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Sayed IM, Vo DT, Alcantara J, Inouye KM, Pranadinata RF, Luo L, Boland CR, Goyal NP, Kuo DJ, Huang SC, Sahoo D, Ghosh P, Das S. Molecular Signatures for Microbe-Associated Colorectal Cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595902. [PMID: 38853996 PMCID: PMC11160670 DOI: 10.1101/2024.05.26.595902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics. Methods A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS). Results The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs. Conclusions Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.
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De R, Whiteley M, Azad RK. A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1. mSystems 2023; 8:e0047323. [PMID: 37921470 PMCID: PMC10734507 DOI: 10.1128/msystems.00473-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 10/04/2023] [Indexed: 11/04/2023] Open
Abstract
IMPORTANCE We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.
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Affiliation(s)
- Ronika De
- Department of Biological Sciences, University of North Texas, Denton, Texas, USA
- BioDiscovery Institute, University of North Texas, Denton, Texas, USA
| | - Marvin Whiteley
- Center for Microbial Dynamics and Infection, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- Emory-Children’s Cystic Fibrosis Center, Atlanta, Georgia, USA
| | - Rajeev K. Azad
- Department of Biological Sciences, University of North Texas, Denton, Texas, USA
- BioDiscovery Institute, University of North Texas, Denton, Texas, USA
- Department of Mathematics, University of North Texas, Denton, Texas, USA
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3
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Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol 2023; 19:e1010770. [PMID: 37471455 PMCID: PMC10393163 DOI: 10.1371/journal.pcbi.1010770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/22/2023] Open
Abstract
While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.
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Affiliation(s)
- Roger Vargas
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
- Harvard University, Cambridge, Massachusetts, United States of America
| | - Liam Abbott
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Daniel Bower
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Nicole Frahm
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Mike Shaffer
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Wen-Han Yu
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
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Igarashi W, Takagi D, Okada D, Kobayashi D, Oka M, Io T, Ishii K, Ono K, Yamamoto H, Okamoto Y. Bioinformatic Identification of Potential RNA Alterations on the Atrial Fibrillation Remodeling from Human Pulmonary Veins. Int J Mol Sci 2023; 24:10501. [PMID: 37445678 DOI: 10.3390/ijms241310501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Atrial fibrillation (AF) is the most frequent persistent arrhythmia. Many genes have been reported as a genetic background for AF. However, most transcriptome analyses of AF are limited to the atrial samples and have not been evaluated by multiple cardiac regions. In this study, we analyzed the expression levels of protein-coding and long noncoding RNAs (lncRNAs) in six cardiac regions by RNA-seq. Samples were donated from six subjects with or without persistent AF for left atria, left atrial appendages, right atria, sinoatrial nodes, left ventricles, right ventricles, and pulmonary veins (PVs), and additional four right atrial appendages samples were collected from patients undergoing mitral valve replacement. In total, 23 AF samples were compared to 23 non-AF samples. Surprisingly, the most influenced heart region in gene expression by AF was the PV, not the atria. The ion channel-related gene set was significantly enriched upon analysis of these significant genes. In addition, some significant genes are cancer-related lncRNAs in PV in AF. A co-expression network analysis could detect the functional gene clusters. In particular, the cancer-related lncRNA, such as SAMMSON and FOXCUT, belong to the gene network with the cancer-related transcription factor FOXC1. Thus, they may also play an aggravating role in the pathogenesis of AF, similar to carcinogenesis. In the least, this study suggests that (1) RNA alteration is most intense in PVs and (2) post-transcriptional gene regulation by lncRNA may contribute to the progression of AF. Through the screening analysis across the six cardiac regions, the possibility that the PV region can play a role other than paroxysmal triggering in the pathogenesis of AF was demonstrated for the first time. Future research with an increase in the number of PV samples will lead to a novel understanding of the pathophysiology of AF.
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Affiliation(s)
- Wataru Igarashi
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Daichi Takagi
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Shogoinkawahara-cho, Kyoto 606-8507, Japan
| | - Daiki Kobayashi
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Miho Oka
- Research Department, Ono Pharmaceutical Co., Ltd., Kyutaromachi, Osaka 541-0056, Japan
| | - Toshiro Io
- Research Department, Ono Pharmaceutical Co., Ltd., Kyutaromachi, Osaka 541-0056, Japan
| | - Kuniaki Ishii
- Department of Pharmacology, Faculty of Medicine, Yamagata University, Iida-Nishi, Yamagata 990-9585, Japan
| | - Kyoichi Ono
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Hiroshi Yamamoto
- Department of Cardiovascular Surgery, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
| | - Yosuke Okamoto
- Department of Cell Physiology, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita 010-8543, Japan
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Zage PE, Huo Y, Subramonian D, Le Clorennec C, Ghosh P, Sahoo D. Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network. Genes Chromosomes Cancer 2023; 62:313-331. [PMID: 36680522 PMCID: PMC10257350 DOI: 10.1002/gcc.23124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/22/2023] Open
Abstract
Although induction of differentiation represents an effective strategy for neuroblastoma treatment, the mechanisms underlying neuroblastoma differentiation are poorly understood. We generated a computational model of neuroblastoma differentiation consisting of interconnected gene clusters identified based on symmetric and asymmetric gene expression relationships. We identified a differentiation signature consisting of series of gene clusters comprised of 1251 independent genes that predicted neuroblastoma differentiation in independent datasets and in neuroblastoma cell lines treated with agents known to induce differentiation. This differentiation signature was associated with patient outcomes in multiple independent patient cohorts and validated the role of MYCN expression as a marker of neuroblastoma differentiation. Our results further identified novel genes associated with MYCN via asymmetric Boolean implication relationships that would not have been identified using symmetric computational approaches and that were associated with both neuroblastoma differentiation and patient outcomes. Our differentiation signature included a cluster of genes involved in intracellular signaling and growth factor receptor trafficking pathways that is strongly associated with neuroblastoma differentiation, and we validated the associations of UBE4B, a gene within this cluster, with neuroblastoma cell and tumor differentiation. Our findings demonstrate that Boolean network analyses of symmetric and asymmetric gene expression relationships can identify novel genes and pathways relevant for neuroblastoma tumor differentiation that could represent potential therapeutic targets.
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Affiliation(s)
- Peter E. Zage
- Department of Pediatrics, Division of Hematology-Oncology, University of California San Diego (UCSD), La Jolla, CA
| | - Yuchen Huo
- Department of Pediatrics, Division of Hematology-Oncology, University of California San Diego (UCSD), La Jolla, CA
| | - Divya Subramonian
- Department of Pediatrics, Division of Hematology-Oncology, University of California San Diego (UCSD), La Jolla, CA
| | - Christophe Le Clorennec
- Department of Pediatrics, Division of Hematology-Oncology, University of California San Diego (UCSD), La Jolla, CA
| | - Pradipta Ghosh
- Department of Medicine, UCSD, La Jolla, CA
- Department of Cellular and Molecular Medicine, UCSD, La Jolla, CA
- Veterans Affairs Medical Center, La Jolla, CA
| | - Debashis Sahoo
- Department of Pediatrics, Division of Hematology-Oncology, University of California San Diego (UCSD), La Jolla, CA
- Department of Computer Science and Engineering, Jacobs School of Engineering, UCSD, La Jolla, CA
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Burks DJ, Sengupta S, De R, Mittler R, Azad RK. The Arabidopsis gene co-expression network. PLANT DIRECT 2022; 6:e396. [PMID: 35492683 PMCID: PMC9039629 DOI: 10.1002/pld3.396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High-throughput technologies for assessment and quantification of genome-wide gene expression patterns have enabled systems-level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information-rich RNA-Seq datasets of the model plant Arabidopsis thaliana to construct a gene co-expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co-expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition in Arabidopsis onto the co-expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the first A. thaliana co-expression network constructed using the entire mRNA-Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for the Arabidopsis research community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.
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Affiliation(s)
- David J. Burks
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Soham Sengupta
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Ronika De
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
| | - Ron Mittler
- The Division of Plant Sciences and Interdisciplinary Plant Group, College of Agriculture, Food and Natural ResourcesChristopher S. Bond Life Sciences Center University of MissouriColumbiaMissouriUSA
- Department of SurgeryUniversity of Missouri School of MedicineColumbiaMissouriUSA
| | - Rajeev K. Azad
- Department of Biological Sciences and BioDiscovery Institute, College of ScienceUniversity of North TexasDentonTexasUSA
- Department of MathematicsUniversity of North TexasDentonTexasUSA
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7
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Piya S, Hawk T, Patel B, Baldwin L, Rice JH, Stewart CN, Hewezi T. Kinase-dead mutation: A novel strategy for improving soybean resistance to soybean cyst nematode Heterodera glycines. MOLECULAR PLANT PATHOLOGY 2022; 23:417-430. [PMID: 34851539 PMCID: PMC8828698 DOI: 10.1111/mpp.13168] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/12/2021] [Accepted: 11/12/2021] [Indexed: 05/29/2023]
Abstract
Protein kinases phosphorylate proteins for functional changes and are involved in nearly all cellular processes, thereby regulating almost all aspects of plant growth and development, and responses to biotic and abiotic stresses. We generated two independent co-expression networks of soybean genes using control and stress response gene expression data and identified 392 differentially highly interconnected kinase hub genes among the two networks. Of these 392 kinases, 90 genes were identified as "syncytium highly connected hubs", potentially essential for activating kinase signalling pathways in the nematode feeding site. Overexpression of wild-type coding sequences of five syncytium highly connected kinase hub genes using transgenic soybean hairy roots enhanced plant susceptibility to soybean cyst nematode (SCN; Heterodera glycines) Hg Type 0 (race 3). In contrast, overexpression of kinase-dead variants of these five syncytium kinase hub genes significantly enhanced soybean resistance to SCN. Additionally, three of the five tested kinase hub genes enhanced soybean resistance to SCN Hg Type 1.2.5.7 (race 2), highlighting the potential of the kinase-dead approach to generate effective and durable resistance against a wide range of SCN Hg types. Subcellular localization analysis revealed that kinase-dead mutations do not alter protein cellular localization, confirming the structure-function of the kinase-inactive variants in producing loss-of-function phenotypes causing significant decrease in nematode susceptibility. Because many protein kinases are highly conserved and are involved in plant responses to various biotic and abiotic stresses, our approach of identifying kinase hub genes and their inactivation using kinase-dead mutation could be translated for biotic and abiotic stress tolerance.
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Affiliation(s)
- Sarbottam Piya
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Tracy Hawk
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Bhoomi Patel
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Logan Baldwin
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - John H. Rice
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - C. Neal Stewart
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Tarek Hewezi
- Department of Plant SciencesUniversity of TennesseeKnoxvilleTennesseeUSA
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8
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Burns JJR, Shealy BT, Greer MS, Hadish JA, McGowan MT, Biggs T, Smith MC, Feltus FA, Ficklin SP. Addressing noise in co-expression network construction. Brief Bioinform 2021; 23:6446269. [PMID: 34850822 PMCID: PMC8769892 DOI: 10.1093/bib/bbab495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.
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Affiliation(s)
- Joshua J R Burns
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA
| | - Benjamin T Shealy
- Department of Electrical & Computer Engineering, 105 Riggs Hall. Clemson University, Clemson, SC 29631. USA
| | - Mitchell S Greer
- School of Electrical Engineering and Computer Science, EME 102. Washington State University, Pullman, WA 99164. USA
| | - John A Hadish
- Molecular Plant Sciences Program, French Ad 324g. Washington State University, Pullman, WA 99164. USA
| | - Matthew T McGowan
- Molecular Plant Sciences Program, French Ad 324g. Washington State University, Pullman, WA 99164. USA
| | - Tyler Biggs
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA
| | - Melissa C Smith
- Department of Electrical & Computer Engineering, 105 Riggs Hall. Clemson University, Clemson, SC 29631. USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, 130 McGinty Court. Clemson University, Clemson, SC 29634. USA.,Biomedical Data Science & Informatics Program, 100 McAdams Hall. Clemson University, Clemson, SC 29634. USA.,Clemson Center for Human Genetics, 114 Gregor Mendel Circle, Greenwood, SC 29646. USA
| | - Stephen P Ficklin
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA.,School of Electrical Engineering and Computer Science, EME 102. Washington State University, Pullman, WA 99164. USA
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9
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Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease. Nat Commun 2021; 12:4246. [PMID: 34253728 PMCID: PMC8275683 DOI: 10.1038/s41467-021-24470-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/21/2021] [Indexed: 12/19/2022] Open
Abstract
Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here we outline an unbiased AI-assisted approach for target identification and validation. A network was built in which clusters of genes are connected by directed edges that highlight asymmetric Boolean relationships. Using machine-learning, a path of continuum states was pinpointed, which most effectively predicted disease outcome. This path was enriched in gene-clusters that maintain the integrity of the gut epithelial barrier. We exploit this insight to prioritize one target, choose appropriate pre-clinical murine models for target validation and design patient-derived organoid models. Potential for treatment efficacy is confirmed in patient-derived organoids using multivariate analyses. This AI-assisted approach identifies a first-in-class gut barrier-protective agent in IBD and predicted Phase-III success of candidate agents. Traditional drug discovery process use differential, Bayesian and other network based approaches. We developed a Boolean approach for building disease maps and prioritizing pre-clinical models to discover a first-in-class therapy to restore and protect the leaky gut barrier in inflammatory bowel disease.
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10
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Karabekmez ME, Taymaz-Nikerel H, Eraslan S, Kirdar B. Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin. Mol Omics 2021; 17:572-582. [PMID: 34095940 DOI: 10.1039/d1mo00046b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.
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Affiliation(s)
| | - Hilal Taymaz-Nikerel
- Department of Genetics and Bioengineering, Istanbul Bilgi University, 34060 Eyup, Istanbul, Turkey
| | - Serpil Eraslan
- Koç University Hospital, Diagnosis Centre for Genetic Disorders, Topkapı, Istanbul, Turkey
| | - Betul Kirdar
- Department of Chemical Engineering, Bogazici University, 34342 Bebek, Istanbul, Turkey.
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Satokangas I, Martin SH, Helanterä H, Saramäki J, Kulmuni J. Multi-locus interactions and the build-up of reproductive isolation. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190543. [PMID: 32654649 PMCID: PMC7423273 DOI: 10.1098/rstb.2019.0543] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2020] [Indexed: 12/15/2022] Open
Abstract
All genes interact with other genes, and their additive effects and epistatic interactions affect an organism's phenotype and fitness. Recent theoretical and empirical work has advanced our understanding of the role of multi-locus interactions in speciation. However, relating different models to one another and to empirical observations is challenging. This review focuses on multi-locus interactions that lead to reproductive isolation (RI) through reduced hybrid fitness. We first review theoretical approaches and show how recent work incorporating a mechanistic understanding of multi-locus interactions recapitulates earlier models, but also makes novel predictions concerning the build-up of RI. These include high variance in the build-up rate of RI among taxa, the emergence of strong incompatibilities producing localized barriers to introgression, and an effect of population size on the build-up of RI. We then review recent experimental approaches to detect multi-locus interactions underlying RI using genomic data. We argue that future studies would benefit from overlapping methods like ancestry disequilibrium scans, genome scans of differentiation and analyses of hybrid gene expression. Finally, we highlight a need for further overlap between theoretical and empirical work, and approaches that predict what kind of patterns multi-locus interactions resulting in incompatibilities will leave in genome-wide polymorphism data. This article is part of the theme issue 'Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers'.
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Affiliation(s)
- I. Satokangas
- Organismal & Evolutionary Biology Research Programme, University of Helsinki, Viikinkaari 1, PO Box 65, 00014 Helsinki, Finland
| | - S. H. Martin
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh EH9 3FL, UK
| | - H. Helanterä
- Ecology and Genetics research unit, University of Oulu, PO Box 3000, 90014 Oulu, Finland
| | - J. Saramäki
- Department of Computer Science, Aalto University, PO Box 11000, 00076 Aalto, Espoo, Finland
| | - J. Kulmuni
- Organismal & Evolutionary Biology Research Programme, University of Helsinki, Viikinkaari 1, PO Box 65, 00014 Helsinki, Finland
- Tvärminne Zoological Station, University of Helsinki, J. A. Palménin tie 260, 10900 Hanko, Finland
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Abstract
Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, quantitative proteomics revealed that selection pressures not relating to optimal function play much larger roles than previously thought, acting perhaps most importantly via protein expression levels. This paper first summarizes recent progress in the 21st century toward recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying 'non-function' selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases.
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Borgmann-Winter KE, Wang K, Bandyopadhyay S, Torshizi AD, Blair IA, Hahn CG. The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia. Schizophr Res 2020; 217:148-161. [PMID: 31416743 PMCID: PMC7500806 DOI: 10.1016/j.schres.2019.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 01/08/2023]
Abstract
The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.
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Affiliation(s)
- Karin E Borgmann-Winter
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America; Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Kai Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Sabyasachi Bandyopadhyay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America
| | - Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Ian A Blair
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
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14
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Zhang W, Leon-Ricardo BX, van Schooten B, Van Belleghem SM, Counterman BA, McMillan WO, Kronforst MR, Papa R. Comparative Transcriptomics Provides Insights into Reticulate and Adaptive Evolution of a Butterfly Radiation. Genome Biol Evol 2019; 11:2963-2975. [PMID: 31518398 PMCID: PMC6821300 DOI: 10.1093/gbe/evz202] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/09/2019] [Indexed: 12/14/2022] Open
Abstract
Butterfly eyes are complex organs that are composed of a diversity of proteins and they play a central role in visual signaling and ultimately, speciation, and adaptation. Here, we utilized the whole eye transcriptome to obtain a more holistic view of the evolution of the butterfly eye while accounting for speciation events that co-occur with ancient hybridization. We sequenced and assembled transcriptomes from adult female eyes of eight species representing all major clades of the Heliconius genus and an additional outgroup species, Dryas iulia. We identified 4,042 orthologous genes shared across all transcriptome data sets and constructed a transcriptome-wide phylogeny, which revealed topological discordance with the mitochondrial phylogenetic tree in the Heliconius pupal mating clade. We then estimated introgression among lineages using additional genome data and found evidence for ancient hybridization leading to the common ancestor of Heliconius hortense and Heliconius clysonymus. We estimated the Ka/Ks ratio for each orthologous cluster and performed further tests to demonstrate genes showing evidence of adaptive protein evolution. Furthermore, we characterized patterns of expression for a subset of these positively selected orthologs using qRT-PCR. Taken together, we identified candidate eye genes that show signatures of adaptive molecular evolution and provide evidence of their expression divergence between species, tissues, and sexes. Our results demonstrate: 1) greater evolutionary changes in younger Heliconius lineages, that is, more positively selected genes in the cydno-melpomene-hecale group as opposed to the sara-hortense-erato group, and 2) suggest an ancient hybridization leading to speciation among Heliconius pupal-mating species.
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Affiliation(s)
- Wei Zhang
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Life Sciences, Peking University, Beijing, China
- Department of Ecology and Evolution, University of Chicago
| | | | - Bas van Schooten
- Department of Biology, University of Puerto Rico
- Molecular Sciences and Research Center, University of Puerto Rico
| | | | | | | | | | - Riccardo Papa
- Department of Biology, University of Puerto Rico
- Molecular Sciences and Research Center, University of Puerto Rico
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15
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Gu Y, Zu J, Li Y. A novel evolutionary model for constructing gene coexpression networks with comprehensive features. BMC Bioinformatics 2019; 20:460. [PMID: 31492104 PMCID: PMC6731579 DOI: 10.1186/s12859-019-3035-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 08/19/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Uncovering the evolutionary principles of gene coexpression network is important for our understanding of the network topological property of new genes. However, most existing evolutionary models only considered the evolution of duplication genes and only based on the degree of genes, ignoring the other key topological properties. The evolutionary mechanism by which how are new genes integrated into the ancestral networks are not yet to be comprehensively characterized. Herein, based on the human ribonucleic acid-sequencing (RNA-seq) data, we develop a new evolutionary model of gene coexpression network which considers the evolutionary process of both duplication genes and de novo genes. RESULTS Based on the human RNA-seq data, we construct a gene coexpression network consisting of 8061 genes and 638624 links. We find that there are 1394 duplication genes and 126 de novo genes in the network. Then based on human gene age data, we reproduce the evolutionary process of this gene coexpression network and develop a new evolutionary model. We find that the generation rates of duplication genes and de novo genes are approximately 3.58/Myr (Myr=Million year) and 0.31/Myr, respectively. Based on the average degree and coreness of parent genes, we find that the gene duplication is a random process. Eventually duplication genes only inherit 12.89% connections from their parent genes and the retained connections have a smaller edge betweenness. Moreover, we find that both duplication genes and de novo genes prefer to develop new interactions with genes which have a large degree and a large coreness. Our proposed model can generate an evolutionary network when the number of newly added genes or the length of evolutionary time is known. CONCLUSIONS Gene duplication and de novo genes are two dominant evolutionary forces in shaping the coexpression network. Both duplication genes and de novo genes develop new interactions through a "rich-gets-richer" mechanism in terms of degree and coreness. This mechanism leads to the scale-free property and hierarchical architecture of biomolecular network. The proposed model is able to construct a gene coexpression network with comprehensive biological characteristics.
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Affiliation(s)
- Yuexi Gu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Jian Zu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Yu Li
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
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16
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Doostparast Torshizi A, Armoskus C, Zhang H, Forrest MP, Zhang S, Souaiaia T, Evgrafov OV, Knowles JA, Duan J, Wang K. Deconvolution of transcriptional networks identifies TCF4 as a master regulator in schizophrenia. SCIENCE ADVANCES 2019; 5:eaau4139. [PMID: 31535015 PMCID: PMC6739105 DOI: 10.1126/sciadv.aau4139] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
Applying tissue-specific deconvolution of transcriptional networks to identify their master regulators (MRs) in neuropsychiatric disorders has been largely unexplored. Here, using two schizophrenia (SCZ) case-control RNA-seq datasets, one on postmortem dorsolateral prefrontal cortex (DLPFC) and another on cultured olfactory neuroepithelium, we deconvolved the transcriptional networks and identified TCF4 as a top candidate MR that may be dysregulated in SCZ. We validated TCF4 as a MR through enrichment analysis of TCF4-binding sites in induced pluripotent stem cell (hiPSC)-derived neurons and in neuroblastoma cells. We further validated the predicted TCF4 targets by knocking down TCF4 in hiPSC-derived neural progenitor cells (NPCs) and glutamatergic neurons (Glut_Ns). The perturbed TCF4 gene network in NPCs was more enriched for pathways involved in neuronal activity and SCZ-associated risk genes, compared to Glut_Ns. Our results suggest that TCF4 may serve as a MR of a gene network dysregulated in SCZ at early stages of neurodevelopment.
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Affiliation(s)
- Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chris Armoskus
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Hanwen Zhang
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
| | - Marc P. Forrest
- Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Autism and Neurodevelopment, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Siwei Zhang
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60015, USA
| | - Tade Souaiaia
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Oleg V. Evgrafov
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - James A. Knowles
- College of Medicine, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, North Shore University Health System, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL 60015, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Zilkhe Neurogenetic Institute, University of Southern California, Los Angeles, CA 90089, USA
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17
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Bogenpohl JW, Smith ML, Farris SP, Dumur CI, Lopez MF, Becker HC, Grant KA, Miles MF. Cross-Species Co-analysis of Prefrontal Cortex Chronic Ethanol Transcriptome Responses in Mice and Monkeys. Front Mol Neurosci 2019; 12:197. [PMID: 31456662 PMCID: PMC6701453 DOI: 10.3389/fnmol.2019.00197] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 07/30/2019] [Indexed: 12/20/2022] Open
Abstract
Despite recent extensive genomic and genetic studies on behavioral responses to ethanol, relatively few new therapeutic targets for the treatment of alcohol use disorder have been validated. Here, we describe a cross-species genomic approach focused on identifying gene networks associated with chronic ethanol consumption. To identify brain mechanisms underlying a chronic ethanol consumption phenotype highly relevant to human alcohol use disorder, and to elucidate potential future therapeutic targets, we conducted a genomic study in a non-human primate model of chronic open-access ethanol consumption. Microarray analysis of RNA expression in anterior cingulate and subgenual cortices from rhesus macaques was performed across multiple cohorts of animals. Gene networks correlating with ethanol consumption or showing enrichment for ethanol-regulated genes were identified, as were major ethanol-related hub genes within these networks. A subsequent consensus module analysis was used to co-analyze monkey data with expression data from a chronic intermittent ethanol vapor-exposure and consumption model in C57BL/6J mice. Ethanol-related gene networks conserved between primates and rodents were enriched for genes involved in discrete biological functions, including; myelination, synaptic transmission, chromatin modification, Golgi apparatus function, translation, cellular respiration, and RNA processing. The myelin-related network, in particular, showed strong correlations with ethanol consumption behavior and displayed marked network reorganization between control and ethanol-drinking animals. Further bioinformatics analysis revealed that these networks also showed highly significant overlap with other ethanol-regulated gene sets. Altogether, these studies provide robust primate and rodent cross-species validation of gene networks associated with chronic ethanol consumption. Our results also suggest potential novel focal points for future therapeutic interventions in alcohol use disorder.
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Affiliation(s)
- James W Bogenpohl
- Department of Molecular Biology and Chemistry, Christopher Newport University, Newport News, VA, United States
| | - Maren L Smith
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Sean P Farris
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States
| | - Catherine I Dumur
- Aurora Diagnostics-Sonic Healthcare, Bernhardt Laboratories, Jacksonville, FL, United States
| | - Marcelo F Lopez
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Howard C Becker
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Kathleen A Grant
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, United States.,Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, United States
| | - Michael F Miles
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States.,Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, United States.,Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States.,VCU Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, United States
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18
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A quantitative model of human neurodegenerative diseases involving protein aggregation. Neurobiol Aging 2019; 80:46-55. [DOI: 10.1016/j.neurobiolaging.2019.04.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/02/2019] [Accepted: 04/02/2019] [Indexed: 12/12/2022]
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19
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Wang Y, Gao S, Zhao Y, Chen WH, Shao JJ, Wang NN, Li M, Zhou GX, Wang L, Shen WJ, Xu JT, Deng WD, Wang W, Chen YL, Jiang Y. Allele-specific expression and alternative splicing in horse×donkey and cattle×yak hybrids. Zool Res 2019; 40:293-304. [PMID: 31271004 PMCID: PMC6680129 DOI: 10.24272/j.issn.2095-8137.2019.042] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Divergence of gene expression and alternative splicing is a crucial driving force in the evolution of species; to date, however the molecular mechanism remains unclear. Hybrids of closely related species provide a suitable model to analyze allele-specific expression (ASE) and allele-specific alternative splicing (ASS). Analysis of ASE and ASS can uncover the differences in cis-regulatory elements between closely related species, while eliminating interference of trans-regulatory elements. Here, we provide a detailed characterization of ASE and ASS from 19 and 10 transcriptome datasets across five tissues from reciprocal-cross hybrids of horse×donkey (mule/hinny) and cattle×yak (dzo), respectively. Results showed that 4.8%-8.7% and 10.8%-16.7% of genes exhibited ASE and ASS, respectively. Notably, lncRNAs and pseudogenes were more likely to show ASE than protein-coding genes. In addition, genes showing ASE and ASS in mule/hinny were found to be involved in the regulation of muscle strength, whereas those of dzo were involved in high-altitude adaptation. In conclusion, our study demonstrated that exploration of genes showing ASE and ASS in hybrids of closely related species is feasible for species evolution research.
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Affiliation(s)
- Yu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Shan Gao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Yue Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Wei-Huang Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Jun-Jie Shao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Ni-Ni Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Guang-Xian Zhou
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Lei Wang
- Stake Key Laboratory of Plateau Ecology and Agriculture, Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining Qinghai 810016, China
| | - Wen-Jing Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming Yunnan 650223, China
| | - Jing-Tao Xu
- Stake Key Laboratory of Plateau Ecology and Agriculture, Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining Qinghai 810016, China
| | - Wei-Dong Deng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming Yunnan 650223, China
| | - Wen Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming Yunnan 650223, China
| | - Yu-Lin Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling Shaanxi 712100, China
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20
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Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors. SCIENCE CHINA-LIFE SCIENCES 2019; 62:594-608. [PMID: 30919280 DOI: 10.1007/s11427-019-9483-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 11/05/2018] [Indexed: 12/23/2022]
Abstract
We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.
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21
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Ghiselli F, Iannello M, Puccio G, Chang PL, Plazzi F, Nuzhdin SV, Passamonti M. Comparative Transcriptomics in Two Bivalve Species Offers Different Perspectives on the Evolution of Sex-Biased Genes. Genome Biol Evol 2018; 10:1389-1402. [PMID: 29897459 PMCID: PMC6007409 DOI: 10.1093/gbe/evy082] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2018] [Indexed: 12/13/2022] Open
Abstract
Comparative genomics has become a central tool for evolutionary biology, and a better knowledge of understudied taxa represents the foundation for future work. In this study, we characterized the transcriptome of male and female mature gonads in the European clam Ruditapes decussatus, compared with that in the Manila clam Ruditapes philippinarum providing, for the first time in bivalves, information about transcription dynamics and sequence evolution of sex-biased genes. In both the species, we found a relatively low number of sex-biased genes (1,284, corresponding to 41.3% of the orthologous genes between the two species), probably due to the absence of sexual dimorphism, and the transcriptional bias is maintained in only 33% of the orthologs. The dN/dS is generally low, indicating purifying selection, with genes where the female-biased transcription is maintained between the two species showing a significantly higher dN/dS. Genes involved in embryo development, cell proliferation, and maintenance of genome stability show a faster sequence evolution. Finally, we report a lack of clear correlation between transcription level and evolutionary rate in these species, in contrast with studies that reported a negative correlation. We discuss such discrepancy and call into question some methodological approaches and rationales generally used in this type of comparative studies.
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Affiliation(s)
- Fabrizio Ghiselli
- Department of Biological, Geological, and Environmental Sciences, University of Bologna, Italy
| | - Mariangela Iannello
- Department of Biological, Geological, and Environmental Sciences, University of Bologna, Italy
| | - Guglielmo Puccio
- Department of Biological, Geological, and Environmental Sciences, University of Bologna, Italy
| | - Peter L Chang
- Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, USA
| | - Federico Plazzi
- Department of Biological, Geological, and Environmental Sciences, University of Bologna, Italy
| | - Sergey V Nuzhdin
- Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, USA
| | - Marco Passamonti
- Department of Biological, Geological, and Environmental Sciences, University of Bologna, Italy
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22
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Masuda N, Kojaku S, Sano Y. Configuration model for correlation matrices preserving the node strength. Phys Rev E 2018; 98:012312. [PMID: 30110768 DOI: 10.1103/physreve.98.012312] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Indexed: 11/07/2022]
Abstract
Correlation matrices are a major type of multivariate data. To examine properties of a given correlation matrix, a common practice is to compare the same quantity between the original correlation matrix and reference correlation matrices, such as those derived from random matrix theory, that partially preserve properties of the original matrix. We propose a model to generate such reference correlation and covariance matrices for the given matrix. Correlation matrices are often analyzed as networks, which are heterogeneous across nodes in terms of the total connectivity to other nodes for each node. Given this background, the present algorithm generates random networks that preserve the expectation of total connectivity of each node to other nodes, akin to configuration models for conventional networks. Our algorithm is derived from the maximum entropy principle. We will apply the proposed algorithm to measurement of clustering coefficients and community detection, both of which require a null model to assess the statistical significance of the obtained results.
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Affiliation(s)
- Naoki Masuda
- Department of Engineering Mathematics, Merchant Venturers Building, University of Bristol, Woodland Road, Clifton, Bristol BS8 1UB, England, United Kingdom
| | - Sadamori Kojaku
- Department of Engineering Mathematics, Merchant Venturers Building, University of Bristol, Woodland Road, Clifton, Bristol BS8 1UB, England, United Kingdom.,CREST, JST, Kawaguchi Center Building, 4-1-8, Honcho, Kawaguchi-shi, Saitama 332-0012, Japan
| | - Yukie Sano
- Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577 Japan
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23
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Couto CMV, Comin CH, Costa LDF. Effects of threshold on the topology of gene co-expression networks. MOLECULAR BIOSYSTEMS 2018; 13:2024-2035. [PMID: 28770908 DOI: 10.1039/c7mb00101k] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.
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24
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Palakurty SX, Stinchcombe JR, Afkhami ME. Cooperation and coexpression: How coexpression networks shift in response to multiple mutualists. Mol Ecol 2018. [DOI: 10.1111/mec.14550] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
| | - John R. Stinchcombe
- Department of Ecology and Evolutionary Biology University of Toronto Toronto ON Canada
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25
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Biswas K, Acharya D, Podder S, Ghosh TC. Evolutionary rate heterogeneity between multi- and single-interface hubs across human housekeeping and tissue-specific protein interaction network: Insights from proteins' and its partners' properties. Genomics 2017; 110:283-290. [PMID: 29198610 DOI: 10.1016/j.ygeno.2017.11.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 12/12/2022]
Abstract
Integrating gene expression into protein-protein interaction network (PPIN) leads to the construction of tissue-specific (TS) and housekeeping (HK) sub-networks, with distinctive TS- and HK-hubs. All such hub proteins are divided into multi-interface (MI) hubs and single-interface (SI) hubs, where MI hubs evolve slower than SI hubs. Here we explored the evolutionary rate difference between MI and SI proteins within TS- and HK-PPIN and observed that this difference is present only in TS, but not in HK-class. Next, we explored whether proteins' own properties or its partners' properties are more influential in such evolutionary discrepancy. Statistical analyses revealed that this evolutionary rate correlates negatively with protein's own properties like expression level, miRNA count, conformational diversity and functional properties and with its partners' properties like protein disorder and tissue expression similarity. Moreover, partial correlation and regression analysis revealed that both proteins' and its partners' properties have independent effects on protein evolutionary rate.
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Affiliation(s)
- Kakali Biswas
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India
| | - Debarun Acharya
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India
| | - Soumita Podder
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India; Department of Microbiology, Raiganj University, Raiganj, Uttar Dinajpur 733134, India
| | - Tapash Chandra Ghosh
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India.
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26
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Mariño-Ramírez L, Bodenreider O, Kantz N, Jordan IK. Co-Evolutionary Rates of Functionally Related Yeast Genes. Evol Bioinform Online 2017. [DOI: 10.1177/117693430600200017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Evolutionary knowledge is often used to facilitate computational attempts at gene function prediction. One rich source of evolutionary information is the relative rates of gene sequence divergence, and in this report we explore the connection between gene evolutionary rates and function. We performed a genome-scale evaluation of the relationship between evolutionary rates and functional annotations for the yeast Saccharomyces cerevisiae. Non-synonymous ( dN) and synonymous ( dS) substitution rates were calculated for 1,095 orthologous gene sets common to S. cerevisiae and six other closely related yeast species. Differences in evolutionary rates between pairs of genes (Δ dN & Δ dS) were then compared to their functional similarities ( sGO), which were measured using Gene Ontology (GO) annotations. Substantial and statistically significant correlations were found between Δ dN and sGO, whereas there is no apparent relationship between Δ dS and sGO. These results are consistent with a mode of action for natural selection that is based on similar rates of elimination of deleterious protein coding sequence variants for functionally related genes. The connection between gene evolutionary rates and function was stronger than seen for phylogenetic profiles, which have previously been employed to inform functional inference. The co-evolution of functionally related yeast genes points to the relevance of specific function for the efficacy of natural selection and underscores the utility of gene evolutionary rates for functional predictions.
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Affiliation(s)
- Leonardo Mariño-Ramírez
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, U.S.A
| | - Olivier Bodenreider
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, U.S.A
| | - Natalie Kantz
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, U.S.A
| | - I. King Jordan
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A
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27
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Saddic LA, Sigurdsson MI, Chang TW, Mazaika E, Heydarpour M, Shernan SK, Seidman CE, Seidman JG, Aranki SF, Body SC, Muehlschlegel JD. The Long Noncoding RNA Landscape of the Ischemic Human Left Ventricle. ACTA ACUST UNITED AC 2017; 10:CIRCGENETICS.116.001534. [PMID: 28115490 DOI: 10.1161/circgenetics.116.001534] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 11/07/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND The discovery of functional classes of long noncoding RNAs (lncRNAs) has expanded our understanding of the variety of RNA species that exist in cells. In the heart, lncRNAs have been implicated in the regulation of development, ischemic and dilated cardiomyopathy, and myocardial infarction. Nevertheless, there is a limited description of expression profiles for these transcripts in human subjects. METHODS AND RESULTS We obtained left ventricular tissue from human patients undergoing cardiac surgery and used RNA sequencing to describe an lncRNA profile. We then identified a list of lncRNAs that were differentially expressed between pairs of samples before and after the ischemic insult of cardiopulmonary bypass. The expression of some of these lncRNAs correlates with ischemic time. Coding genes in close proximity to differentially expressed lncRNAs and coding genes that have coordinated expression with these lncRNAs are enriched in functional categories related to myocardial infarction, including heart function, metabolism, the stress response, and the immune system. CONCLUSIONS We describe a list of lncRNAs that are differentially expressed after ischemia in the human heart. These genes are predicted to function in pathways consistent with myocardial injury. As a result, lncRNAs may serve as novel diagnostic and therapeutic targets for ischemic heart disease. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00985049.
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Affiliation(s)
- Louis A Saddic
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Martin I Sigurdsson
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Tzuu-Wang Chang
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Erica Mazaika
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Mahyar Heydarpour
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Stanton K Shernan
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Christine E Seidman
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Jon G Seidman
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Sary F Aranki
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Simon C Body
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA
| | - Jochen D Muehlschlegel
- From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital (L.A.S., M.I.S., T.-W.C., M.H., S.K.S., S.C.B., J.D.M.), Division of Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital (S.F.A.), and Department of Genetics (E.M., C.E.S., J.G.S.), Harvard Medical School, Boston, MA.
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Wang HL, Liu J, Qin ZM. A novel method to identify differential pathways in uterine leiomyomata based on network strategy. Oncol Lett 2017; 14:5765-5772. [PMID: 29113205 PMCID: PMC5661392 DOI: 10.3892/ol.2017.6928] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 06/02/2017] [Indexed: 01/21/2023] Open
Abstract
The aim of the present study was to identify differential pathways in uterine leiomyomata (UL) using a novel method based on protein-protein interaction networks and pathway analysis. The pathway networks were constructed by examining the intersections of the Reactome database and the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) protein-protein interaction (PPI) networks. The Objective network was defined as the differential expressed genes (DEGs) associated with the interactions identified by STRING. Topological centrality (degree) analysis was performed for the Objective network to explore the hub genes and hub networks. Subsequent to isolating the intersections between the Pathway and Objective networks, randomization tests were conducted to identify the differential pathways. There were 559,598 interactions in the Pathway networks. A total of 657 genes with 3,835 interactions were mapped in the Objective network, which included 20 hub genes. It was identified that 358 pathways demonstrated interaction with the Objective network, such as Signal Transduction, Immune System and Signaling by G-protein-coupled receptor (GPCR). By accessing the randomization tests, P-values of these pathways were close to 0, which indicated that they were significantly different. The present study successfully identified differential pathways (such as signal transduction, immune system and signaling by GPCR) in UL, which may be potential biomarkers in the detection and treatment of UL.
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Affiliation(s)
- Hui-Ling Wang
- First Gynecological Ward, Binzhou People's Hospital, Binzhou, Shandong 256610, P.R. China
| | - Jing Liu
- Department of Health Checkup, Binzhou People's Hospital, Binzhou, Shandong 256610, P.R. China
| | - Zhao-Min Qin
- Department of Nursing, Shandong Medical College, Jinan, Shandong 250002, P.R. China
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29
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Xu C, Qi R, Ping Y, Li J, Zhao H, Wang L, Du MY, Xiao Y, Li X. Systemically identifying and prioritizing risk lncRNAs through integration of pan-cancer phenotype associations. Oncotarget 2017; 8:12041-12051. [PMID: 28076842 PMCID: PMC5355324 DOI: 10.18632/oncotarget.14510] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 12/12/2016] [Indexed: 02/01/2023] Open
Abstract
LncRNAs have emerged as a major class of regulatory molecules involved in normal cellular physiology and disease, our knowledge of lncRNAs is very limited and it has become a major research challenge in discovering novel disease-related lncRNAs in cancers. Based on the assumption that diverse diseases with similar phenotype associations show similar molecular mechanisms, we presented a pan-cancer network-based prioritization approach to systematically identify disease-specific risk lncRNAs by integrating disease phenotype associations. We applied this strategy to approximately 2800 tumor samples from 14 cancer types for prioritizing disease risk lncRNAs. Our approach yielded an average area under the ROC curve (AUC) of 80.66%, with the highest AUC (98.14%) for medulloblastoma. When evaluated using leave-one-out cross-validation (LOOCV) for prioritization of disease candidate genes, the average AUC score of 97.16% was achieved. Moreover, we demonstrated the robustness as well as the integrative importance of this approach, including disease phenotype associations, known disease genes and the numbers of cancer types. Taking glioblastoma multiforme as a case study, we identified a candidate lncRNA gene SNHG1 as a novel disease risk factor for disease diagnosis and prognosis. In summary, we provided a novel lncRNA prioritization approach by integrating pan-cancer phenotype associations that could help researchers better understand the important roles of lncRNAs in human cancers.
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Affiliation(s)
- Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jie Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | | | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.,Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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30
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Quantifying Network Dynamics and Information Flow Across Chinese Social Media During the African Ebola Outbreak. Disaster Med Public Health Prep 2017; 12:26-37. [PMID: 28760166 DOI: 10.1017/dmp.2017.29] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Social media provides us with a new platform on which to explore how the public responds to disasters and, of particular importance, how they respond to the emergence of infectious diseases such as Ebola. Provided it is appropriately informed, social media offers a potentially powerful means of supporting both early detection and effective containment of communicable diseases, which is essential for improving disaster medicine and public health preparedness. METHODS The 2014 West African Ebola outbreak is a particularly relevant contemporary case study on account of the large number of annual arrivals from Africa, including Chinese employees engaged in projects in Africa. Weibo (Weibo Corp, Beijing, China) is China's most popular social media platform, with more than 2 billion users and over 300 million daily posts, and offers great opportunity to monitor early detection and promotion of public health awareness. RESULTS We present a proof-of-concept study of a subset of Weibo posts during the outbreak demonstrating potential and identifying priorities for improving the efficacy and accuracy of information dissemination. We quantify the evolution of the social network topology within Weibo relating to the efficacy of information sharing. CONCLUSIONS We show how relatively few nodes in the network can have a dominant influence over both the quality and quantity of the information shared. These findings make an important contribution to disaster medicine and public health preparedness from theoretical and methodological perspectives for dealing with epidemics. (Disaster Med Public Health Preparedness. 2018;12:26-37).
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31
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Josephs EB, Wright SI, Stinchcombe JR, Schoen DJ. The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora. Genome Biol Evol 2017; 9:1099-1109. [PMID: 28402527 PMCID: PMC5408089 DOI: 10.1093/gbe/evx068] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2017] [Indexed: 12/12/2022] Open
Abstract
Interactions between genes can have important consequences for how selection shapes sequence variation at these genes. Specifically, genes that have pleiotropic effects by affecting the expression level of many other genes may be under stronger selective constraint. We used coexpression networks to measure connectivity between genes and investigated the relationship between gene connectivity and selection in a natural population of the plant Capsella grandiflora. We observed that network connectivity was negatively correlated with genetic divergence due to stronger negative selection on highly-connected genes even when controlling for variation in gene expression level. However, the presence of local regulatory variation for a gene's expression level was also associated with reduced negative selection and lower gene connectivity. While it is difficult to disentangle the causal relationships between these factors, our results show that both connectivity and local regulatory variation are important factors for explaining variation in selection between genes.
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Affiliation(s)
- Emily B. Josephs
- Department of Evolution and Ecology, University of California, Davis
| | - Stephen I. Wright
- Department of Ecology and Evolutionary Biology, University of Toronto, Ontario, Canada
| | - John R. Stinchcombe
- Department of Ecology and Evolutionary Biology, University of Toronto, Ontario, Canada
| | - Daniel J. Schoen
- Department of Biology, McGill University, Stewart Biology Building, Montreal, Quebec, Canada
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32
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Abstract
The wealth of available genetic information is allowing the reconstruction of human demographic and adaptive history. Demography and purifying selection affect the purge of rare, deleterious mutations from the human population, whereas positive and balancing selection can increase the frequency of advantageous variants, improving survival and reproduction in specific environmental conditions. In this review, I discuss how theoretical and empirical population genetics studies, using both modern and ancient DNA data, are a powerful tool for obtaining new insight into the genetic basis of severe disorders and complex disease phenotypes, rare and common, focusing particularly on infectious disease risk.
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Affiliation(s)
- Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Department of Genomes & Genetics, Institut Pasteur, Paris, 75015, France.
- Centre National de la Recherche Scientifique, URA3012, Paris, 75015, France.
- Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, 75015, France.
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33
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Chen J, Yang HT, Li Z, Xu N, Yu B, Xu JP, Zhao PG, Wang Y, Zhang XJ, Lin DJ. Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm. Oncol Lett 2016; 12:1792-1800. [PMID: 27588126 PMCID: PMC4998145 DOI: 10.3892/ol.2016.4822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 12/01/2015] [Indexed: 12/14/2022] Open
Abstract
Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.
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Affiliation(s)
- Juan Chen
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China; Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Hai-Tao Yang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Zhu Li
- Department of Hepatobiliary Surgery, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Ning Xu
- Department of Respiratory Medicine, Weihai Municipal Hospital, Weihai, Shandong 264200, P.R. China
| | - Bo Yu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Jun-Ping Xu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Pei-Ge Zhao
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Yan Wang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Xiu-Juan Zhang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Dian-Jie Lin
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China
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Görnerup O, Gillblad D, Vasiloudis T. Domain-agnostic discovery of similarities and concepts at scale. Knowl Inf Syst 2016. [DOI: 10.1007/s10115-016-0984-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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35
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He F, Karve AA, Maslov S, Babst BA. Large-Scale Public Transcriptomic Data Mining Reveals a Tight Connection between the Transport of Nitrogen and Other Transport Processes in Arabidopsis. FRONTIERS IN PLANT SCIENCE 2016; 7:1207. [PMID: 27563305 PMCID: PMC4981021 DOI: 10.3389/fpls.2016.01207] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 07/29/2016] [Indexed: 05/29/2023]
Abstract
Movement of nitrogen to the plant tissues where it is needed for growth is an important contribution to nitrogen use efficiency. However, we have very limited knowledge about the mechanisms of nitrogen transport. Loading of nitrogen into the xylem and/or phloem by transporter proteins is likely important, but there are several families of genes that encode transporters of nitrogenous molecules (collectively referred to as N transporters here), each comprised of many gene members. In this study, we leveraged publicly available microarray data of Arabidopsis to investigate the gene networks of N transporters to elucidate their possible biological roles. First, we showed that tissue-specificity of nitrogen (N) transporters was well reflected among the public microarray data. Then, we built coexpression networks of N transporters, which showed relationships between N transporters and particular aspects of plant metabolism, such as phenylpropanoid biosynthesis and carbohydrate metabolism. Furthermore, genes associated with several biological pathways were found to be tightly coexpressed with N transporters in different tissues. Our coexpression networks provide information at the systems-level that will serve as a resource for future investigation of nitrogen transport systems in plants, including candidate gene clusters that may work together in related biological roles.
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Affiliation(s)
- Fei He
- Biological, Environmental and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
| | - Abhijit A. Karve
- Biological, Environmental and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- Purdue Research FoundationWest Lafayette, IN, USA
| | - Sergei Maslov
- Biological, Environmental and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- Department of Bioengineering, Carl R. Woese Institute for Genomic Biology, National Center for Supercomputing Applications, University of Illinois at Urbana-ChampaignUrbana, IL, USA
| | - Benjamin A. Babst
- Biological, Environmental and Climate Sciences Department, Brookhaven National LaboratoryUpton, NY, USA
- Arkansas Forest Resources Center, The University of Arkansas at MonticelloMonticello, AR, USA
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36
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Yue H, Yang BO, Yang F, Hu XL, Kong FB. Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm. Exp Ther Med 2016; 11:1707-1715. [PMID: 27168792 PMCID: PMC4840697 DOI: 10.3892/etm.2016.3131] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 01/07/2016] [Indexed: 12/15/2022] Open
Abstract
Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis.
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Affiliation(s)
- Hong Yue
- Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China
| | - B O Yang
- Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China
| | - Fang Yang
- Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China
| | - Xiao-Li Hu
- Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China
| | - Fan-Bin Kong
- Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China
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Aoki Y, Okamura Y, Tadaka S, Kinoshita K, Obayashi T. ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression. PLANT & CELL PHYSIOLOGY 2016; 57:e5. [PMID: 26546318 PMCID: PMC4722172 DOI: 10.1093/pcp/pcv165] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 10/20/2015] [Indexed: 05/17/2023]
Abstract
ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.
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Affiliation(s)
- Yuichi Aoki
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
| | - Yasunobu Okamura
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
| | - Shu Tadaka
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
| | - Kengo Kinoshita
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan Institute of Development, Aging, and Cancer, Tohoku University, Sendai, 980-8575 Japan Tohoku Medical Megabank Organization, Tohoku University, Sendai, 980-8573 Japan
| | - Takeshi Obayashi
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
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César-Razquin A, Snijder B, Frappier-Brinton T, Isserlin R, Gyimesi G, Bai X, Reithmeier RA, Hepworth D, Hediger MA, Edwards AM, Superti-Furga G. A Call for Systematic Research on Solute Carriers. Cell 2015; 162:478-87. [PMID: 26232220 DOI: 10.1016/j.cell.2015.07.022] [Citation(s) in RCA: 381] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Indexed: 01/10/2023]
Abstract
Solute carrier (SLC) membrane transport proteins control essential physiological functions, including nutrient uptake, ion transport, and waste removal. SLCs interact with several important drugs, and a quarter of the more than 400 SLC genes are associated with human diseases. Yet, compared to other gene families of similar stature, SLCs are relatively understudied. The time is right for a systematic attack on SLC structure, specificity, and function, taking into account kinship and expression, as well as the dependencies that arise from the common metabolic space.
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Affiliation(s)
- Adrián César-Razquin
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Berend Snijder
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | - Ruth Isserlin
- The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S 3E1, Canada
| | - Gergely Gyimesi
- Institute of Biochemistry and Molecular Medicine and Swiss National Center of Competence in Research, NCCR TransCure, University of Bern, 3012 Bern, Switzerland
| | - Xiaoyun Bai
- Department of Biochemistry, University of Toronto, Toronto, Ontario, M5S 1A8 Canada
| | | | - David Hepworth
- Worldwide Medicinal Chemistry, Pfizer Worldwide Research and Development, Cambridge, MA 02139, USA
| | - Matthias A Hediger
- Institute of Biochemistry and Molecular Medicine and Swiss National Center of Competence in Research, NCCR TransCure, University of Bern, 3012 Bern, Switzerland.
| | - Aled M Edwards
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
| | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria; Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria.
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Ruan D, Young A, Montana G. Differential analysis of biological networks. BMC Bioinformatics 2015; 16:327. [PMID: 26453322 PMCID: PMC4600256 DOI: 10.1186/s12859-015-0735-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 08/18/2015] [Indexed: 12/13/2022] Open
Abstract
Background In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. Results We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. Conclusions We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.
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Affiliation(s)
- Da Ruan
- Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
| | - Alastair Young
- Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
| | - Giovanni Montana
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. .,Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
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40
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Gu X. Understanding tissue expression evolution: from expression phylogeny to phylogenetic network. Brief Bioinform 2015; 17:249-54. [PMID: 26141828 DOI: 10.1093/bib/bbv041] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Indexed: 01/07/2023] Open
Abstract
Our understanding of tissue expression evolution in multi-cellular model organisms has been considerably advanced with the help of high-throughput technologies from EST, microarray to RNA-seq. Yet, many controversies remained unsolved, ranging from the evolutionary patterns of tissue expressions to expression phylogenetic analysis. Moreover, despite numerous reports published, it is desirable to have a general framework for study of tissue expression evolution. In this article, we first provide an up-to-date and concise review for the study of tissue expression evolution in multi-cellular organisms. While the expression phylogeny of the same tissues sampled from closely or intermediately related species largely reflects the species phylogeny, we demonstrate that phylogenetic network approach may shed some lights for our understanding of the developmental similarity and evolutionary relatedness during the multi-tissue evolution.
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Irizar H, Muñoz-Culla M, Sáenz-Cuesta M, Osorio-Querejeta I, Sepúlveda L, Castillo-Triviño T, Prada A, Lopez de Munain A, Olascoaga J, Otaegui D. Identification of ncRNAs as potential therapeutic targets in multiple sclerosis through differential ncRNA - mRNA network analysis. BMC Genomics 2015; 16:250. [PMID: 25880556 PMCID: PMC4391585 DOI: 10.1186/s12864-015-1396-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 02/24/2015] [Indexed: 11/30/2022] Open
Abstract
Background Several studies have revealed a potential role for both small nucleolar RNAs (snoRNAs) and microRNAs (miRNAs) in the physiopathology of relapsing-remitting multiple sclerosis (RRMS). This potential implication has been mainly described through differential expression studies. However, it has been suggested that, in order to extract additional information from large-scale expression experiments, differential expression studies must be complemented with differential network studies. Thus, the present work is aimed at the identification of potential therapeutic ncRNA targets for RRMS through differential network analysis of ncRNA – mRNA coexpression networks. ncRNA – mRNA coexpression networks have been constructed from both selected ncRNA (specifically miRNAs, snoRNAs and sdRNAs) and mRNA large-scale expression data obtained from 22 patients in relapse, the same 22 patients in remission and 22 healthy controls. Condition-specific (relapse, remission and healthy) networks have been built and compared to identify the parts of the system most affected by perturbation and aid the identification of potential therapeutic targets among the ncRNAs. Results All the coexpression networks we built present a scale-free topology and many snoRNAs are shown to have a prominent role in their architecture. The differential network analysis (relapse vs. remission vs. controls’ networks) has revealed that, although both network topology and the majority of the genes are maintained, few ncRNA – mRNA links appear in more than one network. We have selected as potential therapeutic targets the ncRNAs that appear in the disease-specific network and were found to be differentially expressed in a previous study. Conclusions Our results suggest that the diseased state of RRMS has a strong impact on the ncRNA – mRNA network of peripheral blood leukocytes, as a massive rewiring of the network happens between conditions. Our findings also indicate that the role snoRNAs have in targeted gene silencing is a widespread phenomenon. Finally, among the potential therapeutic target ncRNAs, SNORA40 seems to be the most promising candidate. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1396-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Haritz Irizar
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM), San Sebastián, Spain.
| | - Maider Muñoz-Culla
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM), San Sebastián, Spain.
| | - Matías Sáenz-Cuesta
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM) and Immunology Department, Donostia University Hospital, San Sebastián, Spain.
| | - Iñaki Osorio-Querejeta
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM), San Sebastián, Spain.
| | - Lucía Sepúlveda
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM), San Sebastián, Spain.
| | - Tamara Castillo-Triviño
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM) and Neurology Department, Donostia University Hospital, San Sebastián, Spain.
| | - Alvaro Prada
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Immunology Department, Donostia University Hospital, San Sebastián, Spain.
| | - Adolfo Lopez de Munain
- Biodonostia Health Research Institute, San Sebastián, Spain. .,Department of Neurology, Donostia University Hospital, Donostia - San Sebastián, Spain. .,Centro de Investigación Biomédica en red Enfermedades Neurodegenerativas (CIBERNED) and Department of Neuroscience, University of the Basque Country (UVP/EHU), San Sebastián, Spain.
| | - Javier Olascoaga
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM) and Neurology Department, Donostia University Hospital, San Sebastián, Spain.
| | - David Otaegui
- Multiple Sclerosis group, Biodonostia Health Research Institute, Paseo Dr. Begiristain s/n, San Sebastián, 20001, Spain. .,Spanish Network on Multiple Sclerosis (REEM), San Sebastián, Spain.
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Amrine KCH, Blanco-Ulate B, Cantu D. Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis. PLoS One 2015; 10:e0118731. [PMID: 25730421 PMCID: PMC4346582 DOI: 10.1371/journal.pone.0118731] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2014] [Accepted: 01/08/2015] [Indexed: 01/26/2023] Open
Abstract
Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different experimental conditions and biological systems, into a functionally meaningful co-expression network.
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Affiliation(s)
- Katherine C. H. Amrine
- Department of Viticulture and Enology, University of California Davis, Davis, California, United States of America
| | - Barbara Blanco-Ulate
- Department of Viticulture and Enology, University of California Davis, Davis, California, United States of America
| | - Dario Cantu
- Department of Viticulture and Enology, University of California Davis, Davis, California, United States of America
- * E-mail:
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43
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Vinogradov AE. Consolidation of slow or fast but not moderately evolving genes at the level of pathways and processes. Gene 2015; 561:30-4. [PMID: 25707747 DOI: 10.1016/j.gene.2015.01.066] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Revised: 01/04/2015] [Accepted: 01/09/2015] [Indexed: 11/15/2022]
Abstract
Conservatism versus innovation is probably the most important dichotomy of all evolving systems. In molecular evolution the distinction between conservative (negative) selection, innovative (positive) selection and unconstrained evolution (drift) is usually ambiguous at the gene level. Only rare cases with the ratio of nonsynonymous to synonymous nucleotide substitutions above unity (dN/dS>1) are thought to be due to positive selection, whereas the lower dN/dS ratio may indicate negative selection in combination with drift. The density of the dN/dS ratio for orthologous genes forms a unimodal distribution where no particular regions can be discerned. Here it is shown that at the level of overrepresented pathways and processes the picture is strikingly different. The distribution is strongly polarized with a wide completely depressed middle part. This three-phase distribution is very robust. It is observed with various substitution models and remains at very low significance of overrepresentation (up to p<0.99). This fact suggests consolidation of either negative or positive selection but not of unconstrained evolution at the level of pathways/processes. The effect is demonstrated for different phylogenetic distances: from human to other primates, mammals and vertebrates. This approach suggests estimating the boundaries for conservative and innovative selection using the pathway/process level. Emphasizing the role of a critical mass of negatively or positively selected genes in a pathway/process, it can elucidate how the bridge between 'tinkering' at the gene level and 'design' at the higher levels is forming.
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Eidem HR, McGary KL, Rokas A. Shared Selective Pressures on Fungal and Human Metabolic Pathways Lead to Divergent yet Analogous Genetic Responses. Mol Biol Evol 2015; 32:1449-55. [DOI: 10.1093/molbev/msv034] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
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45
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Qian W, Zhou H, Tang K. Recent coselection in human populations revealed by protein-protein interaction network. Genome Biol Evol 2014; 7:136-53. [PMID: 25532814 PMCID: PMC4316623 DOI: 10.1093/gbe/evu270] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein-protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome.
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Affiliation(s)
- Wei Qian
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Hang Zhou
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kun Tang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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Xu L, Zhao F, Ren H, Li L, Lu J, Liu J, Zhang S, Liu GE, Song J, Zhang L, Wei C, Du L. Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages. Int J Biol Sci 2014; 10:1039-50. [PMID: 25285036 PMCID: PMC4183924 DOI: 10.7150/ijbs.9737] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2014] [Accepted: 08/16/2014] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Muscle development and lipid metabolism play important roles during fetal development stages. The commercial Texel sheep are more muscular than the indigenous Ujumqin sheep. RESULTS We performed serial transcriptomics assays and systems biology analyses to investigate the dynamics of gene expression changes associated with fetal longissimus muscles during different fetal stages in two sheep breeds. Totally, we identified 1472 differentially expressed genes during various fetal stages using time-series expression analysis. A systems biology approach, weighted gene co-expression network analysis (WGCNA), was used to detect modules of correlated genes among these 1472 genes. Dramatically different gene modules were identified in four merged datasets, corresponding to the mid fetal stage in Texel and Ujumqin sheep, the late fetal stage in Texel and Ujumqin sheep, respectively. We further detected gene modules significantly correlated with fetal weight, and constructed networks and pathways using genes with high significances. In these gene modules, we identified genes like TADA3, LMNB1, TGF-β3, EEF1A2, FGFR1, MYOZ1, and FBP2 correlated with fetal weight. CONCLUSION Our study revealed the complex network characteristics involved in muscle development and lipid metabolism during fetal development stages. Diverse patterns of the network connections observed between breeds and fetal stages could involve some hub genes, which play central roles in fetal development, correlating with fetal weight. Our findings could provide potential valuable biomarkers for selection of body weight-related traits in sheep and other livestock.
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Affiliation(s)
- Lingyang Xu
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China; ; 4. Animal Genomics and Improvement Laboratory, U.S. Department of Agriculture-Agricultural Research Services, Beltsville, Maryland 20705, USA; ; 5. Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Fuping Zhao
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Hangxing Ren
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China; ; 2. Chongqing Academy of Animal Sciences, Chongqing, 402460, China
| | - Li Li
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China; ; 3. College of Animal Science and Technology, Sichuan Agricultural University, Ya'an, Sichuan, 625014, China
| | - Jian Lu
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiasen Liu
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Shifang Zhang
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - George E Liu
- 4. Animal Genomics and Improvement Laboratory, U.S. Department of Agriculture-Agricultural Research Services, Beltsville, Maryland 20705, USA
| | - Jiuzhou Song
- 5. Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Li Zhang
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Caihong Wei
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lixin Du
- 1. National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
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OrthoClust: an orthology-based network framework for clustering data across multiple species. Genome Biol 2014; 15:R100. [PMID: 25249401 PMCID: PMC4289247 DOI: 10.1186/gb-2014-15-8-r100] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2014] [Accepted: 06/26/2014] [Indexed: 01/28/2023] Open
Abstract
Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species. It outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific. We demonstrate the application of OrthoClust using the RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium. A potential application of cross-species modules is to infer putative analogous functions of uncharacterized elements like non-coding RNAs based on guilt-by-association.
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Temesi G, Virág V, Hadadi E, Ungvári I, Fodor LE, Bikov A, Nagy A, Gálffy G, Tamási L, Horváth I, Kiss A, Hullám G, Gézsi A, Sárközy P, Antal P, Buzás E, Szalai C. Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations. ALLERGY, ASTHMA & IMMUNOLOGY RESEARCH 2014; 6:496-503. [PMID: 25374748 PMCID: PMC4214969 DOI: 10.4168/aair.2014.6.6.496] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Revised: 03/10/2014] [Accepted: 04/14/2014] [Indexed: 12/03/2022]
Abstract
Purpose Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma. Methods In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals. t-tests, chi-square tests, and logistic regression were carried out in order to assess associations of SNP frequency and expression level with asthma. Permutation tests were performed to account for multiple hypothesis testing. Results The frequency of 4 SNPs in 2 genes differed significantly between asthmatic and control subjects: SNPs rs2240572, rs2240571, rs3735222 in gene SCIN, and rs32588 in gene PPARGC1B. Carriers of the minor alleles had reduced risk of asthma with an odds ratio of 0.64 (0.51-0.80; P=7×10-5) in SCIN and 0.56 (0.42-0.76; P=1.2×10-4) in PPARGC1B. The expression levels of SCIN, PPARGC1B and ITLN1 genes were significantly lower in the sputum of asthmatics. Conclusions Three potentially novel asthma-associated genes were identified based on mouse experiments and human studies.
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Affiliation(s)
- Gergely Temesi
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Viktor Virág
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Eva Hadadi
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary. ; Department of Cardiovascular Science, University of Sheffield, Sheffield, UK
| | - Ildikó Ungvári
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Lili E Fodor
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - András Bikov
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | | | - Gabriella Gálffy
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Lilla Tamási
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Ildikó Horváth
- Department of Pulmonology, Semmelweis University, Budapest, Hungary. ; Ministry of National Resources, Budapest, Hungary
| | - András Kiss
- Heim, Pal Children Hospital, Budapest, Hungary
| | - Gábor Hullám
- Department of Measurement and Information Systems, University of Technology and Economics, Budapest, Hungary
| | - András Gézsi
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Péter Sárközy
- Department of Measurement and Information Systems, University of Technology and Economics, Budapest, Hungary
| | - Péter Antal
- Department of Measurement and Information Systems, University of Technology and Economics, Budapest, Hungary
| | - Edit Buzás
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Csaba Szalai
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary. ; Heim, Pal Children Hospital, Budapest, Hungary. ; Csertex Research Laboratory, Budapest, Hungary
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Systematic identification of cell-wall related genes in Populus based on analysis of functional modules in co-expression network. PLoS One 2014; 9:e95176. [PMID: 24736620 PMCID: PMC3988181 DOI: 10.1371/journal.pone.0095176] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Accepted: 03/24/2014] [Indexed: 12/02/2022] Open
Abstract
The identification of novel genes relevant to plant cell wall (PCW) biosynthesis in Populus is a highly important and challenging problem. We surveyed candidate Populus cell wall genes using a non-targeted approach. First, a genome-wide Populus gene co-expression network (PGCN) was constructed using microarray data available in the public domain. Module detection was then performed, followed by gene ontology (GO) enrichment analysis, to assign the functional category to these modules. Based on GO annotation, the modules involved in PCW biosynthesis were then selected and analyzed in detail to annotate the candidate PCW genes in these modules, including gene annotation, expression of genes in different tissues, and so on. We examined the overrepresented cis-regulatory elements (CREs) in the gene promoters to understand the possible transcriptionally co-regulated relationships among the genes within the functional modules of cell wall biosynthesis. PGCN contains 6,854 nodes (genes) with 324,238 edges. The topological properties of the network indicate scale-free and modular behavior. A total of 435 modules were identified; among which, 67 modules were identified by overrepresented GO terms. Six modules involved in cell wall biosynthesis were identified. Module 9 was mainly involved in cellular polysaccharide metabolic process in the primary cell wall, whereas Module 4 comprises genes involved in secondary cell wall biogenesis. In addition, we predicted and analyzed 10 putative CREs in the promoters of the genes in Module 4 and Module 9. The non-targeted approach of gene network analysis and the data presented here can help further identify and characterize cell wall related genes in Populus.
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Kepp KP, Dasmeh P. A model of proteostatic energy cost and its use in analysis of proteome trends and sequence evolution. PLoS One 2014; 9:e90504. [PMID: 24587382 PMCID: PMC3938754 DOI: 10.1371/journal.pone.0090504] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Accepted: 02/03/2014] [Indexed: 12/25/2022] Open
Abstract
A model of proteome-associated chemical energetic costs of cells is derived from protein-turnover kinetics and protein folding. Minimization of the proteostatic maintenance cost can explain a range of trends of proteomes and combines both protein function, stability, size, proteostatic cost, temperature, resource availability, and turnover rates in one simple framework. We then explore the ansatz that the chemical energy remaining after proteostatic maintenance is available for reproduction (or cell division) and thus, proportional to organism fitness. Selection for lower proteostatic costs is then shown to be significant vs. typical effective population sizes of yeast. The model explains and quantifies evolutionary conservation of highly abundant proteins as arising both from functional mutations and from changes in other properties such as stability, cost, or turnover rates. We show that typical hypomorphic mutations can be selected against due to increased cost of compensatory protein expression (both in the mutated gene and in related genes, i.e. epistasis) rather than compromised function itself, although this compensation depends on the protein's importance. Such mutations exhibit larger selective disadvantage in abundant, large, synthetically costly, and/or short-lived proteins. Selection against increased turnover costs of less stable proteins rather than misfolding toxicity per se can explain equilibrium protein stability distributions, in agreement with recent findings in E. coli. The proteostatic selection pressure is stronger at low metabolic rates (i.e. scarce environments) and in hot habitats, explaining proteome adaptations towards rough environments as a question of energy. The model may also explain several trade-offs observed in protein evolution and suggests how protein properties can coevolve to maintain low proteostatic cost.
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Affiliation(s)
- Kasper P. Kepp
- Department of Chemistry, Technical University of Denmark, Kongens Lyngby, Denmark
- * E-mail:
| | - Pouria Dasmeh
- Department of Chemistry, Technical University of Denmark, Kongens Lyngby, Denmark
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