51
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Chow MZY, Sadrian SN, Keung W, Geng L, Ren L, Kong CW, Wong AOT, Hulot JS, Chen CS, Costa KD, Hajjar RJ, Li RA. Modulation of chromatin remodeling proteins SMYD1 and SMARCD1 promotes contractile function of human pluripotent stem cell-derived ventricular cardiomyocyte in 3D-engineered cardiac tissues. Sci Rep 2019; 9:7502. [PMID: 31097748 PMCID: PMC6522495 DOI: 10.1038/s41598-019-42953-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 04/11/2019] [Indexed: 02/07/2023] Open
Abstract
Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have the ability of differentiating into functional cardiomyocytes (CMs) for cell replacement therapy, tissue engineering, drug discovery and toxicity screening. From a scale-free, co-expression network analysis of transcriptomic data that distinguished gene expression profiles of undifferentiated hESC, hESC-, fetal- and adult-ventricular(V) CM, two candidate chromatin remodeling proteins, SMYD1 and SMARCD1 were found to be differentially expressed. Using lentiviral transduction, SMYD1 and SMARCD1 were over-expressed and suppressed, respectively, in single hESC-VCMs as well as the 3D constructs Cardiac Micro Tissues (CMT) and Tissue Strips (CTS) to mirror the endogenous patterns, followed by dissection of their roles in controlling cardiac gene expression, contractility, Ca2+-handling, electrophysiological functions and in vitro maturation. Interestingly, compared to independent single transductions, simultaneous SMYD1 overexpression and SMARCD1 suppression in hESC-VCMs synergistically interacted to increase the contractile forces of CMTs and CTSs with up-regulated transcripts for cardiac contractile, Ca2+-handing, and ion channel proteins. Certain effects that were not detected at the single-cell level could be unleashed under 3D environments. The two chromatin remodelers SMYD1 and SMARCD1 play distinct roles in cardiac development and maturation, consistent with the notion that epigenetic priming requires triggering signals such as 3D environmental cues for pro-maturation effects.
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Affiliation(s)
- Maggie Zi-Ying Chow
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong.,School of Biomedical Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong.,Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Shatin, Hong Kong
| | - Stephanie N Sadrian
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Wendy Keung
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong.,School of Biomedical Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong.,Dr. Li Dak-Sum Research Centre, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Lin Geng
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong.,Dr. Li Dak-Sum Research Centre, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Lihuan Ren
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Chi-Wing Kong
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong.,School of Biomedical Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Andy On-Tik Wong
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong.,School of Biomedical Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Jean-Sebastien Hulot
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sorbonne Universités, UPMC Univ Paris 06, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, F-75013, Paris, France
| | - Christopher S Chen
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,The Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, USA
| | - Kevin D Costa
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Roger J Hajjar
- Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ronald A Li
- Stem Cell and Regenerative Medicine Consortium, The University of Hong Kong, Pok Fu Lam, Hong Kong. .,School of Biomedical Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong. .,Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Shatin, Hong Kong. .,Dr. Li Dak-Sum Research Centre, The University of Hong Kong, Pok Fu Lam, Hong Kong.
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Damicelli F, Hilgetag CC, Hütt MT, Messé A. Topological reinforcement as a principle of modularity emergence in brain networks. Netw Neurosci 2019; 3:589-605. [PMID: 31157311 PMCID: PMC6542620 DOI: 10.1162/netn_a_00085] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/21/2019] [Indexed: 12/02/2022] Open
Abstract
Modularity is a ubiquitous topological feature of structural brain networks at various scales. Although a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, that is, iteratively allowing connections between non-neighbor nodes with high neighborhood similarity. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial "proto-modules," thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity (co-activations) between nodes. Results produced by the activity-based model are consistent with the ones from the purely topological rule in terms of the final network configuration and modules composition. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism contributing to modularity emergence in brain networks.
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Affiliation(s)
- Fabrizio Damicelli
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany
| | - Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
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53
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Voigt A, Almaas E. Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks. BMC Bioinformatics 2019; 20:58. [PMID: 30691386 PMCID: PMC6350380 DOI: 10.1186/s12859-019-2596-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 01/03/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified. RESULTS Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases. CONCLUSION Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.
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Affiliation(s)
- André Voigt
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
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Swarup V, Hinz FI, Rexach JE, Noguchi KI, Toyoshiba H, Oda A, Hirai K, Sarkar A, Seyfried NT, Cheng C, Haggarty SJ, Grossman M, Van Deerlin VM, Trojanowski JQ, Lah JJ, Levey AI, Kondou S, Geschwind DH. Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia. Nat Med 2019; 25:152-164. [PMID: 30510257 PMCID: PMC6602064 DOI: 10.1038/s41591-018-0223-3] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 09/18/2018] [Indexed: 02/02/2023]
Abstract
Identifying the mechanisms through which genetic risk causes dementia is an imperative for new therapeutic development. Here, we apply a multistage, systems biology approach to elucidate the disease mechanisms in frontotemporal dementia. We identify two gene coexpression modules that are preserved in mice harboring mutations in MAPT, GRN and other dementia mutations on diverse genetic backgrounds. We bridge the species divide via integration with proteomic and transcriptomic data from the human brain to identify evolutionarily conserved, disease-relevant networks. We find that overexpression of miR-203, a hub of a putative regulatory microRNA (miRNA) module, recapitulates mRNA coexpression patterns associated with disease state and induces neuronal cell death, establishing this miRNA as a regulator of neurodegeneration. Using a database of drug-mediated gene expression changes, we identify small molecules that can normalize the disease-associated modules and validate this experimentally. Our results highlight the utility of an integrative, cross-species network approach to drug discovery.
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Affiliation(s)
- Vivek Swarup
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Co-first author
| | - Flora I. Hinz
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Co-first author
| | - Jessica E. Rexach
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ken-ichi Noguchi
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa 251-8555, Japan
| | - Hiroyoshi Toyoshiba
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa 251-8555, Japan
| | - Akira Oda
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa 251-8555, Japan
| | - Keisuke Hirai
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa 251-8555, Japan
| | - Arjun Sarkar
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA,Alzheimer’s Disease Research Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA
| | - Chialin Cheng
- Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Stephen J. Haggarty
- Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - IFGC
- International FTD-Genomics Consortium, a list of members and affiliations appears at the end of the paper
| | - Murray Grossman
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Vivianna M. Van Deerlin
- The Penn FTD Center, Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John Q. Trojanowski
- The Penn FTD Center, Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - James J. Lah
- Alzheimer’s Disease Research Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA
| | - Allan I. Levey
- Alzheimer’s Disease Research Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA
| | - Shinichi Kondou
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa 251-8555, Japan
| | - Daniel H. Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
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55
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Singh D, Swarup V, Le H, Kumar V. Transcriptional Signatures in Liver Reveal Metabolic Adaptations to Seasons in Migratory Blackheaded Buntings. Front Physiol 2018; 9:1568. [PMID: 30538637 PMCID: PMC6277527 DOI: 10.3389/fphys.2018.01568] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 10/18/2018] [Indexed: 01/30/2023] Open
Abstract
The molecular underpinnings of metabolic adaptation to seasons are poorly understood in long- distance migrants. We measured changes in physiology and performed de novo sequencing of RNA extracted from liver samples collected at 4-h intervals over a period of 24 h from a long-distance avian migrant, the blackheaded bunting (Emberiza melanocephala), during two states: photostimulated vernal migratory (M) state and photorefractory non-migratory (nM) state. The M state was differentiated from the nM state based on body fattening and weight gain, as well as on Zugunruhe, that is, nocturnal migratory restlessness in caged birds. We found that baseline blood glucose and triglyceride levels were significantly higher in the M state than the nM state; conversely, surface body temperature was higher in the nM state than the M state. In a total of 6 liver samples that were sequenced from each state, 11,246 genes were annotated, including 4448 genes that were cyclic over 24 h. We found 569 differentially expressed genes (DEGs) between the M and the nM state, and the M state showed 131 upregulated and 438 downregulated genes. These DEGs formed core gene hubs associated with specific biological processes in both the states. In addition, weighted gene coexpression network analysis revealed two discrete modules of coexpressed genes, with a significant difference in the expression pattern of metab olism-associated genes between M and nM states. These results demonstrate, for the first time, transcriptome-wide changes in the liver between two distinct physiological states and give molecular insights into seasonal metabolic adaptations in latitudinal migrants.
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Affiliation(s)
- Devraj Singh
- IndoUS Center for Biological Timing, Department of Zoology, University of Delhi, New Delhi, India
| | - Vivek Swarup
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Hiep Le
- Salk Institute for Biological Studies, La Jolla, CA, United States
| | - Vinod Kumar
- IndoUS Center for Biological Timing, Department of Zoology, University of Delhi, New Delhi, India
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Xu P, Yang J, Liu J, Yang X, Liao J, Yuan F, Xu Y, Liu B, Chen Q. Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis. BMC Med Genomics 2018; 11:96. [PMID: 30382873 PMCID: PMC6211550 DOI: 10.1186/s12920-018-0407-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 09/25/2018] [Indexed: 02/03/2023] Open
Abstract
Background Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma. Method Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients. Results We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21 + MED10 + PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC = 0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n = 156). Conclusion We developed a promising mRNA signature for estimating overall survival in glioblastoma patients. Electronic supplementary material The online version of this article (10.1186/s12920-018-0407-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pengfei Xu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Jian Yang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Junhui Liu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Xue Yang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Jianming Liao
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Fanen Yuan
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Yang Xu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Baohui Liu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China
| | - Qianxue Chen
- Department of Neurosurgery, Renmin Hospital of Wuhan University, 9 Zhangzhidong Road and 238 Jiefang Road, Wuchang, Wuhan, Hubei, 430060, People's Republic of China.
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Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress. PLoS One 2018; 13:e0203266. [PMID: 30192796 PMCID: PMC6128483 DOI: 10.1371/journal.pone.0203266] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 08/17/2018] [Indexed: 02/08/2023] Open
Abstract
Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as—‘photosynthesis’, ‘respiratory burst’, ‘response to hormone’, ‘signal transduction’, ‘metabolic process’, ‘response to water deprivation’, which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.
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58
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Xing S, Tao C, Song Z, Liu W, Yan J, Kang L, Lin C, Sang T. Coexpression network revealing the plasticity and robustness of population transcriptome during the initial stage of domesticating energy crop Miscanthus lutarioriparius. PLANT MOLECULAR BIOLOGY 2018; 97:489-506. [PMID: 30006693 DOI: 10.1007/s11103-018-0754-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 06/26/2018] [Indexed: 06/08/2023]
Abstract
Coexpression network revealing genes with Co-variation Expression pattern (CE) and those with Top rank of Expression fold change (TE) played different roles in responding to new environment of Miscanthus lutarioriparius. Variation in gene expression level, the product of genetic and/or environmental perturbation, determines the robustness-to-plasticity spectrum of a phenotype in plants. Understanding how expression variation of plant population response to a new field is crucial to domesticate energy crops. Weighted Gene Coexpression Network Analysis (WGCNA) was used to explore the patterns of expression variation based on 72 Miscanthus lutarioriparius transcriptomes from two contrasting environments, one near the native habitat and the other in one harsh domesticating region. The 932 genes with Co-variation Expression pattern (CE) and other 932 genes with Top rank of Expression fold change (TE) were identified and the former were strongly associated with the water use efficiency (r ≥ 0.55, P ≤ 10-7). Functional enrichment of CE genes were related to three organelles, which well matched the annotation of twelve motifs identified from their conserved noncoding sequence; while TE genes were mostly related to biotic and/or abiotic stress. The expression robustness of CE genes with high genetic diversity kept relatively stable between environments while the harsh environment reduced the expression robustness of TE genes with low genetic diversity. The expression plasticity of CE genes was increased less than that of TE genes. These results suggested that expression variation of CE genes and TE genes could account for the robustness and plasticity of acclimation ability of Miscanthus, respectively. The patterns of expression variation revealed by transcriptomic network would shed new light on breeding and domestication of energy crops.
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Affiliation(s)
- Shilai Xing
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chengcheng Tao
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Song
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wei Liu
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Juan Yan
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Lifang Kang
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Cong Lin
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Tao Sang
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
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59
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Sanati N, Iancu OD, Wu G, Jacobs JE, McWeeney SK. Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma. Front Genet 2018; 9:183. [PMID: 29910823 PMCID: PMC5992410 DOI: 10.3389/fgene.2018.00183] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 05/07/2018] [Indexed: 11/23/2022] Open
Abstract
The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.
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Affiliation(s)
- Nasim Sanati
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Ovidiu D Iancu
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, United States
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - James E Jacobs
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States.,Division of Pediatric Hematology/Oncology, Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States
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Shang J, Wang F, Chen P, Wang X, Ding F, Liu S, Zhao Q. Co-expression Network Analysis Identified COL8A1 Is Associated with the Progression and Prognosis in Human Colon Adenocarcinoma. Dig Dis Sci 2018; 63:1219-1228. [PMID: 29497907 DOI: 10.1007/s10620-018-4996-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 02/22/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUNDS AND AIMS Human colon adenocarcinoma is one of the major causes of tumor-induced death worldwide. A complicated gene interconnection network significantly regulates its progression and prognosis. The aim of our study was to find hub genes associated with the progression and prognosis of colon adenocarcinoma and to illustrate the underlying mechanisms. METHODS A weighted gene co-expression network analysis was performed in our study to identify significant gene modules and hub genes associated with the TNM stage of colon adenocarcinoma (n = 441). RESULTS In the turquoise module of interest, 23 hub genes were initially selected, and 10 of them were identified as "real" hub genes with high connectivity in the protein-protein interaction network. In the terms of validation, COL8A1 had the highest correlation with clinical traits among all of the hub genes. Data obtained from the Oncomine and GEPIA databases showed a higher expression of COL8A1 in colon adenocarcinoma tissues compared with normal colon tissues. Kaplan-Meier survival curves showed that higher expression of COL8A1 resulted in a shorter overall survival time and disease-free survival time. Univariate and multivariate Cox proportional hazards analyses indicated that the COL8A1 expression was an independent prognostic factor for survival in colon adenocarcinoma patients. Finally, gene set enrichment analysis indicated that the gene sets associated with focal adhesion were significantly enriched in colon adenocarcinoma samples with COL8A1 highly expressed. CONCLUSIONS COL8A1 was identified and proved to be correlated with the progression and prognosis of human colon adenocarcinoma, probably through regulating focal adhesion-related pathways.
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Affiliation(s)
- Jian Shang
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Fan Wang
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Pengfei Chen
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Xiaobing Wang
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Feng Ding
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Shi Liu
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China.,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China
| | - Qiu Zhao
- Department of Gastroenterology/Hepatology, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, People's Republic of China. .,The Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, 430071, People's Republic of China.
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Shi S, Duan G, Li D, Wu J, Liu X, Hong B, Yi M, Zhang Z. Two-dimensional analysis provides molecular insight into flower scent of Lilium 'Siberia'. Sci Rep 2018; 8:5352. [PMID: 29599431 PMCID: PMC5876372 DOI: 10.1038/s41598-018-23588-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 03/16/2018] [Indexed: 11/10/2022] Open
Abstract
Lily is a popular flower around the world not only because of its elegant appearance, but also due to its appealing scent. Little is known about the regulation of the volatile compound biosynthesis in lily flower scent. Here, we conducted an approach combining two-dimensional analysis and weighted gene co-expression network analysis (WGCNA) to explore candidate genes regulating flower scent production. In the approach, changes of flower volatile emissions and corresponding gene expression profiles at four flower developmental stages and four circadian times were both captured by GC-MS and RNA-seq methods. By overlapping differentially-expressed genes (DEGs) that responded to flower scent changes in flower development and circadian rhythm, 3,426 DEGs were initially identified to be candidates for flower scent production, of which 1,270 were predicted as transcriptional factors (TFs). The DEGs were further correlated to individual flower volatiles by WGCNA. Finally, 37, 41 and 90 genes were identified as candidate TFs likely regulating terpenoids, phenylpropanoids and fatty acid derivatives productions, respectively. Moreover, by WGCNA several genes related to auxin, gibberellins and ABC transporter were revealed to be responsible for flower scent production. Thus, this strategy provides an important foundation for future studies on the molecular mechanisms involved in floral scent production.
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Affiliation(s)
- Shaochuan Shi
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China
| | - Guangyou Duan
- Energy Plant Research Center, School of Life Sciences, Qilu Normal University, Jinan, China
| | - Dandan Li
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China
| | - Jie Wu
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China
| | - Xintong Liu
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China
| | - Bo Hong
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China
| | - Mingfang Yi
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China.
| | - Zhao Zhang
- Beijing Key Laboratory of Development and Quality Control of Ornamental Crops, Department of Ornamental Horticulture, China Agricultural University, Beijing, China.
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62
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Ray S, Hossain SMM, Khatun L, Mukhopadhyay A. A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression. BMC Bioinformatics 2017; 18:579. [PMID: 29262769 PMCID: PMC5738049 DOI: 10.1186/s12859-017-1946-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 11/21/2017] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease. RESULTS Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules. CONCLUSIONS In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.
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Affiliation(s)
- Sumanta Ray
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India
| | - Sk Md Mosaddek Hossain
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India.
| | - Lutfunnesa Khatun
- Department of Computer Science and Engineering, Aliah University, Kolkata, 700156, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, 741235, West Bengal, India
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Farcuh M, Li B, Rivero RM, Shlizerman L, Sadka A, Blumwald E. Sugar metabolism reprogramming in a non-climacteric bud mutant of a climacteric plum fruit during development on the tree. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:5813-5828. [PMID: 29186495 PMCID: PMC5854140 DOI: 10.1093/jxb/erx391] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 10/06/2017] [Indexed: 05/08/2023]
Abstract
We investigated sugar metabolism in leaves and fruits of two Japanese plum (Prunus salicina Lindl.) cultivars, the climacteric Santa Rosa and its bud sport mutant the non-climacteric Sweet Miriam, during development on the tree. We previously characterized differences between the two cultivars. Here, we identified key sugar metabolic pathways. Pearson coefficient correlations of metabolomics and transcriptomic data and weighted gene co-expression network analysis (WGCNA) of RNA sequencing (RNA-Seq) data allowed the identification of 11 key sugar metabolism-associated genes: sucrose synthase, sucrose phosphate synthase, cytosolic invertase, vacuolar invertase, invertase inhibitor, α-galactosidase, β-galactosidase, galactokinase, trehalase, galactinol synthase, and raffinose synthase. These pathways were further assessed and validated through the biochemical characterization of the gene products and with metabolite analysis. Our results demonstrated the reprogramming of sugar metabolism in both leaves and fruits in the non-climacteric plum, which displayed a shift towards increased sorbitol synthesis. Climacteric and non-climacteric fruits showed differences in their UDP-galactose metabolism towards the production of galactose and raffinose, respectively. The higher content of galactinol, myo-inositol, raffinose, and trehalose in the non-climacteric fruits could improve the ability of the fruits to cope with the oxidative processes associated with fruit ripening. Overall, our results support a relationship between sugar metabolism, ethylene, and ripening behavior.
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Affiliation(s)
| | - Bosheng Li
- Department of Plant Sciences, University of California, USA
| | | | | | - Avi Sadka
- Department of Fruit Tree Sciences, ARO, Israel
| | - Eduardo Blumwald
- Department of Plant Sciences, University of California, USA
- Correspondence:
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Peng X, Wang J, Peng W, Wu FX, Pan Y. Protein-protein interactions: detection, reliability assessment and applications. Brief Bioinform 2017; 18:798-819. [PMID: 27444371 DOI: 10.1093/bib/bbw066] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Indexed: 01/06/2023] Open
Abstract
Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.
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Wang W, Jiang W, Hou L, Duan H, Wu Y, Xu C, Tan Q, Li S, Zhang D. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI. BMC Genomics 2017; 18:872. [PMID: 29132311 PMCID: PMC5683603 DOI: 10.1186/s12864-017-4257-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 11/01/2017] [Indexed: 02/08/2023] Open
Abstract
Background The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. Results In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed. Conclusion We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired. Electronic supplementary material The online version of this article (10.1186/s12864-017-4257-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Wenjie Jiang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Lin Hou
- Department of Biochemistry, Medical College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Haiping Duan
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.,Qingdao Municipal Center for Disease Control and Prevention, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China
| | - Yili Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China
| | - Chunsheng Xu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.,Qingdao Municipal Center for Disease Control and Prevention, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China.,Qingdao Institute of Preventive Medicine, No. 175 Shandong Road, Shibei District, Qingdao, 266033, Shandong Province, People's Republic of China
| | - Qihua Tan
- Epidemiology, Biostatistics and Bio-demography, Institute of Public Health, University of Southern Denmark, DK-5000, Odense C, Denmark.,Human Genetics, Institute of Clinical Research, University of Southern Denmark, DK-5000, Odense C, Denmark
| | - Shuxia Li
- Human Genetics, Institute of Clinical Research, University of Southern Denmark, DK-5000, Odense C, Denmark
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, No. 38 Dengzhou Road, Shibei District, Qingdao, 266021, Shandong Province, People's Republic of China.
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Mosaddek Hossain SM, Ray S, Mukhopadhyay A. Preservation affinity in consensus modules among stages of HIV-1 progression. BMC Bioinformatics 2017; 18:181. [PMID: 28320358 PMCID: PMC5359929 DOI: 10.1186/s12859-017-1590-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 03/09/2017] [Indexed: 11/16/2022] Open
Abstract
Background Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding. Methods HIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach. Results We discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages. Conclusions We have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1590-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sk Md Mosaddek Hossain
- Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700156, India
| | - Sumanta Ray
- Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700156, India.
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, 741235, India
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Azimzadeh Jamalkandi S, Mozhgani SH, Gholami Pourbadie H, Mirzaie M, Noorbakhsh F, Vaziri B, Gholami A, Ansari-Pour N, Jafari M. Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways. Front Microbiol 2016; 7:1688. [PMID: 27872612 PMCID: PMC5098112 DOI: 10.3389/fmicb.2016.01688] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 10/07/2016] [Indexed: 12/16/2022] Open
Abstract
The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein–protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.
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Affiliation(s)
| | - Sayed-Hamidreza Mozhgani
- Department of Virology, School of Public Health, Tehran University of Medical Sciences Tehran, Iran
| | | | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University Tehran, Iran
| | - Farshid Noorbakhsh
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences Tehran, Iran
| | - Behrouz Vaziri
- Protein Chemistry and Proteomics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran Tehran, Iran
| | - Alireza Gholami
- WHO Collaborating Center for Reference and Research on Rabies, Pasteur Institute of Iran Tehran, Iran
| | - Naser Ansari-Pour
- Faculty of New Sciences and Technology, University of TehranTehran, Iran; Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College LondonLondon, UK
| | - Mohieddin Jafari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran Tehran, Iran
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68
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Population-specific renal proteomes of marine and freshwater three-spined sticklebacks. J Proteomics 2016; 135:112-131. [DOI: 10.1016/j.jprot.2015.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2015] [Revised: 09/16/2015] [Accepted: 10/02/2015] [Indexed: 12/20/2022]
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69
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RNA-Seq Reveals the Angiogenesis Diversity between the Fetal and Adults Bone Mesenchyme Stem Cell. PLoS One 2016; 11:e0149171. [PMID: 26901069 PMCID: PMC4764296 DOI: 10.1371/journal.pone.0149171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 01/05/2016] [Indexed: 12/17/2022] Open
Abstract
In this research, we used RNA sequencing (RNA-seq) to analyze 23 single cell samples and 2 bulk cells sample from human adult bone mesenchyme stem cell line and human fetal bone mesenchyme stem cell line. The results from the research demonstrated that there were big differences between two cell lines. Adult bone mesenchyme stem cell lines showed a strong trend on the blood vessel differentiation and cell motion, 48/49 vascular related differential expressed genes showed higher expression in adult bone mesenchyme stem cell lines (Abmsc) than fetal bone mesenchyme stem cell lines (Fbmsc). 96/106 cell motion related genes showed the same tendency. Further analysis showed that genes like ANGPT1, VEGFA, FGF2, PDGFB and PDGFRA showed higher expression in Abmsc. This work showed cell heterogeneity between human adult bone mesenchyme stem cell line and human fetal bone mesenchyme stem cell line. Also the work may give an indication that Abmsc had a better potency than Fbmsc in the future vascular related application.
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Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, Darzi Y, Audic S, Berline L, Brum J, Coelho LP, Espinoza JCI, Malviya S, Sunagawa S, Dimier C, Kandels-Lewis S, Picheral M, Poulain J, Searson S, Stemmann L, Not F, Hingamp P, Speich S, Follows M, Karp-Boss L, Boss E, Ogata H, Pesant S, Weissenbach J, Wincker P, Acinas SG, Bork P, de Vargas C, Iudicone D, Sullivan MB, Raes J, Karsenti E, Bowler C, Gorsky G. Plankton networks driving carbon export in the oligotrophic ocean. Nature 2016; 532:465-470. [PMID: 26863193 PMCID: PMC4851848 DOI: 10.1038/nature16942] [Citation(s) in RCA: 326] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 12/18/2015] [Indexed: 01/02/2023]
Abstract
The biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterised. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria, alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of just a few bacterial and viral genes can predict most of the variability in carbon export in these regions.
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Affiliation(s)
- Lionel Guidi
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France.,Department of Oceanography, University of Hawaii, Honolulu, Hawaii, USA
| | - Samuel Chaffron
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.,Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium.,Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Lucie Bittner
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Institut de Biologie Paris-Seine (IBPS), Evolution Paris Seine, F-75005, Paris, France.,Ecole Normale Supérieure, PSL Research University, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France.,Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire Adaptation et Diversité en Milieu Marin, Station Biologique de Roscoff, Roscoff, France
| | - Damien Eveillard
- LINA UMR 6241, Université de Nantes, EMN, CNRS, 44322 Nantes, France
| | | | - Simon Roux
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Youssef Darzi
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.,Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium
| | - Stephane Audic
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire Adaptation et Diversité en Milieu Marin, Station Biologique de Roscoff, Roscoff, France
| | - Léo Berline
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France
| | - Jennifer Brum
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Luis Pedro Coelho
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | | | - Shruti Malviya
- Ecole Normale Supérieure, PSL Research University, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France
| | - Shinichi Sunagawa
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
| | - Céline Dimier
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire Adaptation et Diversité en Milieu Marin, Station Biologique de Roscoff, Roscoff, France
| | - Stefanie Kandels-Lewis
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany.,Directors' Research European Molecular Biology Laboratory Meyerhofstr. 1 69117 Heidelberg Germany
| | - Marc Picheral
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France
| | - Julie Poulain
- CEA - Institut de Génomique, GENOSCOPE, 2 rue Gaston Crémieux, 91057 Evry France
| | - Sarah Searson
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France.,Department of Oceanography, University of Hawaii, Honolulu, Hawaii, USA
| | | | - Lars Stemmann
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France
| | - Fabrice Not
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire Adaptation et Diversité en Milieu Marin, Station Biologique de Roscoff, Roscoff, France
| | - Pascal Hingamp
- Aix Marseille Université CNRS IGS UMR 7256 13288 Marseille France
| | - Sabrina Speich
- Department of Geosciences, Laboratoire de Météorologie Dynamique (LMD), Ecole Normale Supérieure, 24 rue Lhomond 75231 Paris Cedex 05 France
| | - Mick Follows
- Dept of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA
| | - Lee Karp-Boss
- School of Marine Sciences, University of Maine, Orono, USA
| | - Emmanuel Boss
- School of Marine Sciences, University of Maine, Orono, USA
| | - Hiroyuki Ogata
- Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Stephane Pesant
- PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany.,MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
| | - Jean Weissenbach
- CEA - Institut de Génomique, GENOSCOPE, 2 rue Gaston Crémieux, 91057 Evry France.,CNRS, UMR 8030, CP5706, Evry France.,Université d'Evry, UMR 8030, CP5706, Evry France
| | - Patrick Wincker
- CEA - Institut de Génomique, GENOSCOPE, 2 rue Gaston Crémieux, 91057 Evry France.,CNRS, UMR 8030, CP5706, Evry France.,Université d'Evry, UMR 8030, CP5706, Evry France
| | - Silvia G Acinas
- Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC Pg. Marítim de la Barceloneta 37-49 Barcelona E08003 Spain
| | - Peer Bork
- Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany.,Max-Delbrück-Centre for Molecular Medicine, 13092 Berlin, Germany
| | - Colomban de Vargas
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire Adaptation et Diversité en Milieu Marin, Station Biologique de Roscoff, Roscoff, France
| | - Daniele Iudicone
- Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy
| | - Matthew B Sullivan
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Jeroen Raes
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.,Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium.,Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Eric Karsenti
- Ecole Normale Supérieure, PSL Research University, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France.,Directors' Research European Molecular Biology Laboratory Meyerhofstr. 1 69117 Heidelberg Germany
| | - Chris Bowler
- Ecole Normale Supérieure, PSL Research University, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France
| | - Gabriel Gorsky
- Sorbonne Universités, UPMC Université Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Océanologique, Villefranche-sur-Mer, France
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71
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Chowdhury A, Rakshit P, Konar A. Prediction of protein-protein interaction network using a multi-objective optimization approach. J Bioinform Comput Biol 2016; 14:1650008. [PMID: 26846814 DOI: 10.1142/s0219720016500086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.
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Affiliation(s)
- Archana Chowdhury
- 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India
| | - Pratyusha Rakshit
- 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India
| | - Amit Konar
- 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India
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72
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Ansari-Pour N, Razaghi-Moghadam Z, Barneh F, Jafari M. Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure. J Proteome Res 2016; 15:1011-22. [PMID: 26794825 DOI: 10.1021/acs.jproteome.5b01080] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.
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Affiliation(s)
- Naser Ansari-Pour
- Faculty of New Sciences and Technology, University of Tehran , North Kargar Street, Tehran 143995-7131, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM) , Tehran 19395-5531, Iran
| | - Zahra Razaghi-Moghadam
- Faculty of New Sciences and Technology, University of Tehran , North Kargar Street, Tehran 143995-7131, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM) , Tehran 19395-5531, Iran
| | - Farnaz Barneh
- Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences , Tehran 198396-3113, Iran
| | - Mohieddin Jafari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran , Tehran 131694-3551, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM) , Tehran 19395-5531, Iran
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73
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Bergmann JH, Li J, Eckersley-Maslin MA, Rigo F, Freier SM, Spector DL. Regulation of the ESC transcriptome by nuclear long noncoding RNAs. Genome Res 2015; 25:1336-46. [PMID: 26048247 PMCID: PMC4561492 DOI: 10.1101/gr.189027.114] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Accepted: 06/04/2015] [Indexed: 12/18/2022]
Abstract
Long noncoding (lnc)RNAs have recently emerged as key regulators of gene expression. Here, we performed high-depth poly(A)(+) RNA sequencing across multiple clonal populations of mouse embryonic stem cells (ESCs) and neural progenitor cells (NPCs) to comprehensively identify differentially regulated lncRNAs. We establish a biologically robust profile of lncRNA expression in these two cell types and further confirm that the majority of these lncRNAs are enriched in the nucleus. Applying weighted gene coexpression network analysis, we define a group of lncRNAs that are tightly associated with the pluripotent state of ESCs. Among these, we show that acute depletion of Platr14 using antisense oligonucleotides impacts the differentiation- and development-associated gene expression program of ESCs. Furthermore, we demonstrate that Firre, a lncRNA highly enriched in the nucleoplasm and previously reported to mediate chromosomal contacts in ESCs, controls a network of genes related to RNA processing. Together, we provide a comprehensive, up-to-date, and high resolution compilation of lncRNA expression in ESCs and NPCs and show that nuclear lncRNAs are tightly integrated into the regulation of ESC gene expression.
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Affiliation(s)
- Jan H Bergmann
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Jingjing Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | | | - Frank Rigo
- Isis Pharmaceuticals, Inc., Carlsbad, California 92010, USA
| | - Susan M Freier
- Isis Pharmaceuticals, Inc., Carlsbad, California 92010, USA
| | - David L Spector
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
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74
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Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015; 161:1187-1201. [PMID: 26000487 PMCID: PMC4441768 DOI: 10.1016/j.cell.2015.04.044] [Citation(s) in RCA: 2140] [Impact Index Per Article: 237.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 02/23/2015] [Accepted: 04/20/2015] [Indexed: 12/11/2022]
Abstract
It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.
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Affiliation(s)
- Allon M Klein
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Linas Mazutis
- School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA; Vilnius University Institute of Biotechnology, Vilnius LT-02241, Lithuania
| | - Ilke Akartuna
- School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA
| | - Naren Tallapragada
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Adrian Veres
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
| | - Victor Li
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Leonid Peshkin
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - David A Weitz
- School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA.
| | - Marc W Kirschner
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
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75
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Sedaghat N, Saegusa T, Randolph T, Shojaie A. Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways. Cancer Inform 2014; 13:55-66. [PMID: 25288880 PMCID: PMC4179645 DOI: 10.4137/cin.s13781] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 05/08/2014] [Accepted: 05/10/2014] [Indexed: 12/16/2022] Open
Abstract
Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our findings also provide evidence for significant differences between networks of normal and tumor samples, even after accounting for the considerable variability in structures of networks estimated using different reconstruction methods. These differences can offer new insight into changes in mechanisms of genetic interaction associated with cancer initiation and progression.
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Affiliation(s)
- Nafiseh Sedaghat
- Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran
| | - Takumi Saegusa
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Timothy Randolph
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
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76
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Esteve-Altava B, Rasskin-Gutman D. Beyond the functional matrix hypothesis: a network null model of human skull growth for the formation of bone articulations. J Anat 2014; 225:306-16. [PMID: 24975579 PMCID: PMC4166971 DOI: 10.1111/joa.12212] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2014] [Indexed: 11/29/2022] Open
Abstract
Craniofacial sutures and synchondroses form the boundaries among bones in the human skull, providing functional, developmental and evolutionary information. Bone articulations in the skull arise due to interactions between genetic regulatory mechanisms and epigenetic factors such as functional matrices (soft tissues and cranial cavities), which mediate bone growth. These matrices are largely acknowledged for their influence on shaping the bones of the skull; however, it is not fully understood to what extent functional matrices mediate the formation of bone articulations. Aiming to identify whether or not functional matrices are key developmental factors guiding the formation of bone articulations, we have built a network null model of the skull that simulates unconstrained bone growth. This null model predicts bone articulations that arise due to a process of bone growth that is uniform in rate, direction and timing. By comparing predicted articulations with the actual bone articulations of the human skull, we have identified which boundaries specifically need the presence of functional matrices for their formation. We show that functional matrices are necessary to connect facial bones, whereas an unconstrained bone growth is sufficient to connect non-facial bones. This finding challenges the role of the brain in the formation of boundaries between bones in the braincase without neglecting its effect on skull shape. Ultimately, our null model suggests where to look for modified developmental mechanisms promoting changes in bone growth patterns that could affect the development and evolution of the head skeleton.
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Affiliation(s)
- Borja Esteve-Altava
- Theoretical Biology Research Group, Cavanilles Institute for Biodiversity and Evolutionary Biology, University of ValenciaValencia, Spain
| | - Diego Rasskin-Gutman
- Theoretical Biology Research Group, Cavanilles Institute for Biodiversity and Evolutionary Biology, University of ValenciaValencia, Spain
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77
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Chou WC, Cheng AL, Brotto M, Chuang CY. Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BMC Genomics 2014; 15:300. [PMID: 24758163 PMCID: PMC4234489 DOI: 10.1186/1471-2164-15-300] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Accepted: 04/04/2014] [Indexed: 11/10/2022] Open
Abstract
Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments. Results ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle. Conclusions The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.
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Affiliation(s)
| | | | | | - Chun-Yu Chuang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan.
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78
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Xiao Y, Fan H, Zhang Y, Xing W, Ping Y, Zhao H, Xu C, Li Y, Wang L, Li F, Hu J, Huang T, Lv Y, Ren H, Li X. Systematic identification of core transcription factors mediating dysregulated links bridging inflammatory bowel diseases and colorectal cancer. PLoS One 2013; 8:e83495. [PMID: 24386215 PMCID: PMC3873387 DOI: 10.1371/journal.pone.0083495] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2013] [Accepted: 11/04/2013] [Indexed: 12/28/2022] Open
Abstract
Accumulating evidence shows a tight link between inflammation and cancer. However, comprehensive identification of pivotal transcription factors (i.e., core TFs) mediating the dysregulated links remains challenging, mainly due to a lack of samples that can effectively reflect the connections between inflammation and tumorigenesis. Here, we constructed a series of TF-mediated regulatory networks from a large compendium of expression profiling of normal colonic tissues, inflammatory bowel diseases (IBDs) and colorectal cancer (CRC), which contains 1201 samples in total, and then proposed a network-based approach to characterize potential links bridging inflammation and cancer. For this purpose, we computed significantly dysregulated relationships between inflammation and their linked cancer networks, and then 24 core TFs with their dysregulated genes were identified. Collectively, our approach provides us with quite important insight into inflammation-associated tumorigenesis in colorectal cancer, which could also be applied to identify functionally dysregulated relationships mediating the links between other different disease phenotypes.
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Affiliation(s)
- Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Huihui Fan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenjing Xing
- Department of immunology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanyan Ping
- 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
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yiqun Li
- 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
| | - Feng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Teng Huang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanling Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Huan Ren
- Department of immunology, Harbin Medical University, Harbin, Heilongjiang, China
- * E-mail: (XL); (HR)
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
- * E-mail: (XL); (HR)
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79
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Gao C, Ju Z, Li S, Zuo J, Fu D, Tian H, Luo Y, Zhu B. Deciphering ascorbic acid regulatory pathways in ripening tomato fruit using a weighted gene correlation network analysis approach. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2013; 55:1080-1091. [PMID: 23718676 DOI: 10.1111/jipb.12079] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Accepted: 05/21/2013] [Indexed: 06/02/2023]
Abstract
Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid (AsA), which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato (Solanum lycopersicum L.) fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty-four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.
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Affiliation(s)
- Chao Gao
- Laboratory of Fruit Biology, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, 100083, China
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80
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Downs GS, Bi YM, Colasanti J, Wu W, Chen X, Zhu T, Rothstein SJ, Lukens LN. A developmental transcriptional network for maize defines coexpression modules. PLANT PHYSIOLOGY 2013; 161:1830-43. [PMID: 23388120 PMCID: PMC3613459 DOI: 10.1104/pp.112.213231] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Here, we present a genome-wide overview of transcriptional circuits in the agriculturally significant crop species maize (Zea mays). We examined transcript abundance data at 50 developmental stages, from embryogenesis to senescence, for 34,876 gene models and classified genes into 24 robust coexpression modules. Modules were strongly associated with tissue types and related biological processes. Sixteen of the 24 modules (67%) have preferential transcript abundance within specific tissues. One-third of modules had an absence of gene expression in specific tissues. Genes within a number of modules also correlated with the developmental age of tissues. Coexpression of genes is likely due to transcriptional control. For a number of modules, key genes involved in transcriptional control have expression profiles that mimic the expression profiles of module genes, although the expression of transcriptional control genes is not unusually representative of module gene expression. Known regulatory motifs are enriched in several modules. Finally, of the 13 network modules with more than 200 genes, three contain genes that are notably clustered (P < 0.05) within the genome. This work, based on a carefully selected set of major tissues representing diverse stages of maize development, demonstrates the remarkable power of transcript-level coexpression networks to identify underlying biological processes and their molecular components.
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81
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Yates PD, Mukhopadhyay ND. An inferential framework for biological network hypothesis tests. BMC Bioinformatics 2013; 14:94. [PMID: 23496778 PMCID: PMC3621801 DOI: 10.1186/1471-2105-14-94] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2012] [Accepted: 03/03/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem. RESULTS We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes. CONCLUSIONS Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios.
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Affiliation(s)
| | - Nitai D Mukhopadhyay
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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Lei C, Ruan J. A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity. ACTA ACUST UNITED AC 2012; 29:355-64. [PMID: 23235927 DOI: 10.1093/bioinformatics/bts688] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
MOTIVATION Recent advances in technology have dramatically increased the availability of protein-protein interaction (PPI) data and stimulated the development of many methods for improving the systems level understanding the cell. However, those efforts have been significantly hindered by the high level of noise, sparseness and highly skewed degree distribution of PPI networks. Here, we present a novel algorithm to reduce the noise present in PPI networks. The key idea of our algorithm is that two proteins sharing some higher-order topological similarities, measured by a novel random walk-based procedure, are likely interacting with each other and may belong to the same protein complex. RESULTS Applying our algorithm to a yeast PPI network, we found that the edges in the reconstructed network have higher biological relevance than in the original network, assessed by multiple types of information, including gene ontology, gene expression, essentiality, conservation between species and known protein complexes. Comparison with existing methods shows that the network reconstructed by our method has the highest quality. Using two independent graph clustering algorithms, we found that the reconstructed network has resulted in significantly improved prediction accuracy of protein complexes. Furthermore, our method is applicable to PPI networks obtained with different experimental systems, such as affinity purification, yeast two-hybrid (Y2H) and protein-fragment complementation assay (PCA), and evidence shows that the predicted edges are likely bona fide physical interactions. Finally, an application to a human PPI network increased the coverage of the network by at least 100%. AVAILABILITY www.cs.utsa.edu/∼jruan/RWS/.
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Affiliation(s)
- Chengwei Lei
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
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83
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Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 2012; 13:328. [PMID: 23217028 PMCID: PMC3586947 DOI: 10.1186/1471-2105-13-328] [Citation(s) in RCA: 256] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Accepted: 11/30/2012] [Indexed: 11/27/2022] Open
Abstract
Background Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). Results We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. Conclusion The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.
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84
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Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MPM, van Eijk K, van den Berg LH, Ophoff RA. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 2012; 13:R97. [PMID: 23034122 PMCID: PMC4053733 DOI: 10.1186/gb-2012-13-10-r97] [Citation(s) in RCA: 445] [Impact Index Per Article: 37.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Accepted: 10/03/2012] [Indexed: 11/30/2022] Open
Abstract
Background Several recent studies reported aging effects on DNA methylation levels of individual CpG dinucleotides. But it is not yet known whether aging-related consensus modules, in the form of clusters of correlated CpG markers, can be found that are present in multiple human tissues. Such a module could facilitate the understanding of aging effects on multiple tissues. Results We therefore employed weighted correlation network analysis of 2,442 Illumina DNA methylation arrays from brain and blood tissues, which enabled the identification of an age-related co-methylation module. Module preservation analysis confirmed that this module can also be found in diverse independent data sets. Biological evaluation showed that module membership is associated with Polycomb group target occupancy counts, CpG island status and autosomal chromosome location. Functional enrichment analysis revealed that the aging-related consensus module comprises genes that are involved in nervous system development, neuron differentiation and neurogenesis, and that it contains promoter CpGs of genes known to be down-regulated in early Alzheimer's disease. A comparison with a standard, non-module based meta-analysis revealed that selecting CpGs based on module membership leads to significantly increased gene ontology enrichment, thus demonstrating that studying aging effects via consensus network analysis enhances the biological insights gained. Conclusions Overall, our analysis revealed a robustly defined age-related co-methylation module that is present in multiple human tissues, including blood and brain. We conclude that blood is a promising surrogate for brain tissue when studying the effects of age on DNA methylation profiles.
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85
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Nassiri I, Masoudi-Nejad A, Jalili M, Moeini A. Nonparametric simulation of signal transduction networks with semi-synchronized update. PLoS One 2012; 7:e39643. [PMID: 22737250 PMCID: PMC3380921 DOI: 10.1371/journal.pone.0039643] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Accepted: 05/23/2012] [Indexed: 01/20/2023] Open
Abstract
Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.
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Affiliation(s)
- Isar Nassiri
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- * E-mail:
| | - Mahdi Jalili
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Moeini
- Department of Algorithms and Computation, College of Engineering, University of Tehran, Tehran, Iran
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86
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Farkas IJ, Szántó-Várnagy A, Korcsmáros T. Linking proteins to signaling pathways for experiment design and evaluation. PLoS One 2012; 7:e36202. [PMID: 22558382 PMCID: PMC3338605 DOI: 10.1371/journal.pone.0036202] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2011] [Accepted: 04/03/2012] [Indexed: 11/20/2022] Open
Abstract
Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.
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Affiliation(s)
- Illés J Farkas
- Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary.
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88
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Krzywinski M, Birol I, Jones SJM, Marra MA. Hive plots--rational approach to visualizing networks. Brief Bioinform 2011; 13:627-44. [PMID: 22155641 DOI: 10.1093/bib/bbr069] [Citation(s) in RCA: 164] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Networks are typically visualized with force-based or spectral layouts. These algorithms lack reproducibility and perceptual uniformity because they do not use a node coordinate system. The layouts can be difficult to interpret and are unsuitable for assessing differences in networks. To address these issues, we introduce hive plots (http://www.hiveplot.com) for generating informative, quantitative and comparable network layouts. Hive plots depict network structure transparently, are simple to understand and can be easily tuned to identify patterns of interest. The method is computationally straightforward, scales well and is amenable to a plugin for existing tools.
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89
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Lu Y, Liu P, Van den Bergh F, Zellmer V, James M, Wen W, Grubbs CJ, Lubet RA, You M. Modulation of Gene Expression and Cell-Cycle Signaling Pathways by the EGFR Inhibitor Gefitinib (Iressa) in Rat Urinary Bladder Cancer. Cancer Prev Res (Phila) 2011; 5:248-59. [DOI: 10.1158/1940-6207.capr-10-0363] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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90
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Chen L, Li W, Zhang L, Wang H, He W, Tai J, Li X, Li X. Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules. PLoS One 2011; 6:e24495. [PMID: 21915342 PMCID: PMC3167857 DOI: 10.1371/journal.pone.0024495] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2010] [Accepted: 08/11/2011] [Indexed: 01/01/2023] Open
Abstract
Background Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as ‘a form of biological systems’, consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic. Methodology/Principal Findings In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways. Conclusions/Significance Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.
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Affiliation(s)
- Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
- * E-mail: (LC); (LZ); (XL)
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
| | - Liangcai Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
- * E-mail: (LC); (LZ); (XL)
| | - Hong Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
| | - Jingxie Tai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
| | - Xu Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
- * E-mail: (LC); (LZ); (XL)
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91
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Wang A, Huang K, Shen Y, Xue Z, Cai C, Horvath S, Fan G. Functional modules distinguish human induced pluripotent stem cells from embryonic stem cells. Stem Cells Dev 2011; 20:1937-50. [PMID: 21542696 DOI: 10.1089/scd.2010.0574] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
It has been debated whether human induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs) express distinctive transcriptomes. By using the method of weighted gene co-expression network analysis, we showed here that iPSCs exhibit altered functional modules compared with ESCs. Notably, iPSCs and ESCs differentially express 17 modules that primarily function in transcription, metabolism, development, and immune response. These module activations (up- and downregulation) are highly conserved in a variety of iPSCs, and genes in each module are coherently co-expressed. Furthermore, the activation levels of these modular genes can be used as quantitative variables to discriminate iPSCs and ESCs with high accuracy (96%). Thus, differential activations of these functional modules are the conserved features distinguishing iPSCs from ESCs. Strikingly, the overall activation level of these modules is inversely correlated with the DNA methylation level, suggesting that DNA methylation may be one mechanism regulating the module differences. Overall, we conclude that human iPSCs and ESCs exhibit distinct gene expression networks, which are likely associated with different epigenetic reprogramming events during the derivation of iPSCs and ESCs.
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Affiliation(s)
- Anyou Wang
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095, USA
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92
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Winden KD, Karsten SL, Bragin A, Kudo LC, Gehman L, Ruidera J, Geschwind DH, Engel J. A systems level, functional genomics analysis of chronic epilepsy. PLoS One 2011; 6:e20763. [PMID: 21695113 PMCID: PMC3114768 DOI: 10.1371/journal.pone.0020763] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2011] [Accepted: 05/09/2011] [Indexed: 12/28/2022] Open
Abstract
Neither the molecular basis of the pathologic tendency of neuronal circuits to generate spontaneous seizures (epileptogenicity) nor anti-epileptogenic mechanisms that maintain a seizure-free state are well understood. Here, we performed transcriptomic analysis in the intrahippocampal kainate model of temporal lobe epilepsy in rats using both Agilent and Codelink microarray platforms to characterize the epileptic processes. The experimental design allowed subtraction of the confounding effects of the lesion, identification of expression changes associated with epileptogenicity, and genes upregulated by seizures with potential homeostatic anti-epileptogenic effects. Using differential expression analysis, we identified several hundred expression changes in chronic epilepsy, including candidate genes associated with epileptogenicity such as Bdnf and Kcnj13. To analyze these data from a systems perspective, we applied weighted gene co-expression network analysis (WGCNA) to identify groups of co-expressed genes (modules) and their central (hub) genes. One such module contained genes upregulated in the epileptogenic region, including multiple epileptogenicity candidate genes, and was found to be involved the protection of glial cells against oxidative stress, implicating glial oxidative stress in epileptogenicity. Another distinct module corresponded to the effects of chronic seizures and represented changes in neuronal synaptic vesicle trafficking. We found that the network structure and connectivity of one hub gene, Sv2a, showed significant changes between normal and epileptogenic tissue, becoming more highly connected in epileptic brain. Since Sv2a is a target of the antiepileptic levetiracetam, this module may be important in controlling seizure activity. Bioinformatic analysis of this module also revealed a potential mechanism for the observed transcriptional changes via generation of longer alternatively polyadenlyated transcripts through the upregulation of the RNA binding protein HuD. In summary, combining conventional statistical methods and network analysis allowed us to interpret the differentially regulated genes from a systems perspective, yielding new insight into several biological pathways underlying homeostatic anti-epileptogenic effects and epileptogenicity.
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Affiliation(s)
- Kellen D. Winden
- Interdepartmental Program for Neuroscience, University of California Los Angeles, Los Angeles, California, United States of America
- Program in Neurogenetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Stanislav L. Karsten
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
- Division of Neuroscience Research, Department of Neurology, Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Anatol Bragin
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
- The Brain Research Institute, University of California Los Angeles, Los Angeles, California, United States of America
| | - Lili C. Kudo
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
- NeuroIndx Inc., Signal Hill, California, United States of America
| | - Lauren Gehman
- Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Josephine Ruidera
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Daniel H. Geschwind
- Interdepartmental Program for Neuroscience, University of California Los Angeles, Los Angeles, California, United States of America
- Program in Neurogenetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (DHG); (JE)
| | - Jerome Engel
- Department of Neurology, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Neurobiology, University of California Los Angeles, Los Angeles, California, United States of America
- The Brain Research Institute, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (DHG); (JE)
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Presson AP, Yoon NK, Bagryanova L, Mah V, Alavi M, Maresh EL, Rajasekaran AK, Goodglick L, Chia D, Horvath S. Protein expression based multimarker analysis of breast cancer samples. BMC Cancer 2011; 11:230. [PMID: 21651811 PMCID: PMC3142534 DOI: 10.1186/1471-2407-11-230] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2010] [Accepted: 06/08/2011] [Indexed: 12/11/2022] Open
Abstract
Background Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups. Methods We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis. Results We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach. Conclusions We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.
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Konopka G. Functional genomics of the brain: uncovering networks in the CNS using a systems approach. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2010; 3:628-48. [PMID: 21197665 DOI: 10.1002/wsbm.139] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The central nervous system (CNS) is undoubtedly the most complex human organ system in terms of its diverse functions, cellular composition, and connections. Attempts to capture this diversity experimentally were the foundation on which the field of neurobiology was built. Until now though, techniques were either painstakingly slow or insufficient in capturing this heterogeneity. In addition, the combination of multiple layers of information needed for a complete picture of neuronal diversity from the epigenome to the proteome requires an even more complex compilation of data. In this era of high-throughput genomics though, the ability to isolate and profile neurons and brain tissue has increased tremendously and now requires less effort. Both microarrays and next-generation sequencing have identified neuronal transcriptomes and signaling networks involved in normal brain development, as well as in disease. However, the expertise needed to organize and prioritize the resultant data remains substantial. A combination of supervised organization and unsupervised analyses are needed to fully appreciate the underlying structure in these datasets. When utilized effectively, these analyses have yielded striking insights into a number of fundamental questions in neuroscience on topics ranging from the evolution of the human brain to neuropsychiatric and neurodegenerative disorders. Future studies will incorporate these analyses with behavioral and physiological data from patients to more efficiently move toward personalized therapeutics.
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Affiliation(s)
- Genevieve Konopka
- Department of Neurology, University of California, Los Angeles, CA, USA.
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95
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Cai C, Langfelder P, Fuller TF, Oldham MC, Luo R, van den Berg LH, Ophoff RA, Horvath S. Is human blood a good surrogate for brain tissue in transcriptional studies? BMC Genomics 2010; 11:589. [PMID: 20961428 PMCID: PMC3091510 DOI: 10.1186/1471-2164-11-589] [Citation(s) in RCA: 86] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2010] [Accepted: 10/20/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). RESULTS We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10⁻⁹⁰); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. CONCLUSIONS Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable.
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Affiliation(s)
- Chaochao Cai
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
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Zhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S. Weighted gene coexpression network analysis: state of the art. J Biopharm Stat 2010; 20:281-300. [PMID: 20309759 DOI: 10.1080/10543400903572753] [Citation(s) in RCA: 190] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Weighted gene coexpression network analysis (WGCNA) has been applied to many important studies since its introduction in 2005. WGCNA can be used as a data exploratory tool or as a gene screening method; WGCNA can also be used as a tool to generate testable hypothesis for validation in independent data sets. In this article, we review key concepts of WGCNA and some of its applications in gene expression analysis of oncology, brain function, and protein interaction data.
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Affiliation(s)
- Wei Zhao
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
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de Jong S, Fuller TF, Janson E, Strengman E, Horvath S, Kas MJH, Ophoff RA. Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background. BMC Genomics 2010; 11:20. [PMID: 20064228 PMCID: PMC2823687 DOI: 10.1186/1471-2164-11-20] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2009] [Accepted: 01/11/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits. We performed a whole-genome gene expression experiment and applied a systems biology approach using weighted gene co-expression network analysis. RESULTS We were able to identify modules of co-expressed genes that distinguish a strain or brain region. Analysis of the networks that are most informative for hippocampus and amygdala revealed enrichment in neurologically, genetically and psychologically related pathways. Close examination of the strain-specific gene expression profiles, however, revealed no functional relevance but a significant enrichment of single nucleotide polymorphisms in the probe sequences used for array hybridization. This artifact was not observed for the modules of co-expressed genes that distinguish amygdala and hippocampus. CONCLUSIONS The brain-region specific modules were found to be independent of genetic background and are therefore likely to represent biologically relevant molecular networks that can be studied to complement our knowledge about pathways in neuropsychiatric disease.
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Affiliation(s)
- Simone de Jong
- Department of Medical Genetics and Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
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Voevodski K, Teng SH, Xia Y. Spectral affinity in protein networks. BMC SYSTEMS BIOLOGY 2009; 3:112. [PMID: 19943959 PMCID: PMC2797010 DOI: 10.1186/1752-0509-3-112] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Accepted: 11/29/2009] [Indexed: 01/15/2023]
Abstract
Background Protein-protein interaction (PPI) networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. Results We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. Conclusion We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to quickly find nodes closest to a queried vertex in any protein network available from BioGRID or specified by the user.
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Aid-Pavlidis T, Pavlidis P, Timmusk T. Meta-coexpression conservation analysis of microarray data: a "subset" approach provides insight into brain-derived neurotrophic factor regulation. BMC Genomics 2009; 10:420. [PMID: 19737418 PMCID: PMC2748098 DOI: 10.1186/1471-2164-10-420] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2008] [Accepted: 09/08/2009] [Indexed: 11/26/2022] Open
Abstract
Background Alterations in brain-derived neurotrophic factor (BDNF) gene expression contribute to serious pathologies such as depression, epilepsy, cancer, Alzheimer's, Huntington and Parkinson's disease. Therefore, exploring the mechanisms of BDNF regulation represents a great clinical importance. Studying BDNF expression remains difficult due to its multiple neural activity-dependent and tissue-specific promoters. Thus, microarray data could provide insight into the regulation of this complex gene. Conventional microarray co-expression analysis is usually carried out by merging the datasets or by confirming the re-occurrence of significant correlations across datasets. However, co-expression patterns can be different under various conditions that are represented by subsets in a dataset. Therefore, assessing co-expression by measuring correlation coefficient across merged samples of a dataset or by merging datasets might not capture all correlation patterns. Results In our study, we performed meta-coexpression analysis of publicly available microarray data using BDNF as a "guide-gene" introducing a "subset" approach. The key steps of the analysis included: dividing datasets into subsets with biologically meaningful sample content (e.g. tissue, gender or disease state subsets); analyzing co-expression with the BDNF gene in each subset separately; and confirming co- expression links across subsets. Finally, we analyzed conservation in co-expression with BDNF between human, mouse and rat, and sought for conserved over-represented TFBSs in BDNF and BDNF-correlated genes. Correlated genes discovered in this study regulate nervous system development, and are associated with various types of cancer and neurological disorders. Also, several transcription factor identified here have been reported to regulate BDNF expression in vitro and in vivo. Conclusion The study demonstrates the potential of the "subset" approach in co-expression conservation analysis for studying the regulation of single genes and proposes novel regulators of BDNF gene expression.
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Affiliation(s)
- Tamara Aid-Pavlidis
- Department of Gene Technology, Tallinn University of Technology, Akadeemia tee 15, 19086 Tallinn, Estonia.
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Saris CGJ, Horvath S, van Vught PWJ, van Es MA, Blauw HM, Fuller TF, Langfelder P, DeYoung J, Wokke JHJ, Veldink JH, van den Berg LH, Ophoff RA. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. BMC Genomics 2009; 10:405. [PMID: 19712483 PMCID: PMC2743717 DOI: 10.1186/1471-2164-10-405] [Citation(s) in RCA: 132] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2009] [Accepted: 08/27/2009] [Indexed: 12/11/2022] Open
Abstract
Background Amyotrophic Lateral Sclerosis (ALS) is a lethal disorder characterized by progressive degeneration of motor neurons in the brain and spinal cord. Diagnosis is mainly based on clinical symptoms, and there is currently no therapy to stop the disease or slow its progression. Since access to spinal cord tissue is not possible at disease onset, we investigated changes in gene expression profiles in whole blood of ALS patients. Results Our transcriptional study showed dramatic changes in blood of ALS patients; 2,300 probes (9.4%) showed significant differential expression in a discovery dataset consisting of 30 ALS patients and 30 healthy controls. Weighted gene co-expression network analysis (WGCNA) was used to find disease-related networks (modules) and disease related hub genes. Two large co-expression modules were found to be associated with ALS. Our findings were replicated in a second (30 patients and 30 controls) and third dataset (63 patients and 63 controls), thereby demonstrating a highly significant and consistent association of two large co-expression modules with ALS disease status. Ingenuity Pathway Analysis of the ALS related module genes implicates enrichment of functional categories related to genetic disorders, neurodegeneration of the nervous system and inflammatory disease. The ALS related modules contain a number of candidate genes possibly involved in pathogenesis of ALS. Conclusion This first large-scale blood gene expression study in ALS observed distinct patterns between cases and controls which may provide opportunities for biomarker development as well as new insights into the molecular mechanisms of the disease.
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Affiliation(s)
- Christiaan G J Saris
- Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht 3584 CX, the Netherlands.
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