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Ektesabi AM, Mori K, Tsoporis JN, Vaswani CM, Gupta S, Walsh C, Varkouhi AK, Mei SH, Stewart DJ, Liles WC, Marshall JC, Hu P, Parker TG, dos Santos CC. Mesenchymal Stem/Stromal Cells Increase Cardiac miR-187-3p Expression in a Polymicrobial Animal Model of Sepsis. Shock 2021; 56:133-141. [PMID: 33378320 PMCID: PMC8240645 DOI: 10.1097/shk.0000000000001701] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 12/27/2022]
Abstract
ABSTRACT Sepsis-induced myocardial dysfunction (MD) is an important pathophysiological feature of multiorgan failure caused by a dysregulated host response to infection. Patients with MD continue to be managed in intensive care units with limited understanding of the molecular mechanisms controlling disease pathogenesis. Emerging evidences support the use of mesenchymal stem/stromal cell (MSC) therapy for treating critically ill septic patients. Combining this with the known role that microRNAs (miRNAs) play in reversing sepsis-induced myocardial-dysfunction, this study sought to investigate how MSC administration alters miRNA expression in the heart. Mice were randomized to experimental polymicrobial sepsis induced by cecal ligation and puncture (CLP) or sham surgery, treated with either MSCs (2.5 × 105) or placebo (saline). Twenty-eight hours post-intervention, RNA was collected from whole hearts for transcriptomic and microRNA profiling. The top microRNAs differentially regulated in hearts by CLP and MSC administration were used to generate a putative mRNA-miRNA interaction network. Key genes, termed hub genes, within the network were then identified and further validated in vivo. Network analysis and RT-qPCR revealed that septic hearts treated with MSCs resulted in upregulation of five miRNAs, including miR-187, and decrease in three top hit putative hub genes (Itpkc, Lrrc59, and Tbl1xr1). Functionally, MSC administration decreased inflammatory and apoptotic pathways, while increasing cardiac-specific structural and functional, gene expression. Taken together, our data suggest that MSC administration regulates host-derived miRNAs production to protect cardiomyocytes from sepsis-induced MD.
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Affiliation(s)
- Amin M. Ektesabi
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Keisuke Mori
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - James N. Tsoporis
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Chirag M. Vaswani
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Sahil Gupta
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Chris Walsh
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Amir K. Varkouhi
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Shirley H.J. Mei
- Ottawa Hospital Research Institute and the University of Ottawa, Ottawa, ON, Canada
| | - Duncan J. Stewart
- Ottawa Hospital Research Institute and the University of Ottawa, Ottawa, ON, Canada
| | - W. Conrad Liles
- Department of Medicine and Sepsis Center of Research Excellence-UW (SCORE-UW), University of Washington, Seattle, Washington
| | - John C. Marshall
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Thomas G. Parker
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Claudia C. dos Santos
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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202
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Seok HS. Enhancing performance of gene expression value prediction with cluster-based regression. Genes Genomics 2021; 43:1059-1064. [PMID: 34181214 DOI: 10.1007/s13258-021-01128-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/16/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND The inherent correlations among gene expressions have received attention. Recently, it was reported that a set of approximately 1000 landmark genes can be utilized for prediction of expression of other genes (target genes). OBJECTIVE The objective of this study is to predict expression values of target genes based on expression values of landmark genes. METHODS A cluster-based regression method is proposed. In the proposed method, clusters are obtained from a set of training instances of a gene and an estimator is obtained per cluster. A test instance is assigned to one of clusters then a regression model corresponding to the cluster predicts expression value. RESULTS Performance of the proposed method is measured on the GEO (Gene Expression Omnibus) expression data and the GTEx (Genotype-Tissue Expression) expression data. In terms of mean absolute error averaged across target genes, the proposed method significantly outperforms previous approaches in the case of the GEO expression data. CONCLUSIONS The experimental results report that the combination of clustering and regression can outperform the state-of-the art methods such as generative adversarial networks and a gradient boosting based method.
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Affiliation(s)
- Ho-Sik Seok
- Department of Computer Science and Engineering, Kangwon National University, Gangwon-do, Chuncheon-si, 24341, Korea.
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203
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Herrera-Uribe J, Wiarda JE, Sivasankaran SK, Daharsh L, Liu H, Byrne KA, Smith TPL, Lunney JK, Loving CL, Tuggle CK. Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing. Front Genet 2021; 12:689406. [PMID: 34249103 PMCID: PMC8261551 DOI: 10.3389/fgene.2021.689406] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/18/2021] [Indexed: 01/03/2023] Open
Abstract
Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.
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Affiliation(s)
- Juber Herrera-Uribe
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Jayne E. Wiarda
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
- Immunobiology Graduate Program, Iowa State University, Ames, IA, United States
- Oak Ridge Institute for Science and Education, Agricultural Research Service Participation Program, Oak Ridge, TN, United States
| | - Sathesh K. Sivasankaran
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
- Genome Informatics Facility, Iowa State University, Ames, IA, United States
| | - Lance Daharsh
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Haibo Liu
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Kristen A. Byrne
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
| | | | - Joan K. Lunney
- USDA-ARS, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory, Beltsville, MD, United States
| | - Crystal L. Loving
- Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, Ames, IA, United States
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204
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Lhee D, Lee J, Ettahi K, Cho CH, Ha JS, Chan YF, Zelzion U, Stephens TG, Price DC, Gabr A, Nowack ECM, Bhattacharya D, Yoon HS. Amoeba Genome Reveals Dominant Host Contribution to Plastid Endosymbiosis. Mol Biol Evol 2021; 38:344-357. [PMID: 32790833 PMCID: PMC7826189 DOI: 10.1093/molbev/msaa206] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Eukaryotic photosynthetic organelles, plastids, are the powerhouses of many aquatic and terrestrial ecosystems. The canonical plastid in algae and plants originated >1 Ga and therefore offers limited insights into the initial stages of organelle evolution. To address this issue, we focus here on the photosynthetic amoeba Paulinella micropora strain KR01 (hereafter, KR01) that underwent a more recent (∼124 Ma) primary endosymbiosis, resulting in a photosynthetic organelle termed the chromatophore. Analysis of genomic and transcriptomic data resulted in a high-quality draft assembly of size 707 Mb and 32,361 predicted gene models. A total of 291 chromatophore-targeted proteins were predicted in silico, 208 of which comprise the ancestral organelle proteome in photosynthetic Paulinella species with functions, among others, in nucleotide metabolism and oxidative stress response. Gene coexpression analysis identified networks containing known high light stress response genes as well as a variety of genes of unknown function (“dark” genes). We characterized diurnally rhythmic genes in this species and found that over 49% are dark. It was recently hypothesized that large double-stranded DNA viruses may have driven gene transfer to the nucleus in Paulinella and facilitated endosymbiosis. Our analyses do not support this idea, but rather suggest that these viruses in the KR01 and closely related P. micropora MYN1 genomes resulted from a more recent invasion.
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Affiliation(s)
- Duckhyun Lhee
- Department of Biological Sciences, Sungkyunkwan University, Suwon, Korea
| | - JunMo Lee
- Department of Oceanography, Kyungpook National University, Daegu, Korea
| | - Khaoula Ettahi
- Department of Biological Sciences, Sungkyunkwan University, Suwon, Korea
| | - Chung Hyun Cho
- Department of Biological Sciences, Sungkyunkwan University, Suwon, Korea
| | - Ji-San Ha
- Department of Biological Sciences, Sungkyunkwan University, Suwon, Korea
| | - Ya-Fan Chan
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ
| | - Udi Zelzion
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ
| | - Timothy G Stephens
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ
| | - Dana C Price
- Department of Entomology, Center for Vector Biology, Rutgers University, New Brunswick, NJ
| | - Arwa Gabr
- Microbiology and Molecular Genetics Graduate Program, Rutgers University, New Brunswick, NJ
| | - Eva C M Nowack
- Institut für Mikrobielle Zellbiologie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | | | - Hwan Su Yoon
- Department of Biological Sciences, Sungkyunkwan University, Suwon, Korea
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205
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Zhang Y, Chatzistamou I, Kiaris H. Transcriptomic coordination at hepatic steatosis indicates robust immune cell engagement prior to inflammation. BMC Genomics 2021; 22:454. [PMID: 34134614 PMCID: PMC8210377 DOI: 10.1186/s12864-021-07784-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/07/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Deregulation in lipid metabolism leads to the onset of hepatic steatosis while at subsequent stages of disease development, the induction of inflammation, marks the transition of steatosis to non-alcoholic steatohepatitis. While differential gene expression unveils individual genes that are deregulated at different stages of disease development, how the whole transcriptome is deregulated in steatosis remains unclear. METHODS Using outbred deer mice fed with high fat as a model, we assessed the correlation of each transcript with every other transcript in the transcriptome. The onset of steatosis in the liver was also evaluated histologically. RESULTS Our results indicate that transcriptional reprogramming directing immune cell engagement proceeds robustly, even in the absence of histologically detectable steatosis, following administration of high fat diet. In the liver transcriptomes of animals with steatosis, a preference for the engagement of regulators of T cell activation and myeloid leukocyte differentiation was also recorded as opposed to the steatosis-free livers at which non-specific lymphocytic activation was seen. As compared to controls, in the animals with steatosis, transcriptome was subjected to more widespread reorganization while in the animals without steatosis, reorganization was less extensive. Comparison of the steatosis and non-steatosis livers showed high retention of coordination suggesting that diet supersedes pathology in shaping the transcriptome's profile. CONCLUSIONS This highly versatile strategy suggests that the molecular changes inducing inflammation proceed robustly even before any evidence of steatohepatitis is recorded, either histologically or by differential expression analysis.
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Affiliation(s)
- Youwen Zhang
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, CLS 713, 715 Sumter Str., Columbia, SC, 29208-3402, USA
| | - Ioulia Chatzistamou
- Department of Pathology, Microbiology and Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA
| | - Hippokratis Kiaris
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, CLS 713, 715 Sumter Str., Columbia, SC, 29208-3402, USA.
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA.
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206
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Sun B, Guo X, Wen X, Xie YB, Liu WH, Pang GF, Yang LY, Zhang Q. Application of weighted gene co-expression network analysis to identify the hub genes in H1N1. INTERNATIONAL JOURNAL OF PHYSIOLOGY, PATHOPHYSIOLOGY AND PHARMACOLOGY 2021; 13:69-85. [PMID: 34336131 PMCID: PMC8310883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/01/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Identifying the disease-associated interactions between different genes helps us to find novel therapeutic targets and predictive biomarkers. METHODS Gene expression data GSE82050 from H1N1 and control human samples were acquired from the NCBI GEO database. Highly co-expressed genes were grouped into modules. Through Person's correlation coefficient calculation between the module and clinical phenotype, notable modules were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted, and the hub genes within the module of interest were identified. Also, gene expression data GSE27131 were acquired from the GEO database to verify differential key gene expression analysis. The CIBERSORT was used to evaluate the immune cells infiltration and the GSVA was performed to identify the differentially regulated pathways in H1N1. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes. RESULT The black module was shown to have the highest correlation with the clinical phenotype, mainly functioning in the signaling pathways such as the mitochondrial inner membrane, DNA conformation change, DNA repair, and cell cycle phase transition. Through analysis of the black module, we found 5 genes that were highly correlated with the H1N1 phenotype. The H1N1 project from GSE27131 confirmed an increased expression of these genes. CONCLUSION By using the WGCNA we analyzed and predicted the key genes in H1N1. BRCA1, CDC20, MAD2L1, MCM2, and UBE2C were found to be the most relevant genes, which may be therapeutic targets and predictive biomarkers for H1N1 therapy.
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Affiliation(s)
- Bo Sun
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Xiang Guo
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Xue Wen
- Department of Hematology, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Yun-Bo Xie
- Department of Geriatrics, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Wei-Hua Liu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Gui-Fen Pang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Lin-Ying Yang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
| | - Qing Zhang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Chengde Medical UniversityChengde 067000, P. R. China
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207
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Xie S, Zeng Q, Ouyang S, Liang Y, Xiao C. Bioinformatics analysis of epigenetic and SNP-related molecular markers in systemic lupus erythematosus. Am J Transl Res 2021; 13:6312-6329. [PMID: 34306371 PMCID: PMC8290799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/23/2021] [Indexed: 06/13/2023]
Abstract
We analyzed gene expression in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE) using public databases. The goal was to identify lupus biomarkers by determining whether differentially expressed genes are mediated by methylation, miRNA, or SNP. Two cDNA microarrays were subjected to integration analysis, and we calculated the mutually differentially expressed genes (|log2fold change (FC)| > 1, P < 0.05). These genes were analyzed using gene otology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. The differences in methylation sites for two methylation chips were calculated and the differentially methylated sites were annotated. These genes were compared to the differentially expressed genes. We obtained 135 differentially expressed microRNAs from the microRNA-chip results using PBMCs from SLE and healthy individuals. Predictive microRNA target genes were identified using GO, KEGG pathways, and PPI networks. The target genes identified were compared to the differentially expressed genes. We downloaded Chinese SLE genome-wide association study data from SLE-related literature, analyzed the loci with a P value < 0.05, and used annotated SLE-associated SNPs. We selected the genes corresponding to an SNP located on an exon and determined the intersection with the differentially expressed genes. We found 18 differentially expressed genes in both cDNA microarrays. The methylation chips had 50 corresponding methylation sites. On the basis of these results, we identified two genes, IFI44 and IFI44L. We further identified 135 differentially expressed microRNAs predicted to affect 5766 target genes. Two identified genes were in common with the differentially expressed genes. Finally, SNP annotated genes and cDNA chip genes overlap with identified MX1. Therefore, we used existing data to analyze the causes of differential gene expression in SLE, introducing new methods for determining biomarkers and therapeutic targets.
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Affiliation(s)
- Shuoshan Xie
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Qinghua Zeng
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Shaxi Ouyang
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Yumei Liang
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
| | - Changjuan Xiao
- Nephrology Department and Laboratory of Kidney Disease, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal UniversityChangsha, PR China
- Changsha Clinical Research Center for Kidney DiseaseChangsha, PR China
- Hunan Clinical Research Center for Chronic Kidney DiseaseChangsha, PR China
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208
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Chang J, Lurie RH, Sharma A, Bashir M, Fung CM, Dettman RW, Dizon MLV. Intrauterine growth restriction followed by oxygen support uniquely interferes with genetic regulators of myelination. eNeuro 2021; 8:ENEURO.0263-20.2021. [PMID: 34099489 PMCID: PMC8266217 DOI: 10.1523/eneuro.0263-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 03/23/2021] [Accepted: 04/23/2021] [Indexed: 12/24/2022] Open
Abstract
Intrauterine growth restriction (IUGR) and oxygen exposure in isolation and combination adversely affect the developing brain, putting infants at risk for neurodevelopmental disability including cerebral palsy. Rodent models of IUGR and postnatal hyperoxia have demonstrated oligodendroglial injury with subsequent white matter injury (WMI) and motor dysfunction. Here we investigate transcriptomic dysregulation in IUGR with and without hyperoxia exposure to account for the abnormal brain structure and function previously documented. We performed RNA sequencing and analysis using a mouse model of IUGR and found that IUGR, hyperoxia, and the combination of IUGR with hyperoxia (IUGR/hyperoxia) produced distinct changes in gene expression. IUGR in isolation demonstrated the fewest differentially expressed genes compared to control. In contrast, we detected several gene alterations in IUGR/hyperoxia; genes involved in myelination were strikingly downregulated. We also identified changes to specific regulators including TCF7L2, BDNF, SOX2, and DGCR8, through Ingenuity Pathway Analysis, that may contribute to impaired myelination in IUGR/hyperoxia. Our findings show that IUGR with hyperoxia induces unique transcriptional changes in the developing brain. These indicate mechanisms for increased risk for WMI in IUGR infants exposed to oxygen and suggest potential therapeutic targets to improve motor outcomes.Significance StatementThis study demonstrates that perinatal exposures of IUGR and/or postnatal hyperoxia result in distinct transcriptomic changes in the developing brain. In particular, we found that genes involved in normal developmental myelination, myelin maintenance, and remyelination were most dysregulated when IUGR was combined with hyperoxia. Understanding how multiple risk factors lead to WMI is the first step in developing future therapeutic interventions. Additionally, because oxygen exposure is often unavoidable after birth, an understanding of gene perturbations in this setting will increase our awareness of the need for tight control of oxygen use to minimize future motor disability.
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Affiliation(s)
- Jill Chang
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
| | - Robert H Lurie
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
| | - Abhineet Sharma
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
| | - Mirrah Bashir
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
| | - Camille M Fung
- University of Utah, Department of Pediatrics, Salt Lake City, Utah, USA
| | - Robert W Dettman
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
| | - Maria L V Dizon
- Children's Hospital of Chicago, Department of Pediatrics, Division of Neonatology, Chicago, Illinois, USA
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209
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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210
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Zhang PY, Qiu X, Fu JX, Wang GR, Wei L, Wang TC. Systematic analysis of differentially expressed ZmMYB genes related to drought stress in maize. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2021; 27:1295-1309. [PMID: 34177148 PMCID: PMC8212317 DOI: 10.1007/s12298-021-01013-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 05/08/2023]
Abstract
UNLABELLED MYB transcription factors play pivotal roles in hormone conduction signaling and abiotic stress response. In this study, 54 differentially expressed ZmMYB genes were identified and comprehensive analyses were conducted including gene's structure, chromosomal localization, phylogenetic tree, motif prediction, cis-elements and expression patterns. The results showed that 54 genes were unevenly distributed on 10 chromosomes and classified into eleven main subgroups by phylogenetic analysis, supported by motif and exon/intron analyses. The mainly stress-related cis-elements were ABRE, ARE, MBS and DRE-core. In addition, 8 core ZmMYB genes were identified by co-expression network. qRT-PCR results showed that the 8 ZmMYB genes exhibited different expression levels under different abiotic stresses, indicating that they were responsive to various abiotic stress. These results will provide insight for further functional investigation of ZmMYB genes. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-021-01013-2.
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Affiliation(s)
- Peng-Yu Zhang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
| | - Xiao Qiu
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
| | - Jia-Xu Fu
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
| | - Guo-Rui Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
| | - Li Wei
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
| | - Tong-Chao Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou, 450046 China
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211
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Deane CS, Willis CRG, Phillips BE, Atherton PJ, Harries LW, Ames RM, Szewczyk NJ, Etheridge T. Transcriptomic meta-analysis of disuse muscle atrophy vs. resistance exercise-induced hypertrophy in young and older humans. J Cachexia Sarcopenia Muscle 2021; 12:629-645. [PMID: 33951310 PMCID: PMC8200445 DOI: 10.1002/jcsm.12706] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/26/2021] [Accepted: 03/29/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Skeletal muscle atrophy manifests across numerous diseases; however, the extent of similarities/differences in causal mechanisms between atrophying conditions in unclear. Ageing and disuse represent two of the most prevalent and costly atrophic conditions, with resistance exercise training (RET) being the most effective lifestyle countermeasure. We employed gene-level and network-level meta-analyses to contrast transcriptomic signatures of disuse and RET, plus young and older RET to establish a consensus on the molecular features of, and therapeutic targets against, muscle atrophy in conditions of high socio-economic relevance. METHODS Integrated gene-level and network-level meta-analysis was performed on publicly available microarray data sets generated from young (18-35 years) m. vastus lateralis muscle subjected to disuse (unilateral limb immobilization or bed rest) lasting ≥7 days or RET lasting ≥3 weeks, and resistance-trained older (≥60 years) muscle. RESULTS Disuse and RET displayed predominantly separate transcriptional responses, and transcripts altered across conditions were mostly unidirectional. However, disuse and RET induced directly inverted expression profiles for mitochondrial function and translation regulation genes, with COX4I1, ENDOG, GOT2, MRPL12, and NDUFV2, the central hub components of altered mitochondrial networks, and ZMYND11, a hub gene of altered translation regulation. A substantial number of genes (n = 140) up-regulated post-RET in younger muscle were not similarly up-regulated in older muscle, with young muscle displaying a more pronounced extracellular matrix (ECM) and immune/inflammatory gene expression response. Both young and older muscle exhibited similar RET-induced ubiquitination/RNA processing gene signatures with associated PWP1, PSMB1, and RAF1 hub genes. CONCLUSIONS Despite limited opposing gene profiles, transcriptional signatures of disuse are not simply the converse of RET. Thus, the mechanisms of unloading cannot be derived from studying muscle loading alone and provides a molecular basis for understanding why RET fails to target all transcriptional features of disuse. Loss of RET-induced ECM mechanotransduction and inflammatory profiles might also contribute to suboptimal ageing muscle adaptations to RET. Disuse and age-dependent molecular candidates further establish a framework for understanding and treating disuse/ageing atrophy.
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Affiliation(s)
- Colleen S Deane
- Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, St. Luke's Campus, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK
| | - Craig R G Willis
- Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, St. Luke's Campus, Exeter, UK
| | - Bethan E Phillips
- MRC-ARUK Centre for Musculoskeletal Ageing Research and National Institute of Health Research, Biomedical Research Centre, Division of Medical Sciences and Graduate Entry Medicine, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby, UK
| | - Philip J Atherton
- MRC-ARUK Centre for Musculoskeletal Ageing Research and National Institute of Health Research, Biomedical Research Centre, Division of Medical Sciences and Graduate Entry Medicine, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby, UK
| | - Lorna W Harries
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ryan M Ames
- Living Systems Institute, University of Exeter, Exeter, UK
| | - Nathaniel J Szewczyk
- MRC-ARUK Centre for Musculoskeletal Ageing Research and National Institute of Health Research, Biomedical Research Centre, Division of Medical Sciences and Graduate Entry Medicine, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby, UK.,Ohio Musculoskeletal and Neurological Institute & Department of Biomedical Sciences, Ohio University, Athens, OH, USA
| | - Timothy Etheridge
- Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, St. Luke's Campus, Exeter, UK
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212
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Lemoine GG, Scott-Boyer MP, Ambroise B, Périn O, Droit A. GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package. BMC Bioinformatics 2021; 22:267. [PMID: 34034647 PMCID: PMC8152313 DOI: 10.1186/s12859-021-04179-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/07/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. RESULTS Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. CONCLUSION GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.
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Affiliation(s)
- Gwenaëlle G. Lemoine
- Département de médecine moléculaire, Faculté de médecine, Université Laval, 2325 rue de l’Université, Québec, G1V 0A6 Canada
| | - Marie-Pier Scott-Boyer
- Centre de recherche du Chu de Quebec-Université Laval, 2705 boulevard Laurier Québec, Québec, G1V 4G2 Canada
| | - Bathilde Ambroise
- L’Oréal Research and Innovation, 15 rue Pierre Dreyfus, 92110 Clichy, France
| | - Olivier Périn
- L’Oréal Research and Innovation, 15 rue Pierre Dreyfus, 92110 Clichy, France
| | - Arnaud Droit
- Département de médecine moléculaire, Faculté de médecine, Université Laval, 2325 rue de l’Université, Québec, G1V 0A6 Canada
- Centre de recherche du Chu de Quebec-Université Laval, 2705 boulevard Laurier Québec, Québec, G1V 4G2 Canada
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213
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Arbet J, Zhuang Y, Litkowski E, Saba L, Kechris K. Comparing Statistical Tests for Differential Network Analysis of Gene Modules. Front Genet 2021; 12:630215. [PMID: 34093641 PMCID: PMC8170128 DOI: 10.3389/fgene.2021.630215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.
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Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Yaxu Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Elizabeth Litkowski
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laura Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora CO, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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214
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Sudhakar P, Verstockt B, Cremer J, Verstockt S, Sabino J, Ferrante M, Vermeire S. Understanding the Molecular Drivers of Disease Heterogeneity in Crohn's Disease Using Multi-omic Data Integration and Network Analysis. Inflamm Bowel Dis 2021; 27:870-886. [PMID: 33313682 PMCID: PMC8128416 DOI: 10.1093/ibd/izaa281] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Indexed: 12/12/2022]
Abstract
Crohn's disease (CD), a form of inflammatory bowel disease (IBD), is characterized by heterogeneity along multiple clinical axes, which in turn impacts disease progression and treatment modalities. Using advanced data integration approaches and systems biology tools, we studied the contribution of CD susceptibility variants and gene expression in distinct peripheral immune cell subsets (CD14+ monocytes and CD4+ T cells) to relevant clinical traits. Our analyses revealed that most clinical traits capturing CD heterogeneity could be associated with CD14+ and CD4+ gene expression rather than disease susceptibility variants. By disentangling the sources of variation, we identified molecular features that could potentially be driving the heterogeneity of various clinical traits of CD patients. Further downstream analyses identified contextual hub proteins such as genes encoding barrier functions, antimicrobial peptides, chemokines, and their receptors, which are either targeted by drugs used in CD or other inflammatory diseases or are relevant to the biological functions implicated in disease pathology. These hubs could be used as cell type-specific targets to treat specific subtypes of CD patients in a more individualized approach based on the underlying biology driving their disease subtypes. Our study highlights the importance of data integration and systems approaches to investigate complex and heterogeneous diseases such as IBD.
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Affiliation(s)
- Padhmanand Sudhakar
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
| | - Bram Verstockt
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
- University Hospitals Leuven, Department of Gastroenterology and Hepatology
| | - Jonathan Cremer
- Department of Microbiology and Immunology, Laboratory of Clinical Immunology, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Sare Verstockt
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
| | - João Sabino
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
- University Hospitals Leuven, Department of Gastroenterology and Hepatology
| | - Marc Ferrante
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
- University Hospitals Leuven, Department of Gastroenterology and Hepatology
| | - Séverine Vermeire
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID)
- University Hospitals Leuven, Department of Gastroenterology and Hepatology
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215
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Bando SY, Bertonha FB, Pimentel-Silva LR, de Oliveira JGM, Carneiro MAD, Oku MHM, Wen HT, Castro LHM, Moreira-Filho CA. Hippocampal CA3 transcriptional modules associated with granule cell alterations and cognitive impairment in refractory mesial temporal lobe epilepsy patients. Sci Rep 2021; 11:10257. [PMID: 33986407 PMCID: PMC8119682 DOI: 10.1038/s41598-021-89802-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/30/2021] [Indexed: 01/03/2023] Open
Abstract
In about a third of the patients with epilepsy the seizures are not drug-controlled. The current limitation of the antiepileptic drug therapy derives from an insufficient understanding of epilepsy pathophysiology. In order to overcome this situation, it is necessary to consider epilepsy as a disturbed network of interactions, instead of just looking for changes in single molecular components. Here, we studied CA3 transcriptional signatures and dentate gyrus histopathologic alterations in hippocampal explants surgically obtained from 57 RMTLE patients submitted to corticoamygdalohippocampectomy. By adopting a systems biology approach, integrating clinical, histopathological, and transcriptomic data (weighted gene co-expression network analysis), we were able to identify transcriptional modules highly correlated with age of disease onset, cognitive dysfunctions, and granule cell alterations. The enrichment analysis of transcriptional modules and the functional characterization of the highly connected genes in each trait-correlated module allowed us to unveil the modules’ main biological functions, paving the way for further investigations on their roles in RMTLE pathophysiology. Moreover, we found 15 genes with high gene significance values which have the potential to become novel biomarkers and/or therapeutic targets in RMTLE.
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Affiliation(s)
- Silvia Yumi Bando
- Department of Pediatrics, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, 05403-900, Brazil
| | - Fernanda Bernardi Bertonha
- Department of Pediatrics, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, 05403-900, Brazil
| | - Luciana Ramalho Pimentel-Silva
- Department of Neurology, Faculdade de Ciências Médicas da Universidade Estadual de Campinas, UNICAMP, Campinas, SP, 13083-887, Brazil
| | | | | | - Mariana Hiromi Manoel Oku
- Department of Neurology, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, 05403-900, Brazil
| | - Hung-Tzu Wen
- Epilepsy Surgery Group, Hospital das Clínicas da FMUSP, São Paulo, SP, 05403-900, Brazil
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216
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Hall TJ, Mullen MP, McHugo GP, Killick KE, Ring SC, Berry DP, Correia CN, Browne JA, Gordon SV, MacHugh DE. Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria. BMC Genomics 2021; 22:343. [PMID: 33980141 PMCID: PMC8117616 DOI: 10.1186/s12864-021-07643-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/22/2021] [Indexed: 12/13/2022] Open
Abstract
Background Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. Results The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. Conclusions The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07643-w.
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Affiliation(s)
- Thomas J Hall
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland
| | - Michael P Mullen
- Bioscience Research Institute, Athlone Institute of Technology, Dublin Road, Athlone, Westmeath, N37 HD68, Ireland
| | - Gillian P McHugo
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland
| | - Kate E Killick
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.,Present address: Genuity Science, Cherrywood Business Park. Loughlinstown, Dublin, D18 K7W4, Ireland
| | - Siobhán C Ring
- Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon, Cork, P72 X050, Ireland
| | - Donagh P Berry
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Cork, P61 C996, Ireland
| | - Carolina N Correia
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland
| | - John A Browne
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland
| | - Stephen V Gordon
- UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.,UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland
| | - David E MacHugh
- Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. .,UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.
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217
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Sabik OL, Calabrese GM, Taleghani E, Ackert-Bicknell CL, Farber CR. Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data. Cell Rep 2021; 32:108145. [PMID: 32937138 DOI: 10.1016/j.celrep.2020.108145] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/31/2020] [Accepted: 08/21/2020] [Indexed: 12/12/2022] Open
Abstract
The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.
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Affiliation(s)
- Olivia L Sabik
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Gina M Calabrese
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Eric Taleghani
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Cheryl L Ackert-Bicknell
- Center for Musculoskeletal Research, University of Rochester Medical Center, University of Rochester, Rochester, NY 14624, USA
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
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218
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Zhao L, Dong Q, Luo C, Wu Y, Bu D, Qi X, Luo Y, Zhao Y. DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis. Comput Struct Biotechnol J 2021; 19:2719-2725. [PMID: 34093987 PMCID: PMC8131983 DOI: 10.1016/j.csbj.2021.04.067] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 01/23/2023] Open
Abstract
Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of variables from different omics datasets and incorporates prior biological information defined by users (such as signaling pathways and tissue networks). Benchmark experiments demonstrate that DeepOmix outperforms the other five cutting-edge prediction methods. Besides, Lower Grade Glioma (LGG) is taken as the case study to perform the prognosis prediction and illustrate the functional module nodes which are associated with the prognostic result in the prediction model.
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Affiliation(s)
- Lianhe Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiongye Dong
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Chunlong Luo
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoning Qi
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufan Luo
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315000, China
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219
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Yu H, Guo Y, Chen J, Chen X, Jia P, Zhao Z. Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods. Genes (Basel) 2021; 12:665. [PMID: 33946654 PMCID: PMC8146818 DOI: 10.3390/genes12050665] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 02/03/2023] Open
Abstract
Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.
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Affiliation(s)
- Hui Yu
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Yan Guo
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; (H.Y.); (Y.G.)
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Xiangning Chen
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV 89154, USA; (J.C.); (X.C.)
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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220
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Capelle CM, Zeng N, Danileviciute E, Rodrigues SF, Ollert M, Balling R, He FQ. Identification of VIMP as a gene inhibiting cytokine production in human CD4+ effector T cells. iScience 2021; 24:102289. [PMID: 33851102 PMCID: PMC8024663 DOI: 10.1016/j.isci.2021.102289] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 02/08/2021] [Accepted: 03/05/2021] [Indexed: 12/14/2022] Open
Abstract
Many players regulating the CD4+ T cell-mediated inflammatory response have already been identified. However, the critical nodes that constitute the regulatory and signaling networks underlying CD4 T cell responses are still missing. Using a correlation-network-guided approach, here we identified VIMP (VCP-interacting membrane protein), one of the 25 genes encoding selenoproteins in humans, as a gene regulating the effector functions of human CD4 T cells, especially production of several cytokines including IL2 and CSF2. We identified VIMP as an endogenous inhibitor of cytokine production in CD4 effector T cells via both the E2F5 transcription regulatory pathway and the Ca2+/NFATC2 signaling pathway. Our work not only indicates that VIMP might be a promising therapeutic target for various inflammation-associated diseases but also shows that our network-guided approach can significantly aid in predicting new functions of the genes of interest.
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Affiliation(s)
- Christophe M. Capelle
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine, University of Luxembourg, 2, avenue de Université, 4365 Esch-sur-Alzette, Luxembourg
| | - Ni Zeng
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
| | - Egle Danileviciute
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
| | - Sabrina Freitas Rodrigues
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
- Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, 5000 C, Denmark
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
| | - Feng Q. He
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, 4367 Belvaux, Luxembourg
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, 45122 Essen, Germany
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221
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Soltanmohammadi E, Zhang Y, Chatzistamou I, Kiaris H. Resilience, plasticity and robustness in gene expression during aging in the brain of outbred deer mice. BMC Genomics 2021; 22:291. [PMID: 33882817 PMCID: PMC8061204 DOI: 10.1186/s12864-021-07613-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/13/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Genes that belong to the same network are frequently co-expressed, but collectively, how the coordination of the whole transcriptome is perturbed during aging remains unclear. To explore this, we calculated the correlation of each gene in the transcriptome with every other, in the brain of young and older outbred deer mice (P. leucopus and P. maniculatus). RESULTS In about 25 % of the genes, coordination was inversed during aging. Gene Ontology analysis in both species, for the genes that exhibited inverse transcriptomic coordination during aging pointed to alterations in the perception of smell, a known impairment occurring during aging. In P. leucopus, alterations in genes related to cholesterol metabolism were also identified. Among the genes that exhibited the most pronounced inversion in their coordination profiles during aging was THBS4, that encodes for thrombospondin-4, a protein that was recently identified as rejuvenation factor in mice. Relatively to its breadth, abolishment of coordination was more prominent in the long-living P. leucopus than in P. maniculatus but in the latter, the intensity of de-coordination was higher. CONCLUSIONS There sults suggest that aging is associated with more stringent retention of expression profiles for some genes and more abrupt changes in others, while more subtle but widespread changes in gene expression appear protective. Our findings shed light in the mode of the transcriptional changes occurring in the brain during aging and suggest that strategies aiming to broader but more modest changes in gene expression may be preferrable to correct aging-associated deregulation in gene expression.
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Affiliation(s)
- E Soltanmohammadi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, SC, Columbia, USA
| | - Y Zhang
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, SC, Columbia, USA
| | - I Chatzistamou
- Department of Pathology, Microbiology and Immunology, School of Medicine, University of South Carolina, SC, Columbia, USA
| | - H Kiaris
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, SC, Columbia, USA.
- Peromyscus Genetic Stock Center, University of South Carolina, SC, Columbia, USA.
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222
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García-Cortés D, Hernández-Lemus E, Espinal-Enríquez J. Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations. Front Genet 2021; 12:629475. [PMID: 33959148 PMCID: PMC8096206 DOI: 10.3389/fgene.2021.629475] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 03/17/2021] [Indexed: 12/20/2022] Open
Abstract
Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.
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Affiliation(s)
- Diana García-Cortés
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
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223
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Jaemthaworn T, Kalapanulak S, Saithong T. Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava. Sci Rep 2021; 11:7872. [PMID: 33846415 PMCID: PMC8041763 DOI: 10.1038/s41598-021-86806-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 03/19/2021] [Indexed: 02/01/2023] Open
Abstract
Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.
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Affiliation(s)
- Thanakorn Jaemthaworn
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
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224
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Chang X, Li D, Liu C, Zhang Z, Wang T. Pentraxin 3 is a diagnostic and prognostic marker for ovarian epithelial cancer patients based on comprehensive bioinformatics and experiments. Cancer Cell Int 2021; 21:193. [PMID: 33952272 PMCID: PMC8097951 DOI: 10.1186/s12935-021-01854-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/20/2021] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Ovarian epithelial cancer is one of the leading malignant tumors in gynecology and lacks effective diagnostic and prognostic markers. Our study aims to screen and verify ovarian epithelial cancer biomarkers. METHODS GSE18520 and GSE26712 were downloaded from the GEO database. The "limma" and "WGCNA" packages were used to explore hub genes. The Kaplan-Meier Plotter database was used for survival analysis of the hub genes. Immunohistochemical analysis was used to identify the expression level of Pentraxin 3 in ovarian epithelial cancer samples. RESULTS In this study, we integrated and analyzed two datasets, GSE18520 and GSE26712, and a total of 238 differentially expressed genes (DEGs) were screened out. Enrichment analysis showed that these DEGs were related to collagen-containing extracellular matrix and other pathways. Further application of WGCNA (weighted gene coexpression network analysis) identified 15 gene modules, with the purple module showing the highest correlation with ovarian epithelial cancer. Twenty-five genes were shared between the purple module and DEGs, 13 genes were related to the prognosis of ovarian epithelial cancer patients, and the PTX3 gene had the highest hazardous risk (HR) value. We performed immunohistochemical analyses on the 255 Pentraxin-3 (PTX3)-based clinical samples. PTX3 was found to be overexpressed in ovarian epithelial cancer and related to the degree of differentiation. The Cox proportional hazard model indicates that high PTX3 expression is an independent risk factor for the prognosis of ovarian epithelial cancer patients. CONCLUSIONS In conclusion, through WGCNA and a series of comprehensive bioinformatics analyses, PTX3 was first identified as a novel diagnostic and prognostic biomarker for ovarian epithelial cancer.
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Affiliation(s)
- Xiaoying Chang
- Department of Pathology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping, Shenyang, 110004, China
| | - Dan Li
- Department of Pathology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping, Shenyang, 110004, China
| | - Chang Liu
- Department of Pathology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping, Shenyang, 110004, China
| | - Zhe Zhang
- Department of Pathology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping, Shenyang, 110004, China
| | - Tao Wang
- Department of Pathology, Shenyang KingMed Center for Clinical Laboratory Co., Ltd, Shenyang, 110164, China.
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225
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Mishra B, Athar M, Mukhtar MS. Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity. Sci Rep 2021; 11:7373. [PMID: 33795737 PMCID: PMC8016976 DOI: 10.1038/s41598-021-86742-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/12/2021] [Indexed: 02/07/2023] Open
Abstract
Macrophages are ubiquitous custodians of tissues, which play decisive role in maintaining cellular homeostasis through regulatory immune responses. Within tissues, macrophage exhibit extremely heterogeneous population with varying functions orchestrated through regulatory response, which can be further exacerbated in diverse genetic backgrounds. Gene regulatory networks (GRNs) offer comprehensive understanding of cellular regulatory behavior by unfolding the transcription factors (TFs) and regulated target genes. RNA-Seq coupled with ATAC-Seq has revolutionized the regulome landscape influenced by gene expression modeling. Here, we employ an integrative multi-omics systems biology-based analysis and generated GRNs derived from the unstimulated bone marrow-derived macrophages of five inbred genetically defined murine strains, which are reported to be linked with most of the population-wide human genetic variants. Our probabilistic modeling of a basal hemostasis pan regulatory repertoire in diverse macrophages discovered 96 TFs targeting 6279 genes representing 468,291 interactions across five inbred murine strains. Subsequently, we identify core and distinctive GRN sub-networks in unstimulated macrophages to describe the system-wide conservation and dissimilarities, respectively across five murine strains. Our study concludes that discrepancies in unstimulated macrophage-specific regulatory networks not only drives the basal functional plasticity within genetic backgrounds, additionally aid in understanding the complexity of racial disparity among the human population during stress.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA
| | - Mohammad Athar
- UAB Research Center of Excellence in Arsenicals, Department of Dermatology, School of Medicine, University of Alabama At Birmingham, Alabama, 35294, USA.
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA. .,Nutrition Obesity Research Center, University of Alabama At Birmingham, 1675 University Blvd, Birmingham, AL, 35294, USA. .,Department of Surgery, University of Alabama At Birmingham, 1808 7th Ave S, Birmingham, AL, 35294, USA.
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226
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Du X, Zhang C, Yin C, Wang W, Yan X, Xie D, Zheng X, Zheng Q, Li M, Song Z. High BLM Expression Predicts Poor Clinical Outcome and Contributes to Malignant Progression in Human Cholangiocarcinoma. Front Oncol 2021; 11:633899. [PMID: 33828983 PMCID: PMC8019910 DOI: 10.3389/fonc.2021.633899] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/09/2021] [Indexed: 12/24/2022] Open
Abstract
Molecular mechanisms underlying the tumorigenesis of a highly malignant cancer, cholangiocarcinoma (CCA), are still obscure. In our study, the CCA expression profile data were acquired from The Cancer Genome Atlas (TCGA) database, and differentially expressed genes (DEGs) in the TCGA-Cholangiocarcinoma (TCGA-CHOL) data set were utilized to construct a co-expression network via weighted gene co-expression network analysis (WGCNA). The blue gene module associated with the histopathologic grade of CCA was screened. Then, five candidate hub genes were screened by combining the co-expression network with protein–protein interaction (PPI) network. After progression and survival analyses, bloom syndrome helicase (BLM) was ultimately identified as a real hub gene. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that BLM had a favorable diagnostic and predictive recurrence value for CCA. The gene set enrichment analysis (GSEA) results for a single hub gene revealed the importance of cell cycle-related pathways in the CCA progression and prognosis. Furthermore, we detected the BLM expression in vitro, and the results demonstrated that the expression level of BLM was much higher in the CCA tissues and cells relative to adjacent non-tumor samples and normal bile duct epithelial cells. Additionally, after further silencing the BLM expression by small interfering RNA (siRNA), the proliferation and migration ability of CCA cells were all inhibited, and the cell cycle was arrested. Altogether, a real hub gene (BLM) and cell cycle-related pathways were identified in the present study, and the gene BLM may be involved in the CCA progression and could act as a reliable biomarker for potential diagnosis and prognostic evaluation.
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Affiliation(s)
- Xiaolong Du
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chen Zhang
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuanzheng Yin
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenjie Wang
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xueke Yan
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dawei Xie
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xichuan Zheng
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qichang Zheng
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Li
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zifang Song
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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227
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Sequential progenitor states mark the generation of pancreatic endocrine lineages in mice and humans. Cell Res 2021; 31:886-903. [PMID: 33692492 DOI: 10.1038/s41422-021-00486-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 01/27/2021] [Indexed: 12/12/2022] Open
Abstract
The pancreatic islet contains multiple hormone+ endocrine lineages (α, β, δ, PP and ε cells), but the developmental processes that underlie endocrinogenesis are poorly understood. Here, we generated novel mouse lines and combined them with various genetic tools to enrich all types of hormone+ cells for well-based deep single-cell RNA sequencing (scRNA-seq), and gene coexpression networks were extracted from the generated data for the optimization of high-throughput droplet-based scRNA-seq analyses. These analyses defined an entire endocrinogenesis pathway in which different states of endocrine progenitor (EP) cells sequentially differentiate into specific endocrine lineages in mice. Subpopulations of the EP cells at the final stage (EP4early and EP4late) show different potentials for distinct endocrine lineages. ε cells and an intermediate cell population were identified as distinct progenitors that independently generate both α and PP cells. Single-cell analyses were also performed to delineate the human pancreatic endocrinogenesis process. Although the developmental trajectory of pancreatic lineages is generally conserved between humans and mice, clear interspecies differences, including differences in the proportions of cell types and the regulatory networks associated with the differentiation of specific lineages, have been detected. Our findings support a model in which sequential transient progenitor cell states determine the differentiation of multiple cell lineages and provide a blueprint for directing the generation of pancreatic islets in vitro.
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228
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Andonegui-Elguera SD, Zamora-Fuentes JM, Espinal-Enríquez J, Hernández-Lemus E. Loss of Long Distance Co-Expression in Lung Cancer. Front Genet 2021; 12:625741. [PMID: 33777098 PMCID: PMC7987938 DOI: 10.3389/fgene.2021.625741] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.
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Affiliation(s)
| | | | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
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229
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Geng R, Huang X. Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity. BMC Med Genomics 2021; 14:55. [PMID: 33622334 PMCID: PMC7903654 DOI: 10.1186/s12920-021-00908-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 02/17/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets. METHODS Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/ ) was used for gene interaction relationship mapping. RESULTS We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. CONCLUSION Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.
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Affiliation(s)
- Ruijie Geng
- Department of Psychological Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Psychological Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, 361015, China
| | - Xiao Huang
- Department of Psychological Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Psychological Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, 361015, China.
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230
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Jeon M, Jagodnik KM, Kropiwnicki E, Stein DJ, Ma'ayan A. Prioritizing Pain-Associated Targets with Machine Learning. Biochemistry 2021; 60:1430-1446. [PMID: 33606503 DOI: 10.1021/acs.biochem.0c00930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.
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Affiliation(s)
- Minji Jeon
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Kathleen M Jagodnik
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Eryk Kropiwnicki
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Daniel J Stein
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
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Luu Trinh MD, Miyazaki D, Ono S, Nomata J, Kono M, Mino H, Niwa T, Okegawa Y, Motohashi K, Taguchi H, Hisabori T, Masuda S. The evolutionary conserved iron-sulfur protein TCR controls P700 oxidation in photosystem I. iScience 2021; 24:102059. [PMID: 33554065 PMCID: PMC7848650 DOI: 10.1016/j.isci.2021.102059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/08/2020] [Accepted: 01/08/2021] [Indexed: 11/21/2022] Open
Abstract
In natural habitats, plants have developed sophisticated regulatory mechanisms to optimize the photosynthetic electron transfer rate at the maximum efficiency and cope with the changing environments. Maintaining proper P700 oxidation at photosystem I (PSI) is the common denominator for most regulatory processes of photosynthetic electron transfers. However, the molecular complexes and cofactors involved in these processes and their function(s) have not been fully clarified. Here, we identified a redox-active chloroplast protein, the triplet-cysteine repeat protein (TCR). TCR shared similar expression profiles with known photosynthetic regulators and contained two triplet-cysteine motifs (CxxxCxxxC). Biochemical analysis indicated that TCR localizes in chloroplasts and has a [3Fe-4S] cluster. Loss of TCR limited the electron sink downstream of PSI during dark-to-light transition. Arabidopsis pgr5-tcr double mutant reduced growth significantly and showed unusual oxidation and reduction of plastoquinone pool. These results indicated that TCR is involved in electron flow(s) downstream of PSI, contributing to P700 oxidation. P700 oxidation at photosystem I is important for regulation of photosynthesis TCR is a redox active chloroplast protein harboring a 3Fe-4S iron-sulfur cluster TCR controls electron flow around photosystem I, contributing to P700 oxidation
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Affiliation(s)
- Mai Duy Luu Trinh
- Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Daichi Miyazaki
- Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Sumire Ono
- Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Jiro Nomata
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Masaru Kono
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
| | - Hiroyuki Mino
- Division of Materials Science (Physics), Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan
| | - Tatsuya Niwa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Yuki Okegawa
- Department of Frontier Life Sciences, Faculty of Life Sciences, Kyoto Sangyo University, Kyoto 603-8555, Japan
| | - Ken Motohashi
- Department of Frontier Life Sciences, Faculty of Life Sciences, Kyoto Sangyo University, Kyoto 603-8555, Japan
| | - Hideki Taguchi
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Toru Hisabori
- Laboratory for Chemistry and Life Science, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Shinji Masuda
- Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Corresponding author
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Duru IC, Bucur FI, Andreevskaya M, Nikparvar B, Ylinen A, Grigore-Gurgu L, Rode TM, Crauwels P, Laine P, Paulin L, Løvdal T, Riedel CU, Bar N, Borda D, Nicolau AI, Auvinen P. High-pressure processing-induced transcriptome response during recovery of Listeria monocytogenes. BMC Genomics 2021; 22:117. [PMID: 33579201 PMCID: PMC7881616 DOI: 10.1186/s12864-021-07407-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/25/2021] [Indexed: 12/18/2022] Open
Abstract
Background High-pressure processing (HPP) is a commonly used technique in the food industry to inactivate pathogens, including L. monocytogenes. It has been shown that L. monocytogenes is able to recover from HPP injuries and can start to grow again during long-term cold storage. To date, the gene expression profiling of L. monocytogenes during HPP damage recovery at cooling temperature has not been studied. In order identify key genes that play a role in recovery of the damage caused by HPP treatment, we performed RNA-sequencing (RNA-seq) for two L. monocytogenes strains (barotolerant RO15 and barosensitive ScottA) at nine selected time points (up to 48 h) after treatment with two pressure levels (200 and 400 MPa). Results The results showed that a general stress response was activated by SigB after HPP treatment. In addition, the phosphotransferase system (PTS; mostly fructose-, mannose-, galactitol-, cellobiose-, and ascorbate-specific PTS systems), protein folding, and cobalamin biosynthesis were the most upregulated genes during HPP damage recovery. We observed that cell-division-related genes (divIC, dicIVA, ftsE, and ftsX) were downregulated. By contrast, peptidoglycan-synthesis genes (murG, murC, and pbp2A) were upregulated. This indicates that cell-wall repair occurs as a part of HPP damage recovery. We also observed that prophage genes, including anti-CRISPR genes, were induced by HPP. Interestingly, a large amount of RNA-seq data (up to 85%) was mapped to Rli47, which is a non-coding RNA that is upregulated after HPP. Thus, we predicted that Rli47 plays a role in HPP damage recovery in L. monocytogenes. Moreover, gene-deletion experiments showed that amongst peptidoglycan biosynthesis genes, pbp2A mutants are more sensitive to HPP. Conclusions We identified several genes and mechanisms that may play a role in recovery from HPP damage of L. monocytogenes. Our study contributes to new information on pathogen inactivation by HPP. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07407-6.
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Affiliation(s)
- Ilhan Cem Duru
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Florentina Ionela Bucur
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | | | - Bahareh Nikparvar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Anne Ylinen
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Leontina Grigore-Gurgu
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Tone Mari Rode
- Department of Process Technology, Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, N-4068, Stavanger, Norway
| | - Peter Crauwels
- Institute of Microbiology and Biotechnology, Ulm, University, Albert-Einstein-Allee 11, D-89081, Ulm, Germany
| | - Pia Laine
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Lars Paulin
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Trond Løvdal
- Department of Process Technology, Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, N-4068, Stavanger, Norway
| | - Christian U Riedel
- Institute of Microbiology and Biotechnology, Ulm, University, Albert-Einstein-Allee 11, D-89081, Ulm, Germany
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Daniela Borda
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Anca Ioana Nicolau
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Petri Auvinen
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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Lopes I, Altab G, Raina P, de Magalhães JP. Gene Size Matters: An Analysis of Gene Length in the Human Genome. Front Genet 2021; 12:559998. [PMID: 33643374 PMCID: PMC7905317 DOI: 10.3389/fgene.2021.559998] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/06/2021] [Indexed: 12/23/2022] Open
Abstract
While it is expected for gene length to be associated with factors such as intron number and evolutionary conservation, we are yet to understand the connections between gene length and function in the human genome. In this study, we show that, as expected, there is a strong positive correlation between gene length, transcript length, and protein size as well as a correlation with the number of genetic variants and introns. Among tissue-specific genes, we find that the longest transcripts tend to be expressed in the blood vessels, nerves, thyroid, cervix uteri, and the brain, while the smallest transcripts tend to be expressed in the pancreas, skin, stomach, vagina, and testis. We report, as shown previously, that natural selection suppresses changes for genes with longer transcripts and promotes changes for genes with smaller transcripts. We also observe that genes with longer transcripts tend to have a higher number of co-expressed genes and protein-protein interactions, as well as more associated publications. In the functional analysis, we show that bigger transcripts are often associated with neuronal development, while smaller transcripts tend to play roles in skin development and in the immune system. Furthermore, pathways related to cancer, neurons, and heart diseases tend to have genes with longer transcripts, with smaller transcripts being present in pathways related to immune responses and neurodegenerative diseases. Based on our results, we hypothesize that longer genes tend to be associated with functions that are important in the early development stages, while smaller genes tend to play a role in functions that are important throughout the whole life, like the immune system, which requires fast responses.
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Affiliation(s)
| | | | | | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom
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234
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Emad A, Sinha S. Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study. NPJ Syst Biol Appl 2021; 7:9. [PMID: 33558504 PMCID: PMC7870953 DOI: 10.1038/s41540-021-00169-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/05/2021] [Indexed: 01/30/2023] Open
Abstract
Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.
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Affiliation(s)
- Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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235
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Seumois G, Ramírez-Suástegui C, Schmiedel BJ, Liang S, Peters B, Sette A, Vijayanand P. Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma. Sci Immunol 2021; 5:5/48/eaba6087. [PMID: 32532832 DOI: 10.1126/sciimmunol.aba6087] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 05/22/2020] [Indexed: 12/19/2022]
Abstract
CD4+ T helper (TH) cells and regulatory T (Treg) cells that respond to common allergens play an important role in driving and dampening airway inflammation in patients with asthma. Until recently, direct, unbiased molecular analysis of allergen-reactive TH and Treg cells has not been possible. To better understand the diversity of these T cell subsets in allergy and asthma, we analyzed the single-cell transcriptome of ~50,000 house dust mite (HDM) allergen-reactive TH cells and Treg cells from asthmatics with HDM allergy and from three control groups: asthmatics without HDM allergy and nonasthmatics with and without HDM allergy. Our analyses show that HDM allergen-reactive TH and Treg cells are highly heterogeneous and certain subsets are quantitatively and qualitatively different in individuals with HDM-reactive asthma. The number of interleukin-9 (IL-9)-expressing HDM-reactive TH cells is greater in asthmatics with HDM allergy compared with nonasthmatics with HDM allergy, and this IL-9-expressing TH subset displays enhanced pathogenic properties. More HDM-reactive TH and Treg cells expressing the interferon response signature (THIFNR and TregIFNR) are present in asthmatics without HDM allergy compared with those with HDM allergy. In cells from these subsets (THIFNR and TregIFNR), expression of TNFSF10 was enriched; its product, tumor necrosis factor-related apoptosis-inducing ligand, dampens activation of TH cells. These findings suggest that the THIFNR and TregIFNR subsets may dampen allergic responses, which may help explain why only some people develop TH2 responses to nearly ubiquitous allergens.
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Affiliation(s)
- Grégory Seumois
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.
| | | | | | - Shu Liang
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Pandurangan Vijayanand
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. .,Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA.,Clinical and Experimental Sciences, National Institute for Health Research Southampton Respiratory Biomedical Research Unit, Faculty of Medicine, University of Southampton, Southampton SO166YD, UK
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Wang J, Liu B, Yao J, Liu Z, Wang H, Zhang B, Lian X, Ren Z, Liu L, Gao Y. Interleukin-1 receptor-associated kinase 4 as a potential biomarker: Overexpression predicts poor prognosis in patients with glioma. Oncol Lett 2021; 21:254. [PMID: 33664818 PMCID: PMC7882878 DOI: 10.3892/ol.2021.12516] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 01/12/2021] [Indexed: 12/20/2022] Open
Abstract
The undetectable onset of glioma and the difficulty of surgery lead to a poor prognosis. Appropriate biomarkers for diagnosis and treatment need to be identified. Interleukin-1 receptor-associated kinase 4 (IRAK4) is involved in the initiation and progression of cancer. However, up until now, no report has revealed the relationship between IRAK4 and glioma. The present study aimed to examine the expression of IRAK4 in glioma, and to determine if there was a relationship between IRAK4 expression and clinical outcomes or survival prognosis. Thousands of glioma tissue samples and corresponding clinical information were obtained from various databases. Then a series of bioinformatics methods were used to reveal the role of IRAK4 in glioma. Finally, reverse transcription-quantitative PCR technology was used to verify the bioinformatics results. The study found that the expression of IRAK4 was significantly increased in glioma compared with the control brain tissue samples, and IRAK4, as an independent prognostic factor, shortened the overall survival time of patients with glioma. Gene Set Enrichment Analysis showed that IRAK4 promoted the activation of cell signalling pathways, such as NOD-like and Toll-like receptor signalling pathways. Co-expression analysis showed that the expression of IRAK4 was correlated with CMTM6, MOB1A and other genes. The present study demonstrated the role of IRAK4 as an oncogene in the pathological process of glioma for the first time, and highlights the potential of IRAK4 as a biomarker for prognostic evaluation and treatment of glioma.
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Affiliation(s)
- Jialin Wang
- Department of Orthopaedics, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Binfeng Liu
- Department of Orthopaedics, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Jiawei Yao
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University and Institute of Brain Science, Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
| | - Zhendong Liu
- Department of Orthopaedics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan 450001, P.R. China
| | - Hongbo Wang
- Department of Orthopaedics, Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Bo Zhang
- Department of Orthopaedics, Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Xiaoyu Lian
- Department of Orthopaedics, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Zhishuai Ren
- Department of Orthopaedics, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan 450001, P.R. China
| | - Liyun Liu
- Department of Orthopaedics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan 450001, P.R. China
| | - Yanzheng Gao
- Department of Orthopaedics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, School of Clinical Medicine, Henan University, Zhengzhou, Henan 450001, P.R. China
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237
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Joodaki M, Ghadiri N, Maleki Z, Lotfi Shahreza M. A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion. J Biomed Inform 2021; 115:103688. [PMID: 33545331 DOI: 10.1016/j.jbi.2021.103688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 01/10/2021] [Accepted: 01/23/2021] [Indexed: 12/11/2022]
Abstract
One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation.
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Affiliation(s)
- Mehdi Joodaki
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Nasser Ghadiri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
| | - Zeinab Maleki
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
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238
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Diniz WJS, Crouse MS, Cushman RA, McLean KJ, Caton JS, Dahlen CR, Reynolds LP, Ward AK. Cerebrum, liver, and muscle regulatory networks uncover maternal nutrition effects in developmental programming of beef cattle during early pregnancy. Sci Rep 2021; 11:2771. [PMID: 33531552 PMCID: PMC7854659 DOI: 10.1038/s41598-021-82156-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 01/13/2021] [Indexed: 01/30/2023] Open
Abstract
The molecular basis underlying fetal programming in response to maternal nutrition remains unclear. Herein, we investigated the regulatory relationships between genes in fetal cerebrum, liver, and muscle tissues to shed light on the putative mechanisms that underlie the effects of early maternal nutrient restriction on bovine developmental programming. To this end, cerebrum, liver, and muscle gene expression were measured with RNA-Seq in 14 fetuses collected on day 50 of gestation from dams fed a diet initiated at breeding to either achieve 60% (RES, n = 7) or 100% (CON, n = 7) of energy requirements. To build a tissue-to-tissue gene network, we prioritized tissue-specific genes, transcription factors, and differentially expressed genes. Furthermore, we built condition-specific networks to identify differentially co-expressed or connected genes. Nutrient restriction led to differential tissue regulation between the treatments. Myogenic factors differentially regulated by ZBTB33 and ZNF131 may negatively affect myogenesis. Additionally, nutrient-sensing pathways, such as mTOR and PI3K/Akt, were affected by gene expression changes in response to nutrient restriction. By unveiling the network properties, we identified major regulators driving gene expression. However, further research is still needed to determine the impact of early maternal nutrition and strategic supplementation on pre- and post-natal performance.
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Affiliation(s)
- Wellison J. S. Diniz
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Matthew S. Crouse
- grid.463419.d0000 0001 0946 3608USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE USA
| | - Robert A. Cushman
- grid.463419.d0000 0001 0946 3608USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE USA
| | - Kyle J. McLean
- grid.411461.70000 0001 2315 1184Department of Animal Science, University of Tennessee, Knoxville, TN USA
| | - Joel S. Caton
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Carl R. Dahlen
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Lawrence P. Reynolds
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Alison K. Ward
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
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239
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Gutiérrez-Díez PJ, Gomez-Pilar J, Hornero R, Martínez-Rodríguez J, López-Marcos MA, Russo J. The role of gene to gene interaction in the breast's genomic signature of pregnancy. Sci Rep 2021; 11:2643. [PMID: 33514799 PMCID: PMC7846553 DOI: 10.1038/s41598-021-81704-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/18/2020] [Indexed: 12/20/2022] Open
Abstract
Full-term pregnancy at an early age confers long-term protection against breast cancer. Published data shows a specific transcriptomic profile controlling chromatin remodeling that could play a relevant role in the pregnancy-induced protection. This process of chromatin remodeling, induced by the breast differentiation caused by the first full-term pregnancy, has mainly been measured by the expression level of genes individually considered. However, genes equally expressed during the process of chromatin remodeling may behave differently in their interaction with other genes. These changes at the gene cluster level could constitute an additional dimension of chromatin remodeling and therefore of the pregnancy-induced protection. In this research, we apply Information and Graph Theories, Differential Co-expression Network Analysis, and Multiple Regression Analysis, specially designed to examine structural and informational aspects of data sets, to analyze this question. Our findings demonstrate that, independently of the changes in the gene expression at the individual level, there are significant changes in gene-gene interactions and gene cluster behaviors. These changes indicate that the parous breast, through the process of early full-term pregnancy, generates more modules in the networks, with higher density, and a genomic structure performing additional and more complex functions than those found in the nulliparous breast.
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Affiliation(s)
- Pedro J Gutiérrez-Díez
- IMUVA Mathematical Institute, University of Valladolid, Valladolid, Spain
- Faculty of Economics, University of Valladolid, Valladolid, Spain
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain.
| | - Roberto Hornero
- IMUVA Mathematical Institute, University of Valladolid, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales Y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Julia Martínez-Rodríguez
- IMUVA Mathematical Institute, University of Valladolid, Valladolid, Spain
- Faculty of Economics, University of Valladolid, Valladolid, Spain
| | - Miguel A López-Marcos
- IMUVA Mathematical Institute, University of Valladolid, Valladolid, Spain
- Faculty of Science, University of Valladolid, Valladolid, Spain
| | - Jose Russo
- The Irma H. Russo, MD Breast Cancer Research Laboratory, Fox Chase Cancer Center - Temple University Health System, Philadelphia, USA
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Wang J, Sui J, Mao C, Li X, Chen X, Liang C, Wang X, Wang SH, Jia C. Identification of Key Pathways and Genes Related to the Development of Hair Follicle Cycle in Cashmere Goats. Genes (Basel) 2021; 12:genes12020180. [PMID: 33513983 PMCID: PMC7911279 DOI: 10.3390/genes12020180] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/15/2021] [Accepted: 01/22/2021] [Indexed: 12/13/2022] Open
Abstract
The development of hair follicle in cashmere goats shows significant periodic change, as with mice and humans. However, for cashmere goat with double-coat, the periodic change may be due to other regulatory molecules and signal pathways. To understand the mechanism of periodic development of hair follicle, we performed a weighted gene coexpression network analysis (WGCNA) to mine key genes and establish an interaction network by utilizing the NCBI public dataset. Ten coexpression modules, including 7689 protein-coding genes, were constructed by WGCNA, six of which are considered to be significantly related to the development of the hair follicle cycle. A functional enrichment analysis for each model showed that they are closely related to ECM- receptor interaction, focal adhesion, PI3K-Akt signaling pathway, estrogen signaling pathway, and so on. Combined with the analysis of differential expressed genes, 12 hub genes from coexpression modules were selected as candidate markers, i.e., COL1A1, C1QTNF6, COL1A2, AQP3, KRTAP3-1, KRTAP11-1, FA2H, NDUFS5, DERL2, MRPL14, ANTKMT and XAB2, which might be applied to improve cashmere production.
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241
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Mwangi KW, Macharia RW, Bargul JL. Gene co-expression network analysis of Trypanosoma brucei in tsetse fly vector. Parasit Vectors 2021; 14:74. [PMID: 33482903 PMCID: PMC7821691 DOI: 10.1186/s13071-021-04597-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/13/2021] [Indexed: 01/14/2023] Open
Abstract
Background Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly. Methods In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene co-expression network using the weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Furthermore, potential 3′ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool. Results A fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3′ untranslated regions of genes within the same module were predicted. Conclusions The constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.![]()
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Affiliation(s)
- Kennedy W Mwangi
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya. .,Jomo Kenyatta University of Agriculture and Technology, P.O. BOX 62000-00200, Nairobi, Kenya.
| | | | - Joel L Bargul
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100, Nairobi, Kenya.,Jomo Kenyatta University of Agriculture and Technology, P.O. BOX 62000-00200, Nairobi, Kenya
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242
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Portes LL, Small M. Navigating differential structures in complex networks. Phys Rev E 2021; 102:062301. [PMID: 33466036 DOI: 10.1103/physreve.102.062301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 11/20/2020] [Indexed: 11/07/2022]
Abstract
Structural changes in a network representation of a system, due to different experimental conditions, different connectivity across layers, or to its time evolution, can provide insight on its organization, function, and on how it responds to external perturbations. The deeper understanding of how gene networks cope with diseases and treatments is maybe the most incisive demonstration of the gains obtained through this differential network analysis point of view, which led to an explosion of new numeric techniques in the last decade. However, where to focus one's attention, or how to navigate through the differential structures in the context of large networks, can be overwhelming even for a few experimental conditions. In this paper, we propose a theory and a methodological implementation for the characterization of shared "structural roles" of nodes simultaneously within and between networks. Inspired by recent methodological advances in chaotic phase synchronization analysis, we show how the information about the shared structures of a set of networks can be split and organized in an automatic fashion, in scenarios with very different (i) community sizes, (ii) total number of communities, and (iii) even for a large number of 100 networks compared using numerical benchmarks generated by a stochastic block model. Then, we investigate how the network size, number of networks, and mean size of communities influence the method performance in a series of Monte Carlo experiments. To illustrate its potential use in a more challenging scenario with real-world data, we show evidence that the method can still split and organize the structural information of a set of four gene coexpression networks obtained from two cell types × two treatments (interferon-β stimulated or control). Aside from its potential use as for automatic feature extraction and preprocessing tool, we discuss that another strength of the method is its "story-telling"-like characterization of the information encoded in a set of networks, which can be used to pinpoint unexpected shared structure, leading to further investigations and providing new insights. Finally, the method is flexible to address different research-field-specific questions, by not restricting what scientific-meaningful characteristic (or relevant feature) of a node shall be used.
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Affiliation(s)
- Leonardo L Portes
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Nedlands, Perth, WA 6009, Australia
| | - Michael Small
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Nedlands, Perth, WA 6009, Australia.,Mineral Resources, CSIRO, Kensington, Perth, WA 6151, Australia
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243
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Kim D, Song J, Lee S, Jung J, Jang W. An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates. Int J Mol Sci 2021; 22:ijms22020712. [PMID: 33445803 PMCID: PMC7828236 DOI: 10.3390/ijms22020712] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/02/2021] [Accepted: 01/07/2021] [Indexed: 12/20/2022] Open
Abstract
Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.
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244
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Chen PS, Hsu HP, Phan NN, Yen MC, Chen FW, Liu YW, Lin FP, Feng SY, Cheng TL, Yeh PH, Omar HA, Sun Z, Jiang JZ, Chan YS, Lai MD, Wang CY, Hung JH. CCDC167 as a potential therapeutic target and regulator of cell cycle-related networks in breast cancer. Aging (Albany NY) 2021; 13:4157-4181. [PMID: 33461170 PMCID: PMC7906182 DOI: 10.18632/aging.202382] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023]
Abstract
According to cancer statistics reported in 2020, breast cancer constitutes 30% of new cancer cases diagnosed in American women. Histological markers of breast cancer are expressions of the estrogen receptor (ER), the progesterone receptor (PR), and human epidermal growth factor receptor (HER)-2. Up to 80% of breast cancers are grouped as ER-positive, which implies a crucial role for estrogen in breast cancer development. Therefore, identifying potential therapeutic targets and investigating their downstream pathways and networks are extremely important for drug development in these patients. Through high-throughput technology and bioinformatics screening, we revealed that coiled-coil domain-containing protein 167 (CCDC167) was upregulated in different types of tumors; however, the role of CCDC167 in the development of breast cancer still remains unclear. Integrating many kinds of databases including ONCOMINE, MetaCore, IPA, and Kaplan-Meier Plotter, we found that high expression levels of CCDC167 predicted poor prognoses of breast cancer patients. Knockdown of CCDC167 attenuated aggressive breast cancer growth and proliferation. We also demonstrated that treatment with fluorouracil, carboplatin, paclitaxel, and doxorubicin resulted in decreased expression of CCDC167 and suppressed growth of MCF-7 cells. Collectively, these findings suggest that CCDC167 has high potential as a therapeutic target for breast cancer.
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Affiliation(s)
- Pin-Shern Chen
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China
| | - Hui-Ping Hsu
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan, Republic of China
| | - Nam Nhut Phan
- NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh 700000, Vietnam
| | - Meng-Chi Yen
- Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China.,Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Feng-Wei Chen
- Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan, Republic of China
| | - Yu-Wei Liu
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China
| | - Fang-Ping Lin
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China
| | - Sheng-Yao Feng
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China
| | - Tsung-Lin Cheng
- Department of Physiology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China.,Orthopedic Research Center, College of Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China.,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China.,Regenerative Medicine and Cell Therapy Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
| | - Pei-Hsiang Yeh
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China
| | - Hany A Omar
- Sharjah Institute for Medical Research and College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates.,Department of Clinical Sciences, College of Pharmacy, Ajman University, Ajman 23000, United Arab Emirates.,Department of Pharmacology, Faculty of Pharmacy, BeniSuef University, Beni-Suef 62511, Egypt
| | - Zhengda Sun
- Kaiser Permanente, Northern California Regional Laboratories, The Permanente Medical Group, Berkeley, CA 94710, USA
| | - Jia-Zhen Jiang
- Emergency Department, Huashan Hospital North, Fudan University, Shanghai 201508, People's Republic of China
| | - Yi-Shin Chan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan, Republic of China
| | - Ming-Derg Lai
- Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan, Republic of China
| | - Chih-Yang Wang
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan, Republic of China.,PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan, Republic of China
| | - Jui-Hsiang Hung
- Department of Biotechnology, Chia Nan University of Pharmacy and Science, Tainan 70101, Taiwan, Republic of China.,Regenerative Medicine and Cell Therapy Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, Republic of China
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245
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Fang L, Li Y, Ma L, Xu Q, Tan F, Chen G. GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions. Nucleic Acids Res 2021; 49:D97-D103. [PMID: 33151298 PMCID: PMC7779055 DOI: 10.1093/nar/gkaa995] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 10/10/2020] [Accepted: 10/13/2020] [Indexed: 12/11/2022] Open
Abstract
Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.
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Affiliation(s)
- Li Fang
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yunjin Li
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Lu Ma
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Qiyue Xu
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Fei Tan
- Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China
| | - Geng Chen
- Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
- Shanghai Applied Protein Technology Co., Ltd. (APTBIO), Shanghai 200233, China
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246
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Molecular Analysis of 14-3-3 Genes in Citrus sinensis and Their Responses to Different Stresses. Int J Mol Sci 2021; 22:ijms22020568. [PMID: 33430069 PMCID: PMC7826509 DOI: 10.3390/ijms22020568] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 12/31/2022] Open
Abstract
14-3-3 proteins (14-3-3s) are among the most important phosphorylated molecules playing crucial roles in regulating plant development and defense responses to environmental constraints. No report thus far has documented the gene family of 14-3-3s in Citrus sinensis and their roles in response to stresses. In this study, nine 14-3-3 genes, designated as CitGF14s (CitGF14a through CitGF14i) were identified from the latest C. sinensis genome. Phylogenetic analysis classified them into ε-like and non-ε groups, which were supported by gene structure analysis. The nine CitGF14s were located on five chromosomes, and none had duplication. Publicly available RNA-Seq raw data and microarray databases were mined for 14-3-3 expression profiles in different organs of citrus and in response to biotic and abiotic stresses. RT-qPCR was used for further examining spatial expression patterns of CitGF14s in citrus and their temporal expressions in one-year-old C. sinensis “Xuegan” plants after being exposed to different biotic and abiotic stresses. The nine CitGF14s were expressed in eight different organs with some isoforms displayed tissue-specific expression patterns. Six of the CitGF14s positively responded to citrus canker infection (Xanthomonas axonopodis pv. citri). The CitGF14s showed expressional divergence after phytohormone application and abiotic stress treatments, suggesting that 14-3-3 proteins are ubiquitous regulators in C. sinensis. Using the yeast two-hybrid assay, CitGF14a, b, c, d, g, and h were found to interact with CitGF14i proteins to form a heterodimer, while CitGF14i interacted with itself to form a homodimer. Further analysis of CitGF14s co-expression and potential interactors established a 14-3-3s protein interaction network. The established network identified 14-3-3 genes and several candidate clients which may play an important role in developmental regulation and stress responses in this important fruit crop. This is the first study of 14-3-3s in citrus, and the established network may help further investigation of the roles of 14-3-3s in response to abiotic and biotic constraints.
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247
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Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
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248
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Transcriptomic time-series analysis of cold- and heat-shock response in psychrotrophic lactic acid bacteria. BMC Genomics 2021; 22:28. [PMID: 33413101 PMCID: PMC7788899 DOI: 10.1186/s12864-020-07338-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 12/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Psychrotrophic lactic acid bacteria (LAB) species are the dominant species in the microbiota of cold-stored modified-atmosphere-packaged food products and are the main cause of food spoilage. Despite the importance of psychrotrophic LAB, their response to cold or heat has not been studied. Here, we studied the transcriptome-level cold- and heat-shock response of spoilage lactic acid bacteria with time-series RNA-seq for Le. gelidum, Lc. piscium, and P. oligofermentans at 0 °C, 4 °C, 14 °C, 25 °C, and 28 °C. RESULTS We observed that the cold-shock protein A (cspA) gene was the main cold-shock protein gene in all three species. Our results indicated that DEAD-box RNA helicase genes (cshA, cshB) also play a critical role in cold-shock response in psychrotrophic LAB. In addition, several RNase genes were involved in cold-shock response in Lc. piscium and P. oligofermentans. Moreover, gene network inference analysis provided candidate genes involved in cold-shock response. Ribosomal proteins, tRNA modification, rRNA modification, and ABC and efflux MFS transporter genes clustered with cold-shock response genes in all three species, indicating that these genes could be part of the cold-shock response machinery. Heat-shock treatment caused upregulation of Clp protease and chaperone genes in all three species. We identified transcription binding site motifs for heat-shock response genes in Le. gelidum and Lc. piscium. Finally, we showed that food spoilage-related genes were upregulated at cold temperatures. CONCLUSIONS The results of this study provide new insights on the cold- and heat-shock response of psychrotrophic LAB. In addition, candidate genes involved in cold- and heat-shock response predicted using gene network inference analysis could be used as targets for future studies.
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249
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Prasad K, AlOmar SY, Alqahtani SAM, Malik MZ, Kumar V. Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19. Mol Neurobiol 2021; 58:1875-1893. [PMID: 33409839 PMCID: PMC7787249 DOI: 10.1007/s12035-020-02266-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/16/2020] [Indexed: 01/08/2023]
Abstract
Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.
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Affiliation(s)
- Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, 201303, India
| | - Suliman Yousef AlOmar
- Doping research chair, Department of Zoology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | | | - Md Zubbair Malik
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, 201303, India.
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250
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Abstract
Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.
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