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Li HD, Xu Y, Zhu X, Liu Q, Omenn GS, Wang J. ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets. J Bioinform Comput Biol 2021; 18:2040009. [PMID: 32698720 DOI: 10.1142/s0219720020400090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php.
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Affiliation(s)
- Hong-Dong Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Yunpei Xu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Xiaoshu Zhu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China.,School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, P. R. China
| | - Quan Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Gilbert S Omenn
- Departments of Computational Medicine and Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
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Abstract
Ubiquitin (Ub) is one of the proteins that are highly conserved from yeast to humans. It is an essential core unit of the well-defined post-translational modification, called ubiquitination, which is involved in a variety of biological processes. In meta-zoans, Ub is encoded by two monoubiquitin genes and two polyubiquitin genes, in which a single Ub is fused to a ribosomal protein or Ub coding units are arranged in tandem repeats. In mice, polyubiquitin genes (Ubb and Ubc) play a pivotal role to meet the requirement of cellular Ub pools during embryonic development. In addition, expression levels of polyubiquitin genes are increased to adapt to environmental stimuli such as oxidative, heat-shock, and proteotoxic stress. Several researchers have reported about the perturbation of Ub pools through genetic alteration or exogenous Ub delivery using diverse model systems. To study Ub pool changes in a physiologically relevant manner, changing Ub pools via the regulation of endogenous polyubiquitin gene expression has recently been introduced. Furthermore, to understand the regulation of polyubiquitin gene expression more precisely, cis-acting elements and trans-acting factors, which are regulatory components of polyubiquitin genes, have been analyzed. In this review, we discuss how the role of polyu-biquitin genes has been studied during the past decade, es-pecially focusing on their regulation.
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Affiliation(s)
- Seung-Woo Han
- Department of Life Science, University of Seoul, Seoul 02504, Korea
| | - Byung-Kwon Jung
- Department of Life Science, University of Seoul, Seoul 02504, Korea
| | - Kwon-Yul Ryu
- Department of Life Science, University of Seoul, Seoul 02504, Korea
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Rps27a might act as a controller of microglia activation in triggering neurodegenerative diseases. PLoS One 2020; 15:e0239219. [PMID: 32941527 PMCID: PMC7498011 DOI: 10.1371/journal.pone.0239219] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 09/01/2020] [Indexed: 01/10/2023] Open
Abstract
Neurodegenerative diseases (NDDs) are increasing serious menaces to human health in the recent years. Despite exhibiting different clinical phenotypes and selective neuronal loss, there are certain common features in these disorders, suggesting the presence of commonly dysregulated pathways. Identifying causal genes and dysregulated pathways can be helpful in providing effective treatment in these diseases. Interestingly, in spite of the considerable researches on NDDs, to the best of our knowledge, no dysregulated genes and/or pathways were reported in common across all the major NDDs so far. In this study, for the first time, we have applied the three-way interaction model, as an approach to unravel sophisticated gene interactions, to trace switch genes and significant pathways that are involved in six major NDDs. Subsequently, a gene regulatory network was constructed to investigate the regulatory communication of statistically significant triplets. Finally, KEGG pathway enrichment analysis was applied to find possible common pathways. Because of the central role of neuroinflammation and immune system responses in both pathogenic and protective mechanisms in the NDDs, we focused on immune genes in this study. Our results suggest that "cytokine-cytokine receptor interaction" pathway is enriched in all of the studied NDDs, while "osteoclast differentiation" and "natural killer cell mediated cytotoxicity" pathways are enriched in five of the NDDs each. The results of this study indicate that three pathways that include "osteoclast differentiation", "natural killer cell mediated cytotoxicity" and "cytokine-cytokine receptor interaction" are common in five, five and six NDDs, respectively. Additionally, our analysis showed that Rps27a as a switch gene, together with the gene pair {Il-18, Cx3cl1} form a statistically significant and biologically relevant triplet in the major NDDs. More specifically, we suggested that Cx3cl1 might act as a potential upstream regulator of Il-18 in microglia activation, and in turn, might be controlled with Rps27a in triggering NDDs.
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Barry C, Schmitz MT, Argus C, Bolin JM, Probasco MD, Leng N, Duffin BM, Steill J, Swanson S, McIntosh BE, Stewart R, Kendziorski C, Thomson JA, Bacher R. Automated minute scale RNA-seq of pluripotent stem cell differentiation reveals early divergence of human and mouse gene expression kinetics. PLoS Comput Biol 2019; 15:e1007543. [PMID: 31815944 PMCID: PMC6922475 DOI: 10.1371/journal.pcbi.1007543] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 12/19/2019] [Accepted: 11/12/2019] [Indexed: 12/22/2022] Open
Abstract
Pluripotent stem cells retain the developmental timing of their species of origin in vitro, an observation that suggests the existence of a cell-intrinsic developmental clock, yet the nature and machinery of the clock remain a mystery. We hypothesize that one possible component may lie in species-specific differences in the kinetics of transcriptional responses to differentiation signals. Using a liquid-handling robot, mouse and human pluripotent stem cells were exposed to identical neural differentiation conditions and sampled for RNA-sequencing at high frequency, every 4 or 10 minutes, for the first 10 hours of differentiation to test for differences in transcriptomic response rates. The majority of initial transcriptional responses occurred within a rapid window in the first minutes of differentiation for both human and mouse stem cells. Despite similarly early onsets of gene expression changes, we observed shortened and condensed gene expression patterns in mouse pluripotent stem cells compared to protracted trends in human pluripotent stem cells. Moreover, the speed at which individual genes were upregulated, as measured by the slopes of gene expression changes over time, was significantly faster in mouse compared to human cells. These results suggest that downstream transcriptomic response kinetics to signaling cues are faster in mouse versus human cells, and may offer a partial account for the vast differences in developmental rates across species.
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Affiliation(s)
- Christopher Barry
- Morgridge Institute for Research, Madison, WI, United States of America
| | | | - Cara Argus
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Jennifer M. Bolin
- Morgridge Institute for Research, Madison, WI, United States of America
| | | | - Ning Leng
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Bret M. Duffin
- Morgridge Institute for Research, Madison, WI, United States of America
| | - John Steill
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Scott Swanson
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Brian E. McIntosh
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Ron Stewart
- Morgridge Institute for Research, Madison, WI, United States of America
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States of America
| | - James A. Thomson
- Morgridge Institute for Research, Madison, WI, United States of America
- Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, United States of America
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, FL, United States of America
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