1
|
Chen YJ, Ferdousi F, Bejaoui M, Sasaki K, Isoda H. Microarray meta-analysis reveals comprehensive effects of 3,4,5-tricaffeolyquinic acid in cell differentiation and signaling. Eur J Pharmacol 2023; 960:176143. [PMID: 37866748 DOI: 10.1016/j.ejphar.2023.176143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/14/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023]
Abstract
Caffeoylquinic acids (CQA) are polyphenolic compounds found in fruits, vegetables, coffee, and spices that have exhibited several beneficial activities, including antioxidant, antibacterial, neuroprotective, anti-inflammatory, anticancer, antiviral, antidiabetic, and cardiovascular effects. A derivative, TCQA (3,4,5-Tri-O-caffeoylquinic acid), has also shown both neurogenic and pigment differentiation potential. A transcriptomic-based meta-analysis was conducted to explore potential biochemical processes and molecular targets of TCQA. This approach involved integrating data from various cell and tissue types, including human amniotic stem cells, human neural stem cells, human dermal papilla cells, and the brain cortex of aging model mice. It offered a comprehensive perspective on the significant gene regulations in response to TCQA treatment. The objective was to uncover the mechanism and novel targets of TCQA, facilitating a further understanding of its functions. New areas of interest found were TCQA's effect on adipogenesis, heart, and muscle tissue development. In addition, significantly enhanced biological activities found through meta-analysis included cell cycle, VEGFA-VEGFR2 pathway, and BMP signaling. Overall, a comprehensive functional and visual analysis using available biological databases uncovered the multi-target potential of this natural compound.
Collapse
Affiliation(s)
- Yu Jia Chen
- Tsukuba Life Science Innovation Program (T-LSI), Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan; Open Innovation Laboratory for Food and Medicinal Resource Engineering (FoodMed-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8577, Japan
| | - Farhana Ferdousi
- Tsukuba Life Science Innovation Program (T-LSI), Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan; Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan; Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
| | - Meriem Bejaoui
- Open Innovation Laboratory for Food and Medicinal Resource Engineering (FoodMed-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8577, Japan; Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
| | - Kazunori Sasaki
- Open Innovation Laboratory for Food and Medicinal Resource Engineering (FoodMed-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8577, Japan; Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan
| | - Hiroko Isoda
- Tsukuba Life Science Innovation Program (T-LSI), Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan; Open Innovation Laboratory for Food and Medicinal Resource Engineering (FoodMed-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8577, Japan; Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan; Alliance for Research on the Mediterranean and North Africa (ARENA), University of Tsukuba, Tsukuba, Ibaraki, 305-8572, Japan.
| |
Collapse
|
2
|
Haldar A, Oza VH, DeVoss NS, Clark AD, Lasseigne BN. CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis. Bioinformatics 2023; 39:btad759. [PMID: 38109675 PMCID: PMC10749757 DOI: 10.1093/bioinformatics/btad759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/30/2023] [Accepted: 12/16/2023] [Indexed: 12/20/2023] Open
Abstract
SUMMARY High-throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), a Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics. AVAILABILITY AND IMPLEMENTATION https://github.com/lasseignelab/CoSIA.
Collapse
Affiliation(s)
- Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Vishal H Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Nathaniel S DeVoss
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Amanda D Clark
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Brittany N Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| |
Collapse
|
3
|
Haldar A, Oza VH, DeVoss NS, Clark AD, Lasseigne BN. CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537877. [PMID: 37163017 PMCID: PMC10168259 DOI: 10.1101/2023.04.21.537877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.
Collapse
Affiliation(s)
- Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Vishal H. Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Nathaniel S. DeVoss
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Amanda D. Clark
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Brittany N. Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| |
Collapse
|
4
|
Pauza AG, Murphy D, Paton JFR. Transcriptomics of the Carotid Body. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1427:1-11. [PMID: 37322330 DOI: 10.1007/978-3-031-32371-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The carotid body (CB) has emerged as a potential therapeutic target for treating sympathetically mediated cardiovascular, respiratory, and metabolic diseases. In adjunct to its classical role as an arterial O2 sensor, the CB is a multimodal sensor activated by a range of stimuli in the circulation. However, consensus on how CB multimodality is achieved is lacking; even the best studied O2-sensing appears to involve multiple convergent mechanisms. A strategy to understand multimodal sensing is to adopt a hypothesis-free, high-throughput transcriptomic approach. This has proven instrumental for understanding fundamental mechanisms of CB response to hypoxia and other stimulants, its developmental niche, cellular heterogeneity, laterality, and pathophysiological remodeling in disease states. Herein, we review this published work that reveals novel molecular mechanisms underpinning multimodal sensing and reveals numerous gaps in knowledge that require experimental testing.
Collapse
Affiliation(s)
- Audrys G Pauza
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand.
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Julian F R Paton
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand
| |
Collapse
|
5
|
Goutzelas Y, Kontou P, Mamuris Z, Bagos P, Sarafidou T. Meta-analysis of gene expression data in adipose tissue reveals new obesity associated genes. Gene 2022; 818:146223. [PMID: 35063573 DOI: 10.1016/j.gene.2022.146223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/28/2021] [Accepted: 01/13/2022] [Indexed: 01/16/2023]
Abstract
High-throughput transcriptomic and proteomic data like microarray data are deposited in public databases such as Gene Expression Omnibus (GEO). Omics data integration and processing from different and independent studies is achieved by using efficient and effective computational tools through meta-analysis. Meta-analysis is a statistical powerful tool combining data from numerous studies, minimizes bias and increases statistical power by increasing sample size compared to individual studies. Therefore, we performed a meta-analysis of gene expression data in adipose tissue to identify genes that are differentially expressed between obese and non-obese subjects as well as to detect gene expression signatures, pathways and networks associated with obesity. We identified 821 differentially expressed genes (DEGs) in adipose tissue of obese subjects compared to non-obese. A protein-protein interactions (PPIs) network was reconstructed consisting of 168 proteins. Functional enrichment analysis in the network revealed proteins involved in RNA and energy metabolism. The KEGG pathway analysis revealed 15 enriched pathway terms. Furthermore, multiple testing correction methods identified five statistically significant obesity associated genes (NDUFA12, SFI1, SSB, FAR2 and LACE1) that require further investigation.
Collapse
Affiliation(s)
- Yiannis Goutzelas
- Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Panagiota Kontou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Zissis Mamuris
- Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Pantelis Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Theologia Sarafidou
- Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece.
| |
Collapse
|
6
|
Shamloo-Dashtpagerdi R, Aliakbari M, Lindlöf A, Tahmasebi S. A systems biology study unveils the association between a melatonin biosynthesis gene, O-methyl transferase 1 (OMT1) and wheat (Triticum aestivum L.) combined drought and salinity stress tolerance. PLANTA 2022; 255:99. [PMID: 35386021 DOI: 10.1007/s00425-022-03885-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
Enhanced levels of endogenous melatonin in the root of wheat, mainly through the OMT1 gene, augment the antioxidant system, reestablish redox homeostasis and are associated with combined stress tolerance. A systems biology approach, including a collection of computational analyses and experimental assays, led us to uncover some aspects of a poorly understood phenomenon, namely wheat (Triticum aestivum L.) combined drought and salinity stress tolerance. Accordingly, a cross-study comparison of stress experiments was performed via a meta-analysis of Expressed Sequence Tags (ESTs) data from wheat roots to uncover the overlapping gene network of drought and salinity stresses. Identified differentially expressed genes were functionally annotated by gene ontology enrichment analysis and gene network analysis. Among those genes, O-methyl transferase 1 (OMT1) was highlighted as a more important (hub) gene in the dual-stress response gene network. Afterwards, the potential roles of OMT1 in mediating physiochemical indicators of stress tolerance were investigated in two wheat genotypes differing in abiotic stress tolerance. Regression analysis and correspondence analysis (CA) confirmed that the expression profiles of the OMT1 gene and variations in melatonin content, antioxidant enzyme activities, proline accumulation, H2O2 and malondialdehyde (MDA) contents are significantly associated with combined stress tolerance. These results reveal that the OMT1 gene may contribute to wheat combined drought and salinity stress tolerance through augmenting the antioxidant system and re-establishing redox homeostasis, probably via the regulation of melatonin biosynthesis as a master regulator molecule. Our findings provide new insights into the roles of melatonin in wheat combined drought and salinity stress tolerance and suggest a novel plausible regulatory node through the OMT1 gene to improve multiple-stress tolerant crops.
Collapse
Affiliation(s)
| | - Massume Aliakbari
- Department of Crop Production and Plant Breeding, Shiraz University, Shiraz, Iran
| | | | - Sirus Tahmasebi
- Seed and Plant Improvement Research Department, Fars Agricultural and Natural Resources Research and Education Center, AREEO, Shiraz, Iran
| |
Collapse
|
7
|
Makinde FL, Tchamga MSS, Jafali J, Fatumo S, Chimusa ER, Mulder N, Mazandu GK. Reviewing and assessing existing meta-analysis models and tools. Brief Bioinform 2021; 22:bbab324. [PMID: 34415019 PMCID: PMC8575034 DOI: 10.1093/bib/bbab324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/07/2021] [Accepted: 07/25/2021] [Indexed: 01/03/2023] Open
Abstract
Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.
Collapse
Affiliation(s)
- Funmilayo L Makinde
- Computational Biology Division at University of Cape Town in collaboration with the African Institute for Mathematical Sciences (AIMS), South Africa
| | - Milaine S S Tchamga
- Division of Human Genetics at University of Cape in collaboration with the African Institute for Mathematical Sciences (AIMS), South Africa
| | - James Jafali
- Pathogen Biology Research Group, Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Malawi
| | - Segun Fatumo
- London School of Hygiene and Tropical Medicine, University of London, UK
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Division at University of Cape Town, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology at University of Cape Town, and Associate Researcher at the African Institute for Mathematical Sciences (AIMS), South Africa
| |
Collapse
|
8
|
Liu L, Wang T, Huang D, Song D. Comprehensive Analysis of Differentially Expressed Genes in Clinically Diagnosed Irreversible Pulpitis by Multiplatform Data Integration Using a Robust Rank Aggregation Approach. J Endod 2021; 47:1365-1375. [PMID: 34260959 DOI: 10.1016/j.joen.2021.07.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/24/2021] [Accepted: 07/01/2021] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Molecular diagnosis may overcome the limitations of clinical and histologic diagnosis in pulpitis, thereby benefiting many treatment techniques, such as vital pulp therapies. In this study, integrated microarray data on pulpitis were used to obtain a list of normalized differentially expressed (DE) genes for analyzing the molecular mechanisms underlying pulpitis and identifying potential diagnostic biomarkers. METHODS A systematic search of public microarray and sequencing databases was performed to obtain expression data of pulpitis. Robust rank aggregation (RRA) was used to obtain DE gene lists (RRA_DEmRNAs and RRA_DElncRNAs) between inflamed pulp and normal samples. DE genes were evaluated by functional enrichment analyses, correlation analyses for inflammation-related RRA_DEmRNAs, and protein-protein interaction and competing endogenous RNA network construction. Quantitative real-time polymerase chain reaction validation was applied in snap-frozen pulp tissues. RESULTS Using the GSE77459 and GSE92681 data sets, 280 RRA_DEmRNAs and 90 RRA_DElncRNAs were identified. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes and osteoclast differentiation and tumor necrosis factor, chemokine, and B-cell receptor signaling pathways. The molecular complex detection and cytoHubba methods identified 2 clusters and 10 hub genes in the protein-protein interaction network. The competing endogenous RNA network was composed of 2 long noncoding RNAs (ADAMTS9-AS2 and LINC00290), 2 microRNAs (hsa-miR-30a-5p and hsa-miR-128-3p), and 3 messenger RNAs (ABCA1, FBLN5, and SOCS3). The expression between most top inflammation-related RRA_DEmRNAs in pulpitis showed positive correlations. Quantitative real-time polymerase chain reacation validated the expression trends of selected genes, including ITGAX, TREM1, CD86, FCGR2A, ADAMTS9-AS2, LINC00290, hsa-miR-30a-5p, hsa-miR-128-3p, RASGRP3, IL3RA, CCDC178, CRISPLD1, LINC01857, AC007991.2, ARHGEF26-AS1, and AL021408.1. CONCLUSIONS The identified biomarkers provide insight into the pathology and may aid in the molecular diagnosis of pulpitis.
Collapse
Affiliation(s)
- Liu Liu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China; Department of Conservative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Tianyi Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dingming Huang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China; Department of Conservative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dongzhe Song
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China; Department of Conservative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| |
Collapse
|
9
|
Wang L, Mo C, Wang L, Cheng M. Identification of genes and pathways related to breast cancer metastasis in an integrated cohort. Eur J Clin Invest 2021; 51:e13525. [PMID: 33615456 DOI: 10.1111/eci.13525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 01/20/2021] [Accepted: 02/18/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results. MATERIALS AND METHODS Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis. RESULTS A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database. CONCLUSIONS Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.
Collapse
Affiliation(s)
- Lingchen Wang
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Biostatistics, School of Public Health, Nanchang University, Nanchang, China
| | - Changgan Mo
- Department of Cardiology, The People's Hospital of Hechi, Hechi, China
| | - Liqin Wang
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Minzhang Cheng
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Jiangxi Key Laboratory of Molecular Diagnostics and Precision Medicine, Nanchang, China
| |
Collapse
|
10
|
Aliakbari M, Cohen SP, Lindlöf A, Shamloo-Dashtpagerdi R. Rubisco activase A (RcaA) is a central node in overlapping gene network of drought and salinity in Barley (Hordeum vulgare L.) and may contribute to combined stress tolerance. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2021; 161:248-258. [PMID: 33652257 DOI: 10.1016/j.plaphy.2021.02.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Co-occurrence of abiotic stresses, especially drought and salinity, is a natural phenomenon in field conditions and is worse for crop production than any single stress. Nowadays, rigorous methods of meta-analysis and systems biology have made it possible to perform cross-study comparisons of single stress experiments, which can uncover main overlapping mechanisms underlying tolerance to combined stress. In this study, a meta-analysis of RNA-Seq data was conducted to obtain the overlapping gene network of drought and salinity stresses in barley (Hordeum vulgare L.), which identified Rubisco activase A (RcaA) as a hub gene in the dual-stress response. Thereafter, a greenhouse experiment was carried out using two barley genotypes with different abiotic stress tolerance and evaluated several physiochemical properties as well as the expression profile and protein activity of RcaA. Finally, machine learning analysis was applied to uncover relationships among combined stress tolerance and evaluated properties. We identified 441 genes which were differentially expressed under both drought and salinity stress. Results revealed that the photosynthesis pathway and, in particular, the RcaA gene are major components of the dual-stress responsive transcriptome. Comparative physiochemical and molecular evaluations further confirmed that enhanced photosynthesis capability, mainly through regulation of RcaA expression and activity as well as accumulation of proline content, have a significant association with combined drought and salinity stress tolerance in barley. Overall, our results clarify the importance of RcaA in combined stress tolerance and may provide new insights for future investigations.
Collapse
Affiliation(s)
- Massume Aliakbari
- Department of Crop Production and Plant Breeding, Shiraz University, Shiraz, Iran
| | - Stephen P Cohen
- Department of Plant Pathology, The Ohio State University, OH, 43210, USA
| | | | | |
Collapse
|
11
|
Hendrickx DM, Glaab E. Comparative transcriptome analysis of Parkinson's disease and Hutchinson-Gilford progeria syndrome reveals shared susceptible cellular network processes. BMC Med Genomics 2020; 13:114. [PMID: 32811487 PMCID: PMC7437934 DOI: 10.1186/s12920-020-00761-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/04/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Parkinson's Disease (PD) and Hutchinson-Gilford Progeria Syndrome (HGPS) are two heterogeneous disorders, which both display molecular and clinical alterations associated with the aging process. However, similarities and differences between molecular changes in these two disorders have not yet been investigated systematically at the level of individual biomolecules and shared molecular network alterations. METHODS Here, we perform a comparative meta-analysis and network analysis of human transcriptomics data from case-control studies for both diseases to investigate common susceptibility genes and sub-networks in PD and HGPS. Alzheimer's disease (AD) and primary melanoma (PM) were included as controls to confirm that the identified overlapping susceptibility genes for PD and HGPS are non-generic. RESULTS We find statistically significant, overlapping genes and cellular processes with significant alterations in both diseases. Interestingly, the majority of these shared affected genes display changes with opposite directionality, indicating that shared susceptible cellular processes undergo different mechanistic changes in PD and HGPS. A complementary regulatory network analysis also reveals that the altered genes in PD and HGPS both contain targets controlled by the upstream regulator CDC5L. CONCLUSIONS Overall, our analyses reveal a significant overlap of affected cellular processes and molecular sub-networks in PD and HGPS, including changes in aging-related processes that may reflect key susceptibility factors associated with age-related risk for PD.
Collapse
Affiliation(s)
- Diana M. Hendrickx
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, Belvaux, L- 4367 Luxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, Belvaux, L- 4367 Luxembourg
| |
Collapse
|
12
|
Likhitrattanapisal S, Kumkate S, Ajawatanawong P, Wongprasert K, Tohtong R, Janvilisri T. Dysregulation of microRNA in cholangiocarcinoma identified through a meta-analysis of microRNA profiling. World J Gastroenterol 2020; 26:4356-4371. [PMID: 32848339 PMCID: PMC7422534 DOI: 10.3748/wjg.v26.i29.4356] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 05/16/2020] [Accepted: 07/21/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the past decades, the potential of microRNA (miRNA) in cancer diagnostics and prognostics has gained a lot of interests. In this study, a meta-analysis was conducted upon the pooled miRNA microarray data of cholangiocarcinoma (CCA).
AIM To identify differentially expressed (DE) miRNAs and perform functional analyses in order to gain insights to understanding miRNA-target interactions involved in tumorigenesis pathways of CCA.
METHODS Raw data from 8 CCA miRNA microarray datasets, consisting of 443 samples in total, were integrated and statistically analyzed to identify DE miRNAs via comparison of levels of miRNA expression between CCA and normal bile duct samples using t-tests (P < 0.001). The 10-fold cross validation was performed in order to increase the robustness of the t-test results.
RESULTS Our data showed 70 up-regulated and 48 down-regulated miRNAs in CCA. Gene Ontology and pathway enrichment analyses revealed that mRNA targets of DE miRNAs were significantly involved in several biological processes. The most prominent dysregulated pathways included phosphatidylinositol-3 kinases/Akt, mitogen-activated protein kinase and Ras signaling pathways.
CONCLUSION DE miRNAs found in our meta-analysis revealed dysregulation in major cancer pathways involved in the development of CCA. These results indicated the necessity of understanding the miRNA-target interactions and the significance of dysregulated miRNAs in terms of diagnostics and prognostics of cancers.
Collapse
Affiliation(s)
- Somsak Likhitrattanapisal
- National Center for Genetic Engineering and Biotechnology, Pathumthani 12120, Thailand
- Department of Biology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Supeecha Kumkate
- Department of Biology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Pravech Ajawatanawong
- Division of Bioinformatics and Data Management for Research, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Kanokpan Wongprasert
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Rutaiwan Tohtong
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Tavan Janvilisri
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| |
Collapse
|
13
|
Li A, Li A, Deng Z, Guo J, Wu H. Cross-Species Annotation of Expressed Genes and Detection of Different Functional Gene Modules Between 10 Cold- and 10 Hot-Propertied Chinese Herbal Medicines. Front Genet 2020; 11:532. [PMID: 32625232 PMCID: PMC7314971 DOI: 10.3389/fgene.2020.00532] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/04/2020] [Indexed: 12/11/2022] Open
Abstract
According to the traditional Chinese medicine (TCM) system, Chinese herbal medicines (HMs) can be divided into four categories: hot, warm, cold, and cool. A cool nature usually is categorized as a cold nature, and a warm nature is classified as a hot nature. However, the detectable characteristics of the gene expression profile associated with the cold and hot properties have not been studied. To address this question, a strategy for the cross-species annotation of conserved genes was established in the present study by using transcriptome data of 20 HMs with cold and hot properties. Functional enrichment analysis was performed on group-specific expressed genes inferred from the functional genome of the reference species (i.e., Arabidopsis). Results showed that metabolic pathways relevant to chrysoeriol, luteolin, paniculatin, and wogonin were enriched for cold-specific genes, and pathways of inositol, heptadecane, lauric acid, octanoic acid, hexadecanoic acid, and pentadecanoic acid were enriched for hot-specific genes. Six functional modules were identified in the HMs with the cold property: nucleotide biosynthetic process, peptidy-L-cysteine S-palmitoylation, lipid modification, base-excision repair, dipeptide transport, and response to endoplasmic reticulum stress. For the hot HMs, another six functional modules were identified: embryonic meristem development, embryonic pattern specification, axis specification, regulation of RNA polymerase II transcriptional preinitiation complex assembly, mitochondrial RNA modification, and cell redox homeostasis. The research provided a new insight into HMs’ cold and hot properties from the perspective of the gene expression profile of plants.
Collapse
Affiliation(s)
- Arong Li
- Guangzhou Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Pharmacy, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, China
| | - Aqian Li
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Zhijun Deng
- Department of Pharmacy, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, China
| | - Jiewen Guo
- Guangzhou Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Pharmacy, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, China
| | - Hongkai Wu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
14
|
Cheong CM, Mrozik KM, Hewett DR, Bell E, Panagopoulos V, Noll JE, Licht JD, Gronthos S, Zannettino ACW, Vandyke K. Twist-1 is upregulated by NSD2 and contributes to tumour dissemination and an epithelial-mesenchymal transition-like gene expression signature in t(4;14)-positive multiple myeloma. Cancer Lett 2020; 475:99-108. [PMID: 32014459 DOI: 10.1016/j.canlet.2020.01.040] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 01/28/2020] [Accepted: 01/29/2020] [Indexed: 12/11/2022]
Abstract
Approximately 15% of patients with multiple myeloma (MM) harbour the t(4;14) chromosomal translocation, leading to the overexpression of the histone methyltransferase NSD2. Patients with this translocation display increased tumour dissemination, accelerated disease progression and rapid relapse. Using publicly available gene expression profile data from NSD2high (n = 135) and NSD2low (n = 878) MM patients, we identified 39 epithelial-mesenchymal transition (EMT)-associated genes which are overexpressed in NSD2high MM plasma cells. In addition, our analyses identified Twist-1 as a key transcription factor upregulated in NSD2high MM patients and t(4;14)-positive cell lines. Overexpression and knockdown studies confirmed that Twist-1 is involved in driving the expression of EMT-associated genes in the human MM cell line KMS11 and promoted the migration of myeloma cell lines in vitro. Notably, Twist-1 overexpression in the mouse MM cell line 5TGM1 significantly increased tumour dissemination in an intratibial tumour model. These findings demonstrate that Twist-1, downstream of NSD2, contributes to the induction of an EMT-like signature in t(4;14)-positive MM and enhances the dissemination of MM plasma cells in vivo, which may, in part, explain the aggressive disease features associated with t(4;14)-positive MM.
Collapse
Affiliation(s)
- Chee Man Cheong
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Krzysztof M Mrozik
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Duncan R Hewett
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Elyse Bell
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Vasilios Panagopoulos
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Jacqueline E Noll
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Jonathan D Licht
- Departments of Medicine, Biochemistry and Molecular Biology and University of Florida Health Cancer Center, The University of Florida, Gainesville, FL, USA
| | - Stan Gronthos
- Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia; Mesenchymal Stem Cell Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
| | - Andrew C W Zannettino
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia
| | - Kate Vandyke
- Myeloma Research Laboratory, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia; Precision Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, Australia.
| |
Collapse
|
15
|
Nangraj AS, Selvaraj G, Kaliamurthi S, Kaushik AC, Cho WC, Wei DQ. Integrated PPI- and WGCNA-Retrieval of Hub Gene Signatures Shared Between Barrett's Esophagus and Esophageal Adenocarcinoma. Front Pharmacol 2020; 11:881. [PMID: 32903837 PMCID: PMC7438937 DOI: 10.3389/fphar.2020.00881] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 05/28/2020] [Indexed: 02/05/2023] Open
Abstract
Esophageal adenocarcinoma (EAC) is a deadly cancer with high mortality rate, especially in economically advanced countries, while Barrett's esophagus (BE) is reported to be a precursor that strongly increases the risk of EAC. Due to the complexity of these diseases, their molecular mechanisms have not been revealed clearly. This study aims to explore the gene signatures shared between BE and EAC based on integrated network analysis. We obtained EAC- and BE-associated microarray datasets GSE26886, GSE1420, GSE37200, and GSE37203 from the Gene Expression Omnibus and ArrayExpress using systematic meta-analysis. These data were accompanied by clinical data and RNAseq data from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to explore the relationship between gene sets and clinical traits as well as to discover the key relationships behind the co-expression modules. A differentially expressed gene-based protein-protein interaction (PPI) complex was used to extract hub genes through Cytoscape plugins. As a result, 403 DEGs were excavated, comprising 236 upregulated and 167 downregulated genes, which are involved in the cell cycle and replication pathways. Forty key genes were identified using modules of MCODE, CytoHubba, and CytoNCA with different algorithms. A dark-gray module with 207 genes was identified which having a high correlation with phenotype (gender) in the WGCNA. Furthermore, five shared hub gene signatures (SHGS), namely, pre-mRNA processing factor 4 (PRPF4), serine and arginine-rich splicing factor 1 (SRSF1), heterogeneous nuclear ribonucleoprotein M (HNRNPM), DExH-Box Helicase 9 (DHX9), and origin recognition complex subunit 2 (ORC2), were identified between BE and EAC. SHGS enrichment denotes that RNA metabolism and splicosomes play a key role in esophageal cancer development and progress. We conclude that the PPI complex and WGCNA co-expression network highlight the importance of phenotypic identifying hub gene signatures for BE and EAC.
Collapse
Affiliation(s)
- Asma Sindhoo Nangraj
- The State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Gurudeeban Selvaraj
- Center of Interdisciplinary Sciences-Computational Life Sciences, Henan University of Technology, Zhengzhou, China
- *Correspondence: Gurudeeban Selvaraj, ; Dong Qing Wei,
| | - Satyavani Kaliamurthi
- Center of Interdisciplinary Sciences-Computational Life Sciences, Henan University of Technology, Zhengzhou, China
| | - Aman Chandra Kaushik
- The State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - William C. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Dong Qing Wei
- The State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Center of Interdisciplinary Sciences-Computational Life Sciences, Henan University of Technology, Zhengzhou, China
- Peng Cheng Laboratory, Shenzhen, China
- *Correspondence: Gurudeeban Selvaraj, ; Dong Qing Wei,
| |
Collapse
|
16
|
Meta-analysis of gene expression profiles in preeclampsia. Pregnancy Hypertens 2020; 19:52-60. [DOI: 10.1016/j.preghy.2019.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 12/18/2019] [Indexed: 01/12/2023]
|
17
|
Cruz N, Méndez T, Ramos P, Urbina D, Vega A, Gutiérrez RA, Moya-León MA, Herrera R. Induction of PrMADS10 on the lower side of bent pine tree stems: potential role in modifying plant cell wall properties and wood anatomy. Sci Rep 2019; 9:18981. [PMID: 31831838 PMCID: PMC6908731 DOI: 10.1038/s41598-019-55276-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/07/2019] [Indexed: 12/20/2022] Open
Abstract
The molecular mechanisms underlying inclination responses in trees are unclear. In this study, we identified a MADS-box transcription factor differentially expressed early after inclination in the stems of Pinus radiata D. Don. PrMADS10 has a CDS of 582 bp and encodes a group II MADS-box transcription factor. We measured highest accumulation of this transcript on the lower side of inclined pine stems. In an effort to identify putative targets, we stably transformed Arabidopsis thaliana with a 35S::PrMADS10 construct. Transcriptome analysis revealed 1,219 genes differentially-expressed, with 690 and 529 genes up- and down-regulated respectively, when comparing the transgenic and wild-type. Differentially-expressed genes belong to different biological processes, but were enriched in cell wall remodeling and phenylpropanoid metabolic functions. Interestingly, lignin content was 30% higher in transgenic as compared to wild-type plants consistent with observed changes in gene expression. Differentially expressed transcription factors and phenylpropanoid genes were analyzed using STRING. Several MYB and NAC transcription factors showed interactions with genes of the phenylpropanoid pathway. Together, these results implicate PrMADS10 as a regulatory factor, triggering the expression of other transcription factors and genes involved in the synthesis of lignin.
Collapse
Affiliation(s)
- Nicolás Cruz
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile
- Facultad de Ciencias Ambientales, Universidad Técnica Estatal de Quevedo, Quevedo, Ecuador
| | - Tamara Méndez
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile
| | - Patricio Ramos
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile
- Núcleo Científico Multidisciplinario-DI, Universidad de Talca. Av. Lircay s/n, Talca, Chile
| | - Daniela Urbina
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile
| | - Andrea Vega
- FONDAP Center for Genome Regulation. Millennium Institute for Integrative Systems and Synthetic Biology. Departamento de Genética Molecular y Microbiología. Facultad Ciencias Biológicas, P. Universidad Católica de Chile, Alameda 340, Santiago, Chile
| | - Rodrigo A Gutiérrez
- FONDAP Center for Genome Regulation. Millennium Institute for Integrative Systems and Synthetic Biology. Departamento de Genética Molecular y Microbiología. Facultad Ciencias Biológicas, P. Universidad Católica de Chile, Alameda 340, Santiago, Chile
| | - María A Moya-León
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile
| | - Raúl Herrera
- Instituto de Ciencias Biológicas, Universidad de Talca. Av. Lircay s/n, Talca, Chile.
| |
Collapse
|
18
|
Abstract
The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.
Collapse
|
19
|
Kontou P, Pavlopoulou A, Braliou G, Bogiatzi S, Dimou N, Bangalore S, Bagos P. Identification of gene expression profiles in myocardial infarction: a systematic review and meta-analysis. BMC Med Genomics 2018; 11:109. [PMID: 30482209 PMCID: PMC6260684 DOI: 10.1186/s12920-018-0427-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 11/07/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Myocardial infarction (MI) is a multifactorial disease with complex pathogenesis, mainly the result of the interplay of genetic and environmental risk factors. The regulation of thrombosis, inflammation and cholesterol and lipid metabolism are the main factors that have been proposed thus far to be involved in the pathogenesis of MI. Traditional risk-estimation tools depend largely on conventional risk factors but there is a need for identification of novel biochemical and genetic markers. The aim of the study is to identify differentially expressed genes that are consistently associated with the incidence myocardial infarction (MI), which could be potentially incorporated into the traditional cardiovascular diseases risk factors models. METHODS The biomedical literature and gene expression databases, PubMed and GEO, respectively, were searched following the PRISMA guidelines. The key inclusion criteria were gene expression data derived from case-control studies on MI patients from blood samples. Gene expression datasets regarding the effect of medicinal drugs on MI were excluded. The t-test was applied to gene expression data from case-control studies in MI patients. RESULTS A total of 162 articles and 174 gene expression datasets were retrieved. Of those a total of 4 gene expression datasets met the inclusion criteria, which contained data on 31,180 loci in 93 MI patients and 89 healthy individuals. Collectively, 626 differentially expressed genes were detected in MI patients as compared to non-affected individuals at an FDR q-value = 0.01. Of those, 88 genes/gene products were interconnected in an interaction network. Totally, 15 genes were identified as hubs of the network. CONCLUSIONS Functional enrichment analyses revealed that the DEGs and that they are mainly involved in inflammatory/wound healing, RNA processing/transport mechanisms and a yet not fully characterized pathway implicated in RNA transport and nuclear pore proteins. The overlap between the DEGs identified in this study and the genes identified through genetic-association studies is minimal. These data could be useful in future studies on the molecular mechanisms of MI as well as diagnostic and prognostic markers.
Collapse
Affiliation(s)
- Panagiota Kontou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece
| | - Athanasia Pavlopoulou
- Izmir Biomedicine and Genome Institute, Dokuz Eylül University Health Campus, 35340, Izmir, Turkey
| | - Georgia Braliou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece
| | - Spyridoula Bogiatzi
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece
| | - Niki Dimou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Stavros Niarchos Av, 45110, Ioannina, Greece
| | - Sripal Bangalore
- School of Medicine, New York University, New York, NY 10016, USA
| | - Pantelis Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece. .,Lamia, University of Thessaly, Papasiopoulou 2-4, 35131, Lamia, Greece.
| |
Collapse
|
20
|
Mohammadi-Dehcheshmeh M, Niazi A, Ebrahimi M, Tahsili M, Nurollah Z, Ebrahimi Khaksefid R, Ebrahimi M, Ebrahimie E. Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis. FRONTIERS IN PLANT SCIENCE 2018; 9:1550. [PMID: 30483277 PMCID: PMC6240842 DOI: 10.3389/fpls.2018.01550] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 10/03/2018] [Indexed: 05/25/2023]
Abstract
Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.
Collapse
Affiliation(s)
- Manijeh Mohammadi-Dehcheshmeh
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA, Australia
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
| | - Ali Niazi
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
| | | | | | - Zahra Nurollah
- Department of Biotechnology, Shahrekord University, Shahrekord, Iran
| | - Reyhaneh Ebrahimi Khaksefid
- Department of Biotechnology, Shahrekord University, Shahrekord, Iran
- School of Agriculture Food and Wine, Department of Plant Science, The University of Adelaide, Adelaide, SA, Australia
| | - Mahdi Ebrahimi
- Max-Planck-Institute for Informatics, Saarbrucken, Germany
| | - Esmaeil Ebrahimie
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA, Australia
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Division of Information Technology, Engineering and the Environment, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia
- Faculty of Science and Engineering, School of Biological Sciences, Flinders University, Adelaide, SA, Australia
| |
Collapse
|
21
|
Selvaraj G, Kaliamurthi S, Kaushik AC, Khan A, Wei YK, Cho WC, Gu K, Wei DQ. Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms. J Biomed Inform 2018; 86:120-134. [PMID: 30195659 DOI: 10.1016/j.jbi.2018.09.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 08/11/2018] [Accepted: 09/05/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles. RESULTS A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively. CONCLUSION Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.
Collapse
Affiliation(s)
- Gurudeeban Selvaraj
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Chemistry, Chemical Engineering, and Environment, Henan University of Technology, Zhengzhou, China
| | - Satyavani Kaliamurthi
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Chemistry, Chemical Engineering, and Environment, Henan University of Technology, Zhengzhou, China
| | - Aman Chandra Kaushik
- Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Abbas Khan
- Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yong-Kai Wei
- College of Science, Henan University of Technology, Zhengzhou, China
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong
| | - Keren Gu
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Chemistry, Chemical Engineering, and Environment, Henan University of Technology, Zhengzhou, China
| | - Dong-Qing Wei
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China; College of Science, Henan University of Technology, Zhengzhou, China; Department of Bioinformatics, The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
22
|
Ashrafi-Dehkordi E, Alemzadeh A, Tanaka N, Razi H. Meta-analysis of transcriptomic responses to biotic and abiotic stress in tomato. PeerJ 2018; 6:e4631. [PMID: 30038850 PMCID: PMC6054068 DOI: 10.7717/peerj.4631] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 03/27/2018] [Indexed: 12/23/2022] Open
Abstract
A wide range of biotic stresses (BS) and abiotic stresses (AS) adversely affect plant growth and productivity worldwide. The study of individual genes cannot be considered as an effective approach for the understanding of tolerance mechanisms, since these stresses are frequent and often in combination with each other, and a large number of genes are involved in these mechanisms. The availability of high-throughput genomic data has enabled the discovery of the role of transcription factors (TFs) in regulatory networks. A meta-analysis of BS and AS responses was performed by analyzing a total of 391 microarray samples from 23 different experiments and 2,336 differentially expressed genes (DEGs) involved in multiple stresses were identified. We identified 1,862 genes differentially regulated in response to BS was much greater than that regulated by AS, 835 genes, and found 15.4% or 361 DEGs with the conserved expression between AS and BS. The greatest percent of genes related to the cellular process (>76% genes), metabolic process (>76% genes) and response to stimulus (>50%). About 4.2% of genes involved in BS and AS responses belonged to the TF families. We identified several genes, which encode TFs that play an important role in AS and BS responses. These proteins included Jasmonate Ethylene Response Factor 1 (JERF1), SlGRAS6, MYB48, SlERF4, EIL2, protein LATE ELONGATED HYPOCOTYL (LHY), SlERF1, WRKY 26, basic leucine zipper TF, inducer of CBF expression 1-like, pti6, EIL3 and WRKY 11. Six of these proteins, JERF1, MYB48, protein LHY, EIL3, EIL2 and SlGRAS6, play central roles in these mechanisms. This research promoted a new approach to clarify the expression profiles of various genes under different conditions in plants, detected common genes from differentially regulated in response to these conditions and introduced them as candidate genes for improving plant tolerance through genetic engineering approach.
Collapse
Affiliation(s)
- Elham Ashrafi-Dehkordi
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Abbas Alemzadeh
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Nobukazu Tanaka
- Center of Gene Science, Hiroshima University, Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan
| | - Hooman Razi
- Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
| |
Collapse
|
23
|
Nievergelt CM, Ashley-Koch AE, Dalvie S, Hauser MA, Morey RA, Smith AK, Uddin M. Genomic Approaches to Posttraumatic Stress Disorder: The Psychiatric Genomic Consortium Initiative. Biol Psychiatry 2018; 83:831-839. [PMID: 29555185 PMCID: PMC5915904 DOI: 10.1016/j.biopsych.2018.01.020] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/18/2017] [Accepted: 01/18/2018] [Indexed: 10/18/2022]
Abstract
Posttraumatic stress disorder (PTSD) after exposure to a traumatic event is a highly prevalent psychiatric disorder. Heritability estimates from twin studies as well as from recent molecular data (single nucleotide polymorphism-based heritability) indicate moderate to high heritability, yet robust genetic variants for PTSD have not yet been identified and the genetic architecture of this polygenic disorder remains largely unknown. To date, fewer than 10 large-scale genome-wide association studies of PTSD have been published, with findings that highlight the unique challenges for PTSD genomics, including a complex diagnostic entity with contingency of PTSD diagnosis on trauma exposure and the large genetic diversity of the study populations. The Psychiatric Genomics Consortium PTSD group has brought together more than 200 scientists with the goal to increase sample size for genome-wide association studies and other genomic analyses to sufficient numbers where robust discoveries of molecular signatures can be achieved. The sample currently includes more than 32,000 PTSD cases and 100,000 trauma-exposed control subjects, and collection is ongoing. The first results found a significant shared genetic risk of PTSD with other psychiatric disorders and sex-biased heritability estimates with higher heritability in female individuals compared with male individuals. This review describes the scope and current focus of the Psychiatric Genomics Consortium PTSD group and its expansion from the initial genome-wide association study group to nine working groups, including epigenetics, gene expression, imaging, and integrative systems biology. We further briefly outline recent findings and future directions of "omics"-based studies of PTSD, with the ultimate goal of elucidating the molecular architecture of this complex disorder to improve prevention and intervention strategies.
Collapse
Affiliation(s)
- Caroline M. Nievergelt
- University of California San Diego, Department of Psychiatry and Department of Family Medicine and Public Health,Veterans Affairs San Diego Healthcare System and Veterans Affairs Center of Excellence for Stress and Mental Health
| | | | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa, 7925
| | - Michael A. Hauser
- Department of Medicine, Duke University Medical Center, Durham, NC, United States
| | - Rajendra A. Morey
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham NC 27710, Durham VA Medical Center, Durham, NC 27705
| | - Alicia K. Smith
- Emory University, Department of Gynecology and Obstetrics,Emory University, Department of Psychiatry & Behavioral Sciences
| | - Monica Uddin
- University of Illinois Urbana-Champaign, Carl R. Woese Institute for Genomic Biology,University of Illinois Urbana-Champaign, Department of Psychology
| |
Collapse
|
24
|
Dai Z, Coker OO, Nakatsu G, Wu WKK, Zhao L, Chen Z, Chan FKL, Kristiansen K, Sung JJY, Wong SH, Yu J. Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers. MICROBIOME 2018; 6:70. [PMID: 29642940 PMCID: PMC5896039 DOI: 10.1186/s40168-018-0451-2] [Citation(s) in RCA: 296] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 03/19/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Alterations of gut microbiota are associated with colorectal cancer (CRC) in different populations and several bacterial species were found to contribute to the tumorigenesis. The potential use of gut microbes as markers for early diagnosis has also been reported. However, cohort specific noises may distort the structure of microbial dysbiosis in CRC and lead to inconsistent results among studies. In this regard, our study targeted at exploring changes in gut microbiota that are universal across populations at species level. RESULTS Based on the combined analysis of 526 metagenomic samples from Chinese, Austrian, American, and German and French cohorts, seven CRC-enriched bacteria (Bacteroides fragilis, Fusobacterium nucleatum, Porphyromonas asaccharolytica, Parvimonas micra, Prevotella intermedia, Alistipes finegoldii, and Thermanaerovibrio acidaminovorans) have been identified across populations. The seven enriched bacterial markers classified cases from controls with an area under the receiver-operating characteristics curve (AUC) of 0.80 across the different populations. Abundance correlation analysis demonstrated that CRC-enriched and CRC-depleted bacteria respectively formed their own mutualistic networks, in which the latter was disjointed in CRC. The CRC-enriched bacteria have been found to be correlated with lipopolysaccharide and energy biosynthetic pathways. CONCLUSIONS Our study identified potential diagnostic bacterial markers that are robust across populations, indicating their potential universal use for non-invasive CRC diagnosis. We also elucidated the ecological networks and functional capacities of CRC-associated microbiota.
Collapse
Affiliation(s)
- Zhenwei Dai
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Olabisi Oluwabukola Coker
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Geicho Nakatsu
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - William K K Wu
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Liuyang Zhao
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Zigui Chen
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Francis K L Chan
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Karsten Kristiansen
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- Institute of Metagenomics BGI-Shenzhen, Shenzhen, China
| | - Joseph J Y Sung
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sunny Hei Wong
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong.
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
| | - Jun Yu
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong.
- CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
| |
Collapse
|
25
|
Kontou PI, Pavlopoulou A, Bagos PG. Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes. Methods Mol Biol 2018; 1793:183-210. [PMID: 29876898 DOI: 10.1007/978-1-4939-7868-7_12] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.
Collapse
Affiliation(s)
- Panagiota I Kontou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Athanasia Pavlopoulou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.,International Biomedicine and Genome Institute (iBG-Izmir), Dokuz Eylul University, Izmir, 35340, Turkey
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
| |
Collapse
|
26
|
Pietrosemoli N, Mella S, Yennek S, Baghdadi MB, Sakai H, Sambasivan R, Pala F, Di Girolamo D, Tajbakhsh S. Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells. Skelet Muscle 2017; 7:28. [PMID: 29273087 PMCID: PMC5741941 DOI: 10.1186/s13395-017-0144-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 11/29/2017] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Skeletal muscle satellite (stem) cells are quiescent in adult mice and can undergo multiple rounds of proliferation and self-renewal following muscle injury. Several labs have profiled transcripts of myogenic cells during the developmental and adult myogenesis with the aim of identifying quiescent markers. Here, we focused on the quiescent cell state and generated new transcriptome profiles that include subfractionations of adult satellite cell populations, and an artificially induced prenatal quiescent state, to identify core signatures for quiescent and proliferating. METHODS Comparison of available data offered challenges related to the inherent diversity of datasets and biological conditions. We developed a standardized workflow to homogenize the normalization, filtering, and quality control steps for the analysis of gene expression profiles allowing the identification up- and down-regulated genes and the subsequent gene set enrichment analysis. To share the analytical pipeline of this work, we developed Sherpa, an interactive Shiny server that allows multi-scale comparisons for extraction of desired gene sets from the analyzed datasets. This tool is adaptable to cell populations in other contexts and tissues. RESULTS A multi-scale analysis comprising eight datasets of quiescent satellite cells had 207 and 542 genes commonly up- and down-regulated, respectively. Shared up-regulated gene sets include an over-representation of the TNFα pathway via NFKβ signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, while shared down-regulated gene sets exhibited an over-representation of Myc and E2F targets and genes associated to the G2M checkpoint and oxidative phosphorylation. However, virtually all datasets contained genes that are associated with activation or cell cycle entry, such as the immediate early stress response genes Fos and Jun. An empirical examination of fixed and isolated satellite cells showed that these and other genes were absent in vivo, but activated during procedural isolation of cells. CONCLUSIONS Through the systematic comparison and individual analysis of diverse transcriptomic profiles, we identified genes that were consistently differentially expressed among the different datasets and shared underlying biological processes key to the quiescent cell state. Our findings provide impetus to define and distinguish transcripts associated with true in vivo quiescence from those that are first responding genes due to disruption of the stem cell niche.
Collapse
Affiliation(s)
- Natalia Pietrosemoli
- Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Institut Pasteur, 75015 Paris, France
| | - Sébastien Mella
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Siham Yennek
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
- Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, University of Copenhagen, 3B Blegdamsvej, DK-2200 Copenhagen N, Denmark
| | - Meryem B. Baghdadi
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Hiroshi Sakai
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Ramkumar Sambasivan
- Institute for Stem Cell Biology and Regenerative Medicine, GKVK PO, Bellary Road, Bengaluru, 560065 India
| | - Francesca Pala
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Daniela Di Girolamo
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Shahragim Tajbakhsh
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| |
Collapse
|
27
|
Shen PC, Hour AL, Liu LYD. Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis. BOTANICAL STUDIES 2017; 58:22. [PMID: 28510204 PMCID: PMC5432924 DOI: 10.1186/s40529-017-0176-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 05/05/2017] [Indexed: 05/26/2023]
Abstract
BACKGROUND Abiotic stresses are the major limiting factors that affect plant growth, development, yield and final quality. Deciphering the underlying mechanisms of plants' adaptations to stresses using few datasets might overlook the different aspects of stress tolerance in plants, which might be simultaneously and consequently operated in the system. Fortunately, the accumulated microarray expression data offer an opportunity to infer abiotic stress-specific gene expression patterns through meta-analysis. In this study, we propose to combine microarray gene expression data under control, cold, drought, heat, and salt conditions and determined modules (gene sets) of genes highly associated with each other according to the observed expression data. RESULTS By analyzing the expression variations of the Eigen genes from different conditions, we had identified two, three, and five gene modules as cold-, heat-, and salt-specific modules, respectively. Most of the cold- or heat-specific modules were differentially expressed to a particular degree in shoot samples, while most of the salt-specific modules were differentially expressed to a particular degree in root samples. A gene ontology (GO) analysis on the stress-specific modules suggested that the gene modules exclusively enriched stress-related GO terms and that different genes under the same GO terms may be alternatively disturbed in different conditions. The gene regulatory events for two genes, DREB1A and DEAR1, in the cold-specific gene module had also been validated, as evidenced through the literature search. CONCLUSIONS Our protocols study the specificity of the gene modules that were specifically activated under a particular type of abiotic stress. The biplot can also assist to visualize the stress-specific gene modules. In conclusion, our approach has the potential to further elucidate mechanisms in plants and beneficial for future experiments design under different abiotic stresses.
Collapse
Affiliation(s)
- Po-chih Shen
- Biometrics Division, Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Ai-ling Hour
- Department of Life Science, Fu-Jen Catholic University, Xinbei, Taiwan
| | - Li-yu Daisy Liu
- Biometrics Division, Department of Agronomy, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
28
|
Novianti PW, Jong VL, Roes KCB, Eijkemans MJC. Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia. BMC Bioinformatics 2017; 18:210. [PMID: 28399794 PMCID: PMC5387259 DOI: 10.1186/s12859-017-1619-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 03/30/2017] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. RESULTS Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. CONCLUSION Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.
Collapse
Affiliation(s)
- Putri W. Novianti
- Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
- Department of Epidemiology and Biostatistics, VU University medical center, Amsterdam, The Netherlands
- Department of Pathology, VU University medical center, Amsterdam, The Netherlands
| | - Victor L. Jong
- Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
- Viroscience Laboratory, Erasmus Medical Center Rotterdam, 3015 CE Rotterdam, The Netherlands
| | - Kit C. B. Roes
- Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Marinus J. C. Eijkemans
- Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| |
Collapse
|
29
|
Donaires FS, Godoy PRDV, Leandro GS, Puthier D, Sakamoto-Hojo ET. E2F transcription factors associated with up-regulated genes in glioblastoma. Cancer Biomark 2017; 18:199-208. [PMID: 27983535 DOI: 10.3233/cbm-161628] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Glioblastoma is considered to the most common and malignant brain tumor in adults. Patients have a median survival of approximately one year from diagnosis due to poor response to therapy. OBJECTIVE We applied bioinformatics approaches to predict transcription factors (TF) that are deregulated in glioblastoma in an attempt to point out molecular targets for therapy. METHODS Up-regulated genes in glioblastoma selected from public microarray data were submitted to two TF association analyses. Thereafter, the expression levels of TF obtained in the overlap of analyses were assessed by RT-qPCR carried out in seven glioblastoma cell lines (T98, U251, U138, U87, U343, M059J, and M059K). RESULTS E2F1 and E2F4 were highlighted in both TF analyses. However, only E2F1 was confirmed as significantly up-regulated in all glioblastoma cell lines in vitro. CONCLUSION E2F1 is a potential common regulator of differentially expressed genes in glioblastoma, despite the genetic heterogeneity of tumor cells.
Collapse
Affiliation(s)
- Flávia S Donaires
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Paulo R D V Godoy
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Giovana S Leandro
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Denis Puthier
- Technological Advances for Genomics and Clinics (TAGC), UMR, S 1090 INSERM Aix-Marseille Université, U928 Parc Scientifique de Luminy Case 928 163, Avenue de Luminy, 13288 Marseille Cedex 9, France
| | - Elza T Sakamoto-Hojo
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil.,Department of Biology, Faculty of Philosophy, Sciences, and Letters at Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| |
Collapse
|
30
|
Kobzik L. Searching for a Lifeline: Transcriptome Profiling Studies of Influenza Susceptibility and Resistance. J Innate Immun 2017; 9:232-242. [PMID: 28249256 DOI: 10.1159/000457902] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 01/24/2017] [Indexed: 11/19/2022] Open
Abstract
Excess or dysregulated host inflammatory responses cause much of the morbidity and mortality caused by severe influenza. Given the limitations of vaccines and antiviral drugs, novel therapeutics to modulate host responses and improve outcomes in severe influenza are needed. One strategy is to learn from the direct comparison of high-survivor versus high-mortality animal models. This review surveys the results of lung transcriptome profiling studies in murine models that directly compare susceptible versus resistant hosts challenged with identical influenza infections. The potential contributions and limitations of these studies are discussed. To amplify their power, the studies are subjected to a meta-analysis, which helps identify frequently dysregulated pathways and potentially novel areas for investigation. Using connectivity map-based tools (LINCS), transcriptome signatures linked to susceptibility can identify candidate drugs that merit testing for in vivo efficacy.
Collapse
Affiliation(s)
- Lester Kobzik
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, and Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| |
Collapse
|
31
|
Li YF, Nanayakkara G, Sun Y, Li X, Wang L, Cueto R, Shao Y, Fu H, Johnson C, Cheng J, Chen X, Hu W, Yu J, Choi ET, Wang H, Yang XF. Analyses of caspase-1-regulated transcriptomes in various tissues lead to identification of novel IL-1β-, IL-18- and sirtuin-1-independent pathways. J Hematol Oncol 2017; 10:40. [PMID: 28153032 PMCID: PMC5290602 DOI: 10.1186/s13045-017-0406-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 01/25/2017] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND It is well established that caspase-1 exerts its biological activities through its downstream targets such as IL-1β, IL-18, and Sirt-1. The microarray datasets derived from various caspase-1 knockout tissues indicated that caspase-1 can significantly impact the transcriptome. However, it is not known whether all the effects exerted by caspase-1 on transcriptome are mediated only by its well-known substrates. Therefore, we hypothesized that the effects of caspase-1 on transcriptome may be partially independent from IL-1β, IL-18, and Sirt-1. METHODS To determine new global and tissue-specific gene regulatory effects of caspase-1, we took novel microarray data analysis approaches including Venn analysis, cooperation analysis, and meta-analysis methods. We used these statistical methods to integrate different microarray datasets conducted on different caspase-1 knockout tissues and datasets where caspase-1 downstream targets were manipulated. RESULTS We made the following important findings: (1) Caspase-1 exerts its regulatory effects on the majority of genes in a tissue-specific manner; (2) Caspase-1 regulatory genes partially cooperates with genes regulated by sirtuin-1 during organ injury and inflammation in adipose tissue but not in the liver; (3) Caspase-1 cooperates with IL-1β in regulating less than half of the genes involved in cardiovascular disease, organismal injury, and cancer in mouse liver; (4) The meta-analysis identifies 40 caspase-1 globally regulated genes across tissues, suggesting that caspase-1 globally regulates many novel pathways; and (5) The meta-analysis identified new cooperatively and non-cooperatively regulated genes in caspase-1, IL-1β, IL-18, and Sirt-1 pathways. CONCLUSIONS Our findings suggest that caspase-1 regulates many new signaling pathways potentially via its known substrates and also via transcription factors and other proteins that are yet to be identified.
Collapse
Affiliation(s)
- Ya-Feng Li
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.,The Shanxi Provincial People's Hospital, an Affiliate Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, China
| | - Gayani Nanayakkara
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Yu Sun
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Xinyuan Li
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Luqiao Wang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Ramon Cueto
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Ying Shao
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Hangfei Fu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Candice Johnson
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Jiali Cheng
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Xiongwen Chen
- Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Wenhui Hu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Jun Yu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Eric T Choi
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Hong Wang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Physiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Xiao-Feng Yang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA. .,Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA. .,Department of Physiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA. .,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.
| |
Collapse
|
32
|
Identification of Polycystic Ovary Syndrome (PCOS) Specific Genes in Cumulus and Mural Granulosa Cells. PLoS One 2016; 11:e0168875. [PMID: 27997581 PMCID: PMC5173369 DOI: 10.1371/journal.pone.0168875] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 12/07/2016] [Indexed: 11/19/2022] Open
Abstract
Polycystic ovary syndrome (PCOS) is a metabolic and endocrine disorder which affects women of reproductive age with prevalence of 8–18%. The oocyte within the follicle is surrounded by cumulus cells (CCs), which connect with mural granulosa cells (MGCs) that are responsible for secreting steroid hormones. The main aim of this study is comparing gene expression profiles of MGCs and CCs in PCOS and control samples to identify PCOS-specific differentially expressed genes (DEGs). In this study, two microarray databases were searched for mRNA expression microarray studies performed with CCs and MGCs obtained from PCOS patients and control samples. Three independent studies were selected to be integrated with naive meta-analysis since raw meta-data from these studies were found to be highly correlated. DEGs in these somatic cells were identified for PCOS and control groups. This study enabled us to reveal dysregulation in MAPK (mitogen activated protein kinase), insulin and Wnt signaling pathways between CCs and MGCs in PCOS. The meta-analysis results together with qRT-PCR validations provide evidence that molecular signaling is dysregulated through MGCs and CCs in PCOS, which is important for follicle and oocyte maturation and may contribute to the pathogenesis of the syndrome.
Collapse
|
33
|
Plantier L, Renaud H, Respaud R, Marchand-Adam S, Crestani B. Transcriptome of Cultured Lung Fibroblasts in Idiopathic Pulmonary Fibrosis: Meta-Analysis of Publically Available Microarray Datasets Reveals Repression of Inflammation and Immunity Pathways. Int J Mol Sci 2016; 17:ijms17122091. [PMID: 27983601 PMCID: PMC5187891 DOI: 10.3390/ijms17122091] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Revised: 12/02/2016] [Accepted: 12/05/2016] [Indexed: 12/21/2022] Open
Abstract
Heritable profibrotic differentiation of lung fibroblasts is a key mechanism of idiopathic pulmonary fibrosis (IPF). Its mechanisms are yet to be fully understood. In this study, individual data from four independent microarray studies comparing the transcriptome of fibroblasts cultured in vitro from normal (total n = 20) and IPF (total n = 20) human lung were compiled for meta-analysis following normalization to z-scores. One hundred and thirteen transcripts were upregulated and 115 were downregulated in IPF fibroblasts using the Significance Analysis of Microrrays algorithm with a false discovery rate of 5%. Downregulated genes were highly enriched for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional classes related to inflammation and immunity such as Defense response to virus, Influenza A, tumor necrosis factor (TNF) mediated signaling pathway, interferon-inducible absent in melanoma2 (AIM2) inflammasome as well as Apoptosis. Although upregulated genes were not enriched for any functional class, select factors known to play key roles in lung fibrogenesis were overexpressed in IPF fibroblasts, most notably connective tissue growth factor (CTGF) and serum response factor (SRF), supporting their role as drivers of IPF. The full data table is available as a supplement.
Collapse
Affiliation(s)
- Laurent Plantier
- Centre d'Étude des Pathologies Respiratoires-CEPR, Institut National de la Santé et de la Recherche Médicale-INSERM, Unité Mixte de Recherche-UMR1100, Labex Mabimprove, 37000 Tours, France.
- Université François Rabelais, 37000 Tours, France.
- Centre Hospitalier Régional Universitaire-CHRU de Tours, Hôpital Bretonneau, Service de Pneumologie et Explorations Fonctionnelles Respiratoires, 37000 Tours, France.
| | - Hélène Renaud
- Institut National de la Santé et de la Recherche Médicale-INSERM, Unité Mixte de Recherche-UMR1152, Labex Inflamex, 75018 Paris, France.
| | - Renaud Respaud
- Centre d'Étude des Pathologies Respiratoires-CEPR, Institut National de la Santé et de la Recherche Médicale-INSERM, Unité Mixte de Recherche-UMR1100, Labex Mabimprove, 37000 Tours, France.
- Université François Rabelais, 37000 Tours, France.
- Centre Hospitalier Régional Universitaire-CHRU de Tours, Hôpital Trousseau, Service de Pharmacie, 37170 Chambray-les-Tours, France.
| | - Sylvain Marchand-Adam
- Centre d'Étude des Pathologies Respiratoires-CEPR, Institut National de la Santé et de la Recherche Médicale-INSERM, Unité Mixte de Recherche-UMR1100, Labex Mabimprove, 37000 Tours, France.
- Université François Rabelais, 37000 Tours, France.
- Centre Hospitalier Régional Universitaire-CHRU de Tours, Hôpital Bretonneau, Service de Pneumologie et Explorations Fonctionnelles Respiratoires, 37000 Tours, France.
| | - Bruno Crestani
- Institut National de la Santé et de la Recherche Médicale-INSERM, Unité Mixte de Recherche-UMR1152, Labex Inflamex, 75018 Paris, France.
- Université Paris Diderot, PRES Sorbonne Paris Cité, 75018 Paris, France.
- AP-HP, Hôpital Bichat, Service de Pneumologie A, DHU FIRE, 75018 Paris, France.
| |
Collapse
|
34
|
DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma: A meta-analysis. Comput Biol Chem 2016; 65:154-164. [DOI: 10.1016/j.compbiolchem.2016.09.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 09/07/2016] [Indexed: 12/31/2022]
|
35
|
Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing. Immunity 2016; 45:1191-1204. [DOI: 10.1016/j.immuni.2016.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 11/30/2016] [Accepted: 12/01/2016] [Indexed: 12/15/2022]
|
36
|
Jha PK, Vijay A, Sahu A, Ashraf MZ. Comprehensive Gene expression meta-analysis and integrated bioinformatic approaches reveal shared signatures between thrombosis and myeloproliferative disorders. Sci Rep 2016; 6:37099. [PMID: 27892526 PMCID: PMC5125005 DOI: 10.1038/srep37099] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/25/2016] [Indexed: 02/06/2023] Open
Abstract
Thrombosis is a leading cause of morbidity and mortality in patients with myeloproliferative disorders (MPDs), particularly polycythemia vera (PV) and essential thrombocythemia (ET). Despite the attempts to establish a link between them, the shared biological mechanisms are yet to be characterized. An integrated gene expression meta-analysis of five independent publicly available microarray data of the three diseases was conducted to identify shared gene expression signatures and overlapping biological processes. Using INMEX bioinformatic tool, based on combined Effect Size (ES) approaches, we identified a total of 1,157 differentially expressed genes (DEGs) (697 overexpressed and 460 underexpressed genes) shared between the three diseases. EnrichR tool's rich library was used for comprehensive functional enrichment and pathway analysis which revealed "mRNA Splicing" and "SUMO E3 ligases SUMOylate target proteins" among the most enriched terms. Network based meta-analysis identified MYC and FN1 to be the most highly ranked hub genes. Our results reveal that the alterations in biomarkers of the coagulation cascade like F2R, PROS1, SELPLG and ITGB2 were common between the three diseases. Interestingly, the study has generated a novel database of candidate genetic markers, pathways and transcription factors shared between thrombosis and MPDs, which might aid in the development of prognostic therapeutic biomarkers.
Collapse
Affiliation(s)
| | - Aatira Vijay
- Defence Institute of Physiology and Allied Sciences, Delhi, INDIA
| | - Anita Sahu
- Defence Institute of Physiology and Allied Sciences, Delhi, INDIA
| | | |
Collapse
|
37
|
Sweeney TE, Haynes WA, Vallania F, Ioannidis JP, Khatri P. Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Res 2016; 45:e1. [PMID: 27634930 PMCID: PMC5224496 DOI: 10.1093/nar/gkw797] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 08/28/2016] [Accepted: 08/31/2016] [Indexed: 12/28/2022] Open
Abstract
Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.
Collapse
Affiliation(s)
- Timothy E Sweeney
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Winston A Haynes
- Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Francesco Vallania
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John P Ioannidis
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA 94305, USA.,Meta-research Innovation Center at Stanford (METRICS), Stanford, CA 94305, USA
| | - Purvesh Khatri
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA .,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
38
|
Grammer AC, Ryals MM, Heuer SE, Robl RD, Madamanchi S, Davis LS, Lauwerys B, Catalina MD, Lipsky PE. Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis. Lupus 2016; 25:1150-70. [DOI: 10.1177/0961203316657437] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Lupus patients are in need of modern drugs to treat specific manifestations of their disease effectively and safely. In the past half century, only one new treatment has been approved by the US Food and Drug Administration (FDA) for systemic lupus erythematosus (SLE). In 2014–2015, the FDA approved 71 new drugs, only one of which targeted a rheumatic disease and none of which was approved for use in SLE. Repositioning/repurposing drugs approved for other diseases using multiple approaches is one possible means to find new treatment options for lupus patients. “Big Data” analysis approaches this challenge from an unbiased standpoint whereas literature mining and crowd sourcing for candidates assessed by the CoLTs (Combined Lupus Treatment Scoring) system provide a hypothesis-based approach to rank potential therapeutic candidates for possible clinical application. Both approaches mitigate risk since the candidates assessed have largely been extensively tested in clinical trials for other indications. The usefulness of a multi-pronged approach to drug repositioning in lupus is highlighted by orthogonal confirmation of hypothesis-based drug repositioning predictions by “Big Data” analysis of differentially expressed genes from lupus patient samples. The goal is to identify novel therapies that have the potential to affect disease processes specifically. Involvement of SLE patients and the scientists that study this disease in thinking about new drugs that may be effective in lupus though crowd-sourcing sites such as LRxL-STAT ( www.linkedin.com/in/lrxlstat ) is important in stimulating the momentum needed to test these novel drug targets for efficacy in lupus rapidly in small, proof-of-concept trials conducted by LuCIN, the Lupus Clinical Investigators Network ( www.linkedin.com/in/lucinstat ).
Collapse
Affiliation(s)
- A C Grammer
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - M M Ryals
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - S E Heuer
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - R D Robl
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - S Madamanchi
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - L S Davis
- Department of Internal Medicine, UTSW Medical Center at Dallas, Dallas, TX, USA
| | - B Lauwerys
- Université Catholique de Louvain, Brussels, Belgium
| | - M D Catalina
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - P E Lipsky
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| |
Collapse
|
39
|
Lagani V, Karozou AD, Gomez-Cabrero D, Silberberg G, Tsamardinos I. A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions. BMC Bioinformatics 2016; 17 Suppl 5:194. [PMID: 27294826 PMCID: PMC4905611 DOI: 10.1186/s12859-016-1038-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study. RESULTS The meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms. CONCLUSIONS Ignoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.
Collapse
Affiliation(s)
- Vincenzo Lagani
- />Institute of Computer Science, Foundation for Research and Technology – Hellas, Heraklion, Greece
- />Computer Science Department, University of Crete, Heraklion, Sweden
| | - Argyro D. Karozou
- />Institute of Computer Science, Foundation for Research and Technology – Hellas, Heraklion, Greece
| | - David Gomez-Cabrero
- />Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- />Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- />Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Heraklion, Sweden
- />Science for Life Laboratory, 17121 Solna, Sweden
| | - Gilad Silberberg
- />Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- />Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- />Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Heraklion, Sweden
- />Science for Life Laboratory, 17121 Solna, Sweden
| | - Ioannis Tsamardinos
- />Institute of Computer Science, Foundation for Research and Technology – Hellas, Heraklion, Greece
- />Computer Science Department, University of Crete, Heraklion, Sweden
| |
Collapse
|
40
|
Solvang HK, Frigessi A, Kaveh F, Riis MLH, Lüders T, Bukholm IRK, Kristensen VN, Andreassen BK. Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2016; 2016:6. [PMID: 26900390 PMCID: PMC4746218 DOI: 10.1186/s13637-015-0034-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 12/23/2015] [Indexed: 11/17/2022]
Abstract
Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2–2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.
Collapse
Affiliation(s)
- Hiroko K Solvang
- Department of Marine Mammals, Institute of Marine Research, C. Sundts Gate 64, Bergen, 5004 Norway
| | - Arnoldo Frigessi
- Department of Biostatistics, Institute of Basic Medical Science, University of Oslo, Norway and Statistics for Innovation-(sfi)2, Oslo, Norway
| | - Fateme Kaveh
- Medical Genetics Department, Oslo University Hospital (Ullevål), Oslo, Norway
| | - Margit L H Riis
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Torben Lüders
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Ida R K Bukholm
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Institute of Clinical Medicine, University of Oslo, Norwegian Center of HPH Network, Oslo, Norway
| | - Vessela N Kristensen
- Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway ; Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | | |
Collapse
|
41
|
Seo M, Caetano-Anolles K, Rodriguez-Zas S, Ka S, Jeong JY, Park S, Kim MJ, Nho WG, Cho S, Kim H, Lee HJ. Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq. BMC Genomics 2016; 17:81. [PMID: 26818975 PMCID: PMC4728830 DOI: 10.1186/s12864-016-2400-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 01/18/2016] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Molecular mechanisms associated with sexual dimorphism in cattle have not been well elucidated. Furthermore, as recent studies have implied that gene expression patterns are highly tissue specific, it is essential to investigate gene expression in a variety of tissues using RNA-seq. Here, we employed and compared two statistical methods, a simple two group test and Analysis of deviance (ANODEV), in order to investigate bovine sexually dimorphic genes in 40 RNA-seq samples distributed across two factors: sex and tissue. RESULTS As a result, we detected 752 sexually dimorphic genes across tissues from two statistical approaches and identified strong tissue-specific patterns of gene expression. Additionally, significantly detected sex-related genes shared between two mammal species (cattle and rat) were identified using qRT-PCR. CONCLUSIONS Results of our analyses reveal that sexual dimorphism of metabolic tissues and pituitary gland in cattle involves various biological processes. Several differentially expressed genes between sexes in cattle and rat species are shared, but show tissue-specific patterns. Finally, we concluded that two distinct statistical approaches have their advantages and disadvantages in RNA-seq studies investigating multiple tissues.
Collapse
Affiliation(s)
- Minseok Seo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea.
- CHO&KIM genomics, Main Bldg. #514, SNU Research Park, Seoul National University Mt.4-2, NakSeoungDae, Gwanakgu, Seoul, 151-919, Republic of Korea.
| | | | | | - Sojeong Ka
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea.
| | - Jin Young Jeong
- Division of Animal Products R&D, National Institute of Animal science, #1500 Kongjwipatjwi-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 565-851, Republic of Korea.
| | - Sungkwon Park
- Department of food science and technology, Sejong University, 98 Gun-Ja-Dong, Seoul, 143-747, Republic of Korea.
| | - Min Ji Kim
- Department of food science and technology, Sejong University, 98 Gun-Ja-Dong, Seoul, 143-747, Republic of Korea.
| | - Whan-Gook Nho
- Department of Swine & Poultry Science, National College of Agriculture and Fisheries, #1515 Kongjwipatjwi-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 560-500, Republic of Korea.
| | - Seoae Cho
- CHO&KIM genomics, Main Bldg. #514, SNU Research Park, Seoul National University Mt.4-2, NakSeoungDae, Gwanakgu, Seoul, 151-919, Republic of Korea.
| | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea.
- CHO&KIM genomics, Main Bldg. #514, SNU Research Park, Seoul National University Mt.4-2, NakSeoungDae, Gwanakgu, Seoul, 151-919, Republic of Korea.
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea.
| | - Hyun-Jeong Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea.
- Division of Animal Products R&D, National Institute of Animal science, #1500 Kongjwipatjwi-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 565-851, Republic of Korea.
| |
Collapse
|
42
|
Teumer A, Schurmann C, Schillert A, Schramm K, Ziegler A, Prokisch H. Analyzing Illumina Gene Expression Microarray Data Obtained From Human Whole Blood Cell and Blood Monocyte Samples. Methods Mol Biol 2016; 1368:85-97. [PMID: 26614070 DOI: 10.1007/978-1-4939-3136-1_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Microarray profiling of gene expression is widely applied to studies in molecular biology and functional genomics. Experimental and technical variations make not only the statistical analysis of single studies but also meta-analyses of different studies very challenging. Here, we describe the analytical steps required to substantially reduce the variations of gene expression data without affecting true effect sizes. A software pipeline has been established using gene expression data from a total of 3358 whole blood cell and blood monocyte samples, all from three German population-based cohorts, measured on the Illumina HumanHT-12 v3 BeadChip array. In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses of different studies.
Collapse
Affiliation(s)
- Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Fleischmannstraße 8, Greifswald, Germany.
| | - Claudia Schurmann
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Fleischmannstraße 8, Greifswald, Germany
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 10029, New York, NY, USA
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, 10029, New York, NY, USA
| | - Arne Schillert
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Katharina Schramm
- Institute of Human Genetics, Helmholtz Zentrum München, Munich, Germany
- German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Kiel/Lübeck, Lübeck, Germany
- Center for Clinical Trials, University of Lübeck, Lübeck, Germany
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Zentrum München, Munich, Germany
- German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| |
Collapse
|
43
|
Zhang J, Shao J, Wu X, Mao Q, Wang Y, Gao F, Kong W, Liang Z. Type I interferon related genes are common genes on the early stage after vaccination by meta-analysis of microarray data. Hum Vaccin Immunother 2015; 11:739-45. [PMID: 25839220 DOI: 10.1080/21645515.2015.1008884] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The objective of this study was to find common immune mechanism across different kinds of vaccines. A meta-analysis of microarray datasets was performed using publicly available microarray Gene Expression Omnibus (GEO) and Array Express data sets of vaccination records. Seven studies (out of 35) were selected for this meta-analysis. A total of 447 chips (145 pre-vaccination and 302 post-vaccination) were included. Significance analysis of microarrays (SAM) program was used for screening differentially expressed genes (DEGs). Functional pathway enrichment for the DEGs was conducted in DAVID Gene Ontology (GO) database. Twenty DEGs were identified, of which 10 up-regulated genes involved immune response. Six of which were type I interferon (IFN) related genes, including LY6E, MX1, OAS3, IFI44L, IFI6 and IFITM3. Ten down-regulated genes mainly mediated negative regulation of cell proliferation and cell motion. Results of a subgroup analysis showed that although the kinds of genes varied widely between days 3 and 7 post vaccination, the pathways between them are basically the same, such as immune response and response to viruses, etc. For an independent verification of these 6 type I IFN related genes, peripheral blood mononuclear cells (PBMCs) were collected at baseline and day 3 after the vaccination from 8 Enterovirus 71(EV71) vaccinees and were assayed by RT-PCR. Results showed that the 6 DEGs were also upregulated in EV71 vaccinees. In summary, meta-analysis methods were used to explore the immune mechanism of vaccines and results indicated that the type I IFN related genes and corresponding pathways were common in early immune responses for different kinds of vaccines.
Collapse
Key Words
- CPE, cytopathogenic effect
- DCs, dendritic cells
- DEGs, differentially expressed genes
- EV71, enterovirus 71
- GEO, Gene Expression Omnibus
- GO, gene ontology
- IFN, interferon
- PBMCs, peripheral blood mononuclear cells
- PRRs, pattern recognition receptors
- SAM, significance analysis of microarrays
- TLRs, Toll-like receptors
- immune mechanism
- meta-analysis
- microarray
- type I interferon
- vaccine
Collapse
Affiliation(s)
- Junnan Zhang
- a National Institutes for Food and Drug Control ; Beijing , P.R. China
| | | | | | | | | | | | | | | |
Collapse
|
44
|
Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery. MICROARRAYS 2015; 4:389-406. [PMID: 27600230 PMCID: PMC4996376 DOI: 10.3390/microarrays4030389] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/16/2015] [Accepted: 08/17/2015] [Indexed: 01/24/2023]
Abstract
The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.
Collapse
|
45
|
Yang TY. A Simple Rank Product Approach for Analyzing Two Classes. Bioinform Biol Insights 2015; 9:119-23. [PMID: 26244016 PMCID: PMC4507469 DOI: 10.4137/bbi.s26414] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 06/14/2015] [Accepted: 06/21/2015] [Indexed: 11/24/2022] Open
Abstract
The rank product statistic has been widely used to detect differentially expressed genes in replicated microarrays and a one-class setting. The objective of this article is to apply a rank product statistic to approximate the P-value of differential expression in a two-class setting, such as in normal and cancer cells. For this purpose, we introduce a simple statistic that compares the P-values of each class’s rank product statistic. Its null distribution is straightforwardly derived using the change-of-variable technique.
Collapse
Affiliation(s)
- Tae Young Yang
- Department of Mathematics, Myongji University, Yongin, Kyonggi, Korea
| |
Collapse
|
46
|
Oztemur Y, Bekmez T, Aydos A, Yulug IG, Bozkurt B, Dedeoglu BG. A ranking-based meta-analysis reveals let-7 family as a meta-signature for grade classification in breast cancer. PLoS One 2015; 10:e0126837. [PMID: 25978727 PMCID: PMC4433233 DOI: 10.1371/journal.pone.0126837] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 04/08/2015] [Indexed: 12/23/2022] Open
Abstract
Breast cancer is one of the most important causes of cancer-related deaths worldwide in women. In addition to gene expression studies, the progressing work in the miRNA area including miRNA microarray studies, brings new aspects to the research on the cancer development and progression. Microarray technology has been widely used to find new biomarkers in research and many transcriptomic microarray studies are available in public databases. In this study, the breast cancer miRNA and mRNA microarray studies were collected according to the availability of their data and clinical information, and combined by a newly developed ranking-based meta-analysis approach to find out candidate miRNA biomarkers (meta-miRNAs) that classify breast cancers according to their grades and explain the relation between miRNAs and mRNAs. This approach provided meta-miRNAs specific to breast cancer grades, pointing out let-7 family members as grade classifiers. The qRT-PCR studies performed with independent breast tumors confirmed the potential biomarker role of let-7 family members (meta-miRNAs). The concordance between the meta-mRNAs and miRNA target genes specific to tumor grade (common genes) supported the idea of mRNAs as miRNA targets. The pathway analysis results showed that most of the let-7 family miRNA targets, and also common genes, were significantly taking part in cancer-related pathways. The qRT-PCR studies, together with bioinformatic analyses, confirmed the results of meta-analysis approach, which is dynamic and allows combining datasets from different platforms.
Collapse
Affiliation(s)
| | - Tufan Bekmez
- Gazi University, Faculty of Dentistry, Ankara, Turkey
| | - Alp Aydos
- Ankara University, Biotechnology Institute, Ankara, Turkey
| | - Isik G. Yulug
- Bilkent University, Molecular Biology and Genetics Department, Ankara, Turkey
| | - Betul Bozkurt
- Ankara Numune Training and Research Hospital, Department of General Surgery, Ankara, Turkey
| | | |
Collapse
|
47
|
Yang ZH, Zheng R, Gao Y, Zhang Q. Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods. CLINICAL RESPIRATORY JOURNAL 2015; 10:631-46. [PMID: 25619939 DOI: 10.1111/crj.12271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 11/20/2014] [Accepted: 01/20/2015] [Indexed: 11/30/2022]
Abstract
BACKGROUND With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies. OBJECTIVES We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods. METHODS Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity. RESULTS Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01). CONCLUSIONS Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research.
Collapse
Affiliation(s)
- Ze-Hui Yang
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Rui Zheng
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Yuan Gao
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qiang Zhang
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
48
|
A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft. Kidney Int 2015; 87:984-95. [PMID: 25629549 PMCID: PMC4424816 DOI: 10.1038/ki.2014.395] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 10/02/2014] [Accepted: 10/23/2014] [Indexed: 01/21/2023]
Abstract
Patients tolerant to a kidney graft display a specific blood cell transcriptional pattern but results from five different studies were inconsistent, raising the question of relevance for future clinical application. To resolve this, we sought to identify a common gene signature, specific functional and cellular components, and discriminating biomarkers for tolerance following kidney transplantation. A meta-analysis of studies identified a robust gene signature involving proliferation of B and CD4 T cells, and inhibition of CD14 monocyte related functions among 96 tolerant samples. This signature was further supported through a cross-validation approach, yielding 92.5% accuracy independent of the study of origin. Experimental validation, performed on new tolerant samples and using a selection of the top-20 biomarkers, returned 91.7% of good classification. Beyond the confirmation of B-cell involvement, our data also indicated participation of other cell subsets in tolerance. Thus, the use of the top 20 biomarkers, mostly centered on B cells, may provide a common and standardized tool towards personalized medicine for the monitoring of tolerant or low-risk patients among kidney allotransplant recipients. These data point to a global preservation of genes favoring the maintenance of a homeostatic and ‘healthy' environment in tolerant patients and may contribute to a better understanding of tolerance maintenance mechanisms.
Collapse
|
49
|
Abstract
The integrative correlation coefficient was developed to facilitate the validation of expression microarray results in public datasets, by identifying genes that are reproducibly measured across studies and even across microarray platforms. In the current study, we develop a number of interesting and important mathematical and statistical properties of the integrative correlation coefficient, including a unique permutation-based null distribution with the unusual property that the variance does not shrink as the sample size increases, discussing how these findings impact its use and interpretation, and what they have to say about any method for identifying reproducible genes in a meta-analysis.
Collapse
|
50
|
Chang LC, Lin HM, Sibille E, Tseng GC. Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline. BMC Bioinformatics 2013; 14:368. [PMID: 24359104 PMCID: PMC3898528 DOI: 10.1186/1471-2105-14-368] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2013] [Accepted: 12/05/2013] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. RESULTS We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. CONCLUSIONS The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.
Collapse
Affiliation(s)
- Lun-Ching Chang
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hui-Min Lin
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Etienne Sibille
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - George C Tseng
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|