set for batch effect. These methods rely on various assumptions regarding the distribution of the measurements. Forcing the data elements into pre-supposed distribution can severely distort biological signals, thus leading to incorrect results and conclusions. As the discrepancy between the assumptions regarding the data distribution and the actual distribution is wider, the biases introduced by such “correction methods” are greater. We introduce a heuristic method to reduce batch effect. The method does not rely on any assumptions regarding the distribution and the behavior of data elements. Hence, it does not introduce any new biases in the process of correcting the batch effect. It strictly maintains the integrity of measurements within the original batches.
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11
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Yang Y, Zhang Y, Li S, Zheng X, Wong MH, Leung KS, Cheng L. A Robust and Generalizable Immune-Related Signature for Sepsis Diagnostics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3246-3254. [PMID: 34437068 DOI: 10.1109/tcbb.2021.3107874] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.
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12
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Xu C, Li W, Li T, Yuan J, Pang X, Liu T, Liang B, Cheng L, Sun X, Dong S. Iron metabolism-related genes reveal predictive value of acute coronary syndrome. Front Pharmacol 2022; 13:1040845. [PMID: 36330096 PMCID: PMC9622999 DOI: 10.3389/fphar.2022.1040845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/04/2022] [Indexed: 11/25/2022] Open
Abstract
Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.
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Affiliation(s)
- Cong Xu
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Wanyang Li
- School of Mathematics, South China University of Technology, Guangzhou, China
| | - Tangzhiming Li
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Jie Yuan
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Xinli Pang
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Tao Liu
- International Digital Economy Academy, Shenzhen, China
| | - Benhui Liang
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Lixin Cheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
| | - Xin Sun
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
| | - Shaohong Dong
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
- *Correspondence: Lixin Cheng, ; Xin Sun, ; Shaohong Dong,
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13
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Wu Q, Zheng X, Leung KS, Wong MH, Tsui SKW, Cheng L. meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data. Bioinformatics 2022; 38:3513-3522. [PMID: 35674358 DOI: 10.1093/bioinformatics/btac379] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 05/08/2022] [Accepted: 06/01/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Hepatocellular carcinoma (HCC) is a primary malignancy with poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. RESULTS The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender, and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. AVAILABILITY AND IMPLEMENTATION Models are available at https://github.com/bioinformaticStudy/meGPS.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiong Wu
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.,School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.,Department of Paediatrics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
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14
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Platelets Purification Is a Crucial Step for Transcriptomic Analysis. Int J Mol Sci 2022; 23:ijms23063100. [PMID: 35328521 PMCID: PMC8953733 DOI: 10.3390/ijms23063100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/02/2022] [Accepted: 03/08/2022] [Indexed: 01/24/2023] Open
Abstract
Platelets are small anucleate cells derived from the fragmentation of megakaryocytes and are involved in different biological processes especially hemostasis, thrombosis, and immune response. Despite their lack of nucleus, platelets contain a reservoir of megakaryocyte-derived RNAs and all the machinery useful for mRNA translation. Interestingly, platelet transcriptome was analyzed in health and diseases and led to the identification of disease-specific molecular signatures. Platelet contamination by leukocytes and erythrocytes during platelet purification is a major problem in transcriptomic analysis and the presence of few contaminants in platelet preparation could strongly alter transcriptome results. Since contaminant impacts on platelet transcriptome remains theoretical, we aimed to determine whether low leukocyte and erythrocyte contamination could cause great or only minor changes in platelet transcriptome. Using microarray technique, we compared the transcriptome of platelets from the same donor, purified by common centrifugation method or using magnetic microbeads to eliminate contaminating cells. We found that platelet transcriptome was greatly altered by contaminants, as the relative amount of 8274 transcripts was different between compared samples. We observed an increase of transcripts related to leukocytes and erythrocytes in platelet purified without microbeads, while platelet specific transcripts were falsely reduced. In conclusion, serious precautions should be taken during platelet purification process for transcriptomic analysis, in order to avoid platelets contamination and result alteration.
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15
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Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data. Int J Mol Sci 2021; 22:ijms22168451. [PMID: 34445156 PMCID: PMC8395121 DOI: 10.3390/ijms22168451] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/30/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.
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16
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Song Y, Zhu S, Zhang N, Cheng L. Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer. Front Oncol 2021; 11:723779. [PMID: 34368003 PMCID: PMC8343071 DOI: 10.3389/fonc.2021.723779] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/08/2021] [Indexed: 01/07/2023] Open
Abstract
Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis.
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Affiliation(s)
- Yang Song
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Suzhu Zhu
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Ning Zhang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
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17
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Zheng X, Wu Q, Wu H, Leung KS, Wong MH, Liu X, Cheng L. Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data. Front Cell Dev Biol 2021; 9:671302. [PMID: 33996828 PMCID: PMC8116545 DOI: 10.3389/fcell.2021.671302] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/06/2021] [Indexed: 01/07/2023] Open
Abstract
Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.
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Affiliation(s)
- Xubin Zheng
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Qiong Wu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.,School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haonan Wu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Xueyan Liu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Lixin Cheng
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
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18
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Barretto SA, Lasserre F, Huillet M, Régnier M, Polizzi A, Lippi Y, Fougerat A, Person E, Bruel S, Bétoulières C, Naylies C, Lukowicz C, Smati S, Guzylack L, Olier M, Théodorou V, Mselli-Lakhal L, Zalko D, Wahli W, Loiseau N, Gamet-Payrastre L, Guillou H, Ellero-Simatos S. The pregnane X receptor drives sexually dimorphic hepatic changes in lipid and xenobiotic metabolism in response to gut microbiota in mice. MICROBIOME 2021; 9:93. [PMID: 33879258 PMCID: PMC8059225 DOI: 10.1186/s40168-021-01050-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 03/16/2021] [Indexed: 05/10/2023]
Abstract
BACKGROUND The gut microbiota-intestine-liver relationship is emerging as an important factor in multiple hepatic pathologies, but the hepatic sensors and effectors of microbial signals are not well defined. RESULTS By comparing publicly available liver transcriptomics data from conventional vs. germ-free mice, we identified pregnane X receptor (PXR, NR1I2) transcriptional activity as strongly affected by the absence of gut microbes. Microbiota depletion using antibiotics in Pxr+/+ vs Pxr-/- C57BL/6J littermate mice followed by hepatic transcriptomics revealed that most microbiota-sensitive genes were PXR-dependent in the liver in males, but not in females. Pathway enrichment analysis suggested that microbiota-PXR interaction controlled fatty acid and xenobiotic metabolism. We confirmed that antibiotic treatment reduced liver triglyceride content and hampered xenobiotic metabolism in the liver from Pxr+/+ but not Pxr-/- male mice. CONCLUSIONS These findings identify PXR as a hepatic effector of microbiota-derived signals that regulate the host's sexually dimorphic lipid and xenobiotic metabolisms in the liver. Thus, our results reveal a potential new mechanism for unexpected drug-drug or food-drug interactions. Video abstract.
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Affiliation(s)
- Sharon Ann Barretto
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Frederic Lasserre
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Marine Huillet
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Marion Régnier
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Arnaud Polizzi
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Yannick Lippi
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Anne Fougerat
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Elodie Person
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Sandrine Bruel
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Colette Bétoulières
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Claire Naylies
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Céline Lukowicz
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Sarra Smati
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Laurence Guzylack
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Maïwenn Olier
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Vassilia Théodorou
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Laila Mselli-Lakhal
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Daniel Zalko
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Walter Wahli
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, 308232, Singapore
- Center for Integrative Genomics, University of Lausanne, CH-1015, Lausanne, Switzerland
| | - Nicolas Loiseau
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Laurence Gamet-Payrastre
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Hervé Guillou
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France
| | - Sandrine Ellero-Simatos
- Toxalim (Research Centre in Food Toxicology), INRAE, ENVT, INP-Purpan, UPS, Université de Toulouse, Toulouse, France.
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19
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Zheng X, Leung KS, Wong MH, Cheng L. Long non-coding RNA pairs to assist in diagnosing sepsis. BMC Genomics 2021; 22:275. [PMID: 33863291 PMCID: PMC8050902 DOI: 10.1186/s12864-021-07576-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
Background Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. Results Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi < lncRNAj in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. Conclusion Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07576-4.
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Affiliation(s)
- Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
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20
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Wang L, Chu CY, McCall MN, Slaunwhite C, Holden-Wiltse J, Corbett A, Falsey AR, Topham DJ, Caserta MT, Mariani TJ, Walsh EE, Qiu X. Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants. BMC Med Genomics 2021; 14:57. [PMID: 33632195 PMCID: PMC7908785 DOI: 10.1186/s12920-021-00913-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/19/2021] [Indexed: 02/08/2023] Open
Abstract
Background A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. Method We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1–10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). Results NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. Conclusion Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-00913-2.
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Affiliation(s)
- Lu Wang
- Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA
| | - Chin-Yi Chu
- Department of Pediatrics, University of Rochester School Medicine, Rochester, NY, USA
| | - Matthew N McCall
- Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA
| | | | - Jeanne Holden-Wiltse
- Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA
| | - Anthony Corbett
- Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA
| | - Ann R Falsey
- Department of Medicine, University of Rochester School Medicine, Rochester, NY, USA.,Department of Medicine, Rochester General Hospital, Rochester, NY, USA
| | - David J Topham
- Department of Microbiology and Immunology, University of Rochester School Medicine, Rochester, NY, USA
| | - Mary T Caserta
- Department of Pediatrics, University of Rochester School Medicine, Rochester, NY, USA
| | - Thomas J Mariani
- Department of Pediatrics, University of Rochester School Medicine, Rochester, NY, USA.
| | - Edward E Walsh
- Department of Medicine, University of Rochester School Medicine, Rochester, NY, USA. .,Department of Medicine, Rochester General Hospital, Rochester, NY, USA.
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA.
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21
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Cheng L, Zeng Y, Hu S, Zhang N, Cheung KCP, Li B, Leung KS, Jiang L. Systematic prediction of autophagy-related proteins using Arabidopsis thaliana interactome data. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 105:708-720. [PMID: 33128829 DOI: 10.1111/tpj.15065] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/09/2020] [Accepted: 10/21/2020] [Indexed: 06/11/2023]
Abstract
Autophagy is a self-degradative process that is crucial for maintaining cellular homeostasis by removing damaged cytoplasmic components and recycling nutrients. Such an evolutionary conserved proteolysis process is regulated by the autophagy-related (Atg) proteins. The incomplete understanding of plant autophagy proteome and the importance of a proteome-wide understanding of the autophagy pathway prompted us to predict Atg proteins and regulators in Arabidopsis. Here, we developed a systems-level algorithm to identify autophagy-related modules (ARMs) based on protein subcellular localization, protein-protein interactions, and known Atg proteins. This generates a detailed landscape of the autophagic modules in Arabidopsis. We found that the newly identified genes in each ARM tend to be upregulated and coexpressed during the senescence stage of Arabidopsis. We also demonstrated that the Golgi apparatus ARM, ARM13, functions in the autophagy process by module clustering and functional analysis. To verify the in silico analysis, the Atg candidates in ARM13 that are functionally similar to the core Atg proteins were selected for experimental validation. Interestingly, two of the previously uncharacterized proteins identified from the ARM analysis, AGD1 and Sec14, exhibited bona fide association with the autophagy protein complex in plant cells, which provides evidence for a cross-talk between intracellular pathways and autophagy. Thus, the computational framework has facilitated the identification and characterization of plant-specific autophagy-related proteins and novel autophagy proteins/regulators in higher eukaryotes.
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Affiliation(s)
- Lixin Cheng
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Yonglun Zeng
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Shuai Hu
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Ning Zhang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Kenneth C P Cheung
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Baiying Li
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Liwen Jiang
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen, China
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22
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Nan CC, Zhang N, Cheung KCP, Zhang HD, Li W, Hong CY, Chen HS, Liu XY, Li N, Cheng L. Knockdown of lncRNA MALAT1 Alleviates LPS-Induced Acute Lung Injury via Inhibiting Apoptosis Through the miR-194-5p/FOXP2 Axis. Front Cell Dev Biol 2020; 8:586869. [PMID: 33117815 PMCID: PMC7575725 DOI: 10.3389/fcell.2020.586869] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 09/02/2020] [Indexed: 01/07/2023] Open
Abstract
Purpose We aimed to identify and verify the key genes and lncRNAs associated with acute lung injury (ALI) and explore the pathogenesis of ALI. Research showed that lower expression of the lncRNA metastasis-associated lung carcinoma transcript 1 (MALAT1) alleviates lung injury induced by lipopolysaccharide (LPS). Nevertheless, the mechanisms of MALAT1 on cellular apoptosis remain unclear in LPS-stimulated ALI. We investigated the mechanism of MALAT1 in modulating the apoptosis of LPS-induced human pulmonary alveolar epithelial cells (HPAEpiC). Methods Differentially expressed lncRNAs between the ALI samples and normal controls were identified using gene expression profiles. ALI-related genes were determined by the overlap of differentially expressed genes (DEGs), genes correlated with lung, genes correlated with key lncRNAs, and genes sharing significantly high proportions of microRNA targets with MALAT1. Quantitative real-time PCR (qPCR) was applied to detect the expression of MALAT1, microRNA (miR)-194-5p, and forkhead box P2 (FOXP2) mRNA in 1 μg/ml LPS-treated HPAEpiC. MALAT1 knockdown vectors, miR-194-5p inhibitors, and ov-FOXP2 were constructed and used to transfect HPAEpiC. The influence of MALAT1 knockdown on LPS-induced HPAEpiC proliferation and apoptosis via the miR-194-5p/FOXP2 axis was determined using Cell counting kit-8 (CCK-8) assay, flow cytometry, and Western blotting analysis, respectively. The interactions between MALAT1, miR-194-5p, and FOXP2 were verified using dual-luciferase reporter gene assay. Results We identified a key lncRNA (MALAT1) and three key genes (EYA1, WNT5A, and FOXP2) that are closely correlated with the pathogenesis of ALI. LPS stimulation promoted MALAT1 expression and apoptosis and also inhibited HPAEpiC viability. MALAT1 knockdown significantly improved viability and suppressed the apoptosis of LPS-stimulated HPAEpiC. Moreover, MALAT1 directly targeted miR-194-5p, a downregulated miRNA in LPS-stimulated HPAEpiC, when FOXP2 was overexpressed. MALAT1 knockdown led to the overexpression of miR-194-5p and restrained FOXP2 expression. Furthermore, inhibition of miR-194-5p exerted a rescue effect on MALAT1 knockdown of FOXP2, whereas the overexpression of FOXP2 reversed the effect of MALAT1 knockdown on viability and apoptosis of LPS-stimulated HPAEpiC. Conclusion Our results demonstrated that MALAT1 knockdown alleviated HPAEpiC apoptosis by competitively binding to miR-194-5p and then elevating the inhibitory effect on its target FOXP2. These data provide a novel insight into the role of MALAT1 in the progression of ALI and potential diagnostic and therapeutic strategies for ALI patients.
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Affiliation(s)
- Chuan-Chuan Nan
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Ning Zhang
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.,Department of Stomatology Center, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Kenneth C P Cheung
- School of Life Sciences, The Chinese University of Hong Kong, Sha Tin, China
| | - Hua-Dong Zhang
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Wei Li
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Cheng-Ying Hong
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Huai-Sheng Chen
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Xue-Yan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Nan Li
- Department of Stomatology Center, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
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23
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Yin R, Liu X, Yu J, Ji Y, Liu J, Cheng L, Zhou J. Up-regulation of autophagy by low concentration of salicylic acid delays methyl jasmonate-induced leaf senescence. Sci Rep 2020; 10:11472. [PMID: 32651431 PMCID: PMC7351724 DOI: 10.1038/s41598-020-68484-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 06/25/2020] [Indexed: 11/09/2022] Open
Abstract
Crosstalk between salicylic acid (SA) and jasmonic acid (JA) signaling plays an important role in regulation of plant senescence. Our previous work found that SA could delay methyl jasmonate (MeJA)-induced leaf senescence in a concentration-dependent manner. Here, the effect of low concentration of SA (LCSA) application on MeJA-induced leaf senescence was further assessed. High-throughput sequencing (RNA-Seq) results showed that LCSA did not have dominant effects on the genetic regulatory pathways of basal metabolism like nitrogen metabolism, photosynthesis and glycolysis. The ClusterONE was applied to identify discrete gene modules based on protein-protein interaction (PPI) network. Interestingly, an autophagy-related (ATG) module was identified in the differentially expressed genes (DEGs) that exclusively induced by MeJA together with LCSA. RT-qPCR confirmed that the expression of most of the determined ATG genes were upregulated by LCSA. Remarkably, in contrast to wild type (Col-0), LCSA cannot alleviate the leaf yellowing phenotype in autophagy defective mutants (atg5-1 and atg7-2) upon MeJA treatment. Confocal results showed that LCSA increased the number of autophagic bodies accumulated in the vacuole during MeJA-induced leaf senescence. Collectively, our work revealed up-regulation of autophagy by LCSA as a key regulator to alleviate MeJA-induced leaf senescence.
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Affiliation(s)
- Runzhu Yin
- MOE Key Laboratory of Laser Life Science and Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China
| | - Xueyan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China
| | - Jingfang Yu
- MOE Key Laboratory of Laser Life Science and Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China
| | - Yingbin Ji
- MOE Key Laboratory of Laser Life Science and Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China
| | - Jian Liu
- Fujian Provincial Key Laboratory of Plant Functional Biology, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
| | - Jun Zhou
- MOE Key Laboratory of Laser Life Science and Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China.
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24
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Liu X, Zheng X, Wang J, Zhang N, Leung KS, Ye X, Cheng L. A long non-coding RNA signature for diagnostic prediction of sepsis upon ICU admission. Clin Transl Med 2020; 10:e123. [PMID: 32614495 PMCID: PMC7418814 DOI: 10.1002/ctm2.123] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 12/23/2022] Open
Affiliation(s)
- Xueyan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Jun Wang
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Ning Zhang
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Xiufeng Ye
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.,Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
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25
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Cheng L, Nan C, Kang L, Zhang N, Liu S, Chen H, Hong C, Chen Y, Liang Z, Liu X. Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis. J Transl Med 2020; 18:217. [PMID: 32471511 PMCID: PMC7257169 DOI: 10.1186/s12967-020-02372-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/12/2020] [Indexed: 12/21/2022] Open
Abstract
Background Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited. Methods We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules. Results Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs. Conclusion The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.
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Affiliation(s)
- Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Chuanchuan Nan
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Lin Kang
- Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Ning Zhang
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Sheng Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Huaisheng Chen
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Chengying Hong
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Youlian Chen
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Zhen Liang
- Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China.
| | - Xueyan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China.
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26
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Mezencev R, Auerbach SS. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. PLoS One 2020; 15:e0232955. [PMID: 32413060 PMCID: PMC7228135 DOI: 10.1371/journal.pone.0232955] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 04/25/2020] [Indexed: 11/25/2022] Open
Abstract
Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.
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Affiliation(s)
- Roman Mezencev
- Center for Public Health and Environmental Assessment, Office of Research and Development, US EPA, Washington DC, United States of America
| | - Scott S. Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, Durham, NC, United States of America
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27
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Liu X, Xu Y, Wang R, Liu S, Wang J, Luo Y, Leung KS, Cheng L. A network-based algorithm for the identification of moonlighting noncoding RNAs and its application in sepsis. Brief Bioinform 2020; 22:581-588. [PMID: 32003790 DOI: 10.1093/bib/bbz154] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/26/2019] [Accepted: 11/01/2019] [Indexed: 12/26/2022] Open
Abstract
Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA-module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs.
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Affiliation(s)
- Xueyan Liu
- Critical Care Medici at Shenzhen People's Hospital
| | | | - Ran Wang
- Computer Science at The Chinese University of Hong Kong
| | | | | | | | - Kwong-Sak Leung
- Computer Science at the Chinese University of Hong Kong, Hong Kong, China
| | - Lixin Cheng
- Bioinformatics at Shenzhen People's Hospital, China
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28
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Čuklina J, Pedrioli PGA, Aebersold R. Review of Batch Effects Prevention, Diagnostics, and Correction Approaches. Methods Mol Biol 2020; 2051:373-387. [PMID: 31552638 DOI: 10.1007/978-1-4939-9744-2_16] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Systematic technical variation in high-throughput studies consisting of the serial measurement of large sample cohorts is known as batch effects. Batch effects reduce the sensitivity of biological signal extraction and can cause significant artifacts. The systematic bias in the data caused by batch effects is more common in studies in which logistical considerations restrict the number of samples that can be prepared or profiled in a single experiment, thus necessitating the arrangement of subsets of study samples in batches. To mitigate the negative impact of batch effects, statistical approaches for batch correction are used at the stage of experimental design and data processing. Whereas in genomics batch effects and possible remedies have been extensively discussed, they are a relatively new challenge in proteomics because methods with sufficient throughput to systematically measure through large sample cohorts have only recently become available. Here we provide general recommendations to mitigate batch effects: we discuss the design of large-scale proteomic studies, review the most commonly used tools for batch effect correction and overview their application in proteomics.
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Affiliation(s)
- Jelena Čuklina
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, Zürich, Switzerland
| | - Patrick G A Pedrioli
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- ETH Zürich, PHRT-MS, Zürich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
- Faculty of Science, University of Zürich, Zürich, Switzerland.
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29
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Liu X, Li N, Liu S, Wang J, Zhang N, Zheng X, Leung KS, Cheng L. Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review. Front Bioeng Biotechnol 2019; 7:358. [PMID: 32039167 PMCID: PMC6988798 DOI: 10.3389/fbioe.2019.00358] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 11/11/2019] [Indexed: 12/15/2022] Open
Abstract
Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.
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Affiliation(s)
- Xueyan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Nan Li
- Department of Stomatology Center, Shenzhen People's Hospital, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Sheng Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Jun Wang
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Ning Zhang
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Xubin Zheng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Lixin Cheng
- Department of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medicine College of Jinan University, Shenzhen, China
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30
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Barretto SA, Lasserre F, Fougerat A, Smith L, Fougeray T, Lukowicz C, Polizzi A, Smati S, Régnier M, Naylies C, Bétoulières C, Lippi Y, Guillou H, Loiseau N, Gamet-Payrastre L, Mselli-Lakhal L, Ellero-Simatos S. Gene Expression Profiling Reveals that PXR Activation Inhibits Hepatic PPARα Activity and Decreases FGF21 Secretion in Male C57Bl6/J Mice. Int J Mol Sci 2019; 20:ijms20153767. [PMID: 31374856 PMCID: PMC6696478 DOI: 10.3390/ijms20153767] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/29/2019] [Accepted: 07/31/2019] [Indexed: 01/25/2023] Open
Abstract
The pregnane X receptor (PXR) is the main nuclear receptor regulating the expression of xenobiotic-metabolizing enzymes and is highly expressed in the liver and intestine. Recent studies have highlighted its additional role in lipid homeostasis, however, the mechanisms of these regulations are not fully elucidated. We investigated the transcriptomic signature of PXR activation in the liver of adult wild-type vs. Pxr-/- C57Bl6/J male mice treated with the rodent specific ligand pregnenolone 16α-carbonitrile (PCN). PXR activation increased liver triglyceride accumulation and significantly regulated the expression of 1215 genes, mostly xenobiotic-metabolizing enzymes. Among the down-regulated genes, we identified a strong peroxisome proliferator-activated receptor α (PPARα) signature. Comparison of this signature with a list of fasting-induced PPARα target genes confirmed that PXR activation decreased the expression of more than 25 PPARα target genes, among which was the hepatokine fibroblast growth factor 21 (Fgf21). PXR activation abolished plasmatic levels of FGF21. We provide a comprehensive signature of PXR activation in the liver and identify new PXR target genes that might be involved in the steatogenic effect of PXR. Moreover, we show that PXR activation down-regulates hepatic PPARα activity and FGF21 circulation, which could participate in the pleiotropic role of PXR in energy homeostasis.
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Affiliation(s)
- Sharon Ann Barretto
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Frédéric Lasserre
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Anne Fougerat
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Lorraine Smith
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Tiffany Fougeray
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Céline Lukowicz
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Arnaud Polizzi
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Sarra Smati
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Marion Régnier
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Claire Naylies
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Colette Bétoulières
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Yannick Lippi
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Hervé Guillou
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Nicolas Loiseau
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Laurence Gamet-Payrastre
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Laila Mselli-Lakhal
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France
| | - Sandrine Ellero-Simatos
- Institut National de la Recherche Agronomique (INRA), UMR1331 Toxalim, F31-027 Toulouse CEDEX 3, France.
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Cheng L, Liu P, Wang D, Leung KS. Exploiting locational and topological overlap model to identify modules in protein interaction networks. BMC Bioinformatics 2019; 20:23. [PMID: 30642247 PMCID: PMC6332531 DOI: 10.1186/s12859-019-2598-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 01/03/2019] [Indexed: 12/27/2022] Open
Abstract
Background Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells. Results In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL. Conclusion Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks. Electronic supplementary material The online version of this article (10.1186/s12859-019-2598-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lixin Cheng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. .,Institute of translation medicine, Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University, Shenzhen, China.
| | - Pengfei Liu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
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Larriba Y, Rueda C, Fernández MA, Peddada SD. Microarray Data Normalization and Robust Detection of Rhythmic Features. Methods Mol Biol 2019; 1986:207-225. [PMID: 31115890 DOI: 10.1007/978-1-4939-9442-7_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.
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Affiliation(s)
- Yolanda Larriba
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain.
| | - Cristina Rueda
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain
| | - Miguel A Fernández
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain
| | - Shyamal D Peddada
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
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Cheng L, Liu P, Leung K. SMILE: a novel procedure for subcellular module identification with localisation expansion. IET Syst Biol 2018. [PMID: 29533218 PMCID: PMC8687326 DOI: 10.1049/iet-syb.2017.0085] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Computational clustering methods help identify functional modules in protein–protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors’ results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.
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Affiliation(s)
- Lixin Cheng
- Department of Computer Science & EngineeringChinese University of Hong KongMa Liu ShuiHong Kong
| | - Pengfei Liu
- Department of Computer Science & EngineeringChinese University of Hong KongMa Liu ShuiHong Kong
| | - Kwong‐Sak Leung
- Department of Computer Science & EngineeringChinese University of Hong KongMa Liu ShuiHong Kong
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Cheng L, Leung KS. Quantification of non-coding RNA target localization diversity and its application in cancers. J Mol Cell Biol 2018; 10:130-138. [DOI: 10.1093/jmcb/mjy006] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Accepted: 01/24/2018] [Indexed: 12/13/2022] Open
Affiliation(s)
- Lixin Cheng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
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Larriba Y, Rueda C, Fernández MA, Peddada SD. A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data. Front Genet 2018; 9:24. [PMID: 29456555 PMCID: PMC5801422 DOI: 10.3389/fgene.2018.00024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 01/17/2018] [Indexed: 01/01/2023] Open
Abstract
Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html
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Affiliation(s)
- Yolanda Larriba
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain
| | - Cristina Rueda
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain
| | - Miguel A Fernández
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Valladolid, Spain
| | - Shyamal D Peddada
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States.,Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
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Variation-preserving normalization unveils blind spots in gene expression profiling. Sci Rep 2017; 7:42460. [PMID: 28276435 PMCID: PMC5343588 DOI: 10.1038/srep42460] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 01/11/2017] [Indexed: 11/17/2022] Open
Abstract
RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.
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Cheng L, Wang X, Wong PK, Lee KY, Li L, Xu B, Wang D, Leung KS. ICN: a normalization method for gene expression data considering the over-expression of informative genes. MOLECULAR BIOSYSTEMS 2016; 12:3057-66. [DOI: 10.1039/c6mb00386a] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The global increase of gene expression has been frequently established in cancer microarray studies.
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Affiliation(s)
- Lixin Cheng
- Department of Computer Science and Engineering
- The Chinese University of Hong Kong
- New Territories
- China
| | - Xuan Wang
- College of Pharmacy
- Harbin Medical University
- Harbin
- China
| | - Pak-Kan Wong
- Department of Computer Science and Engineering
- The Chinese University of Hong Kong
- New Territories
- China
| | - Kwan-Yeung Lee
- Department of Computer Science and Engineering
- The Chinese University of Hong Kong
- New Territories
- China
| | - Le Li
- Department of Computer Science and Engineering
- The Chinese University of Hong Kong
- New Territories
- China
| | - Bin Xu
- School of Internet of Things
- Nanjing University of Posts and Telecommunications
- Nanjing
- China
| | - Dong Wang
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin
- China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering
- The Chinese University of Hong Kong
- New Territories
- China
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