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Liu Z, Li W, Lv J, Xie R, Huang H, Li Y, He Y, Jiang J, Chen B, Guo S, Chen L. Identification of potential COPD genes based on multi-omics data at the functional level. MOLECULAR BIOSYSTEMS 2016; 12:191-204. [PMID: 26575263 DOI: 10.1039/c5mb00577a] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2025]
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex disease, which involves dysfunctions in multi-omics. The changes in biological processes, such as adhesion junction, signaling transduction, transcriptional regulation, and cell proliferation, will lead to the occurrence of COPD. A novel systematic approach MMMG (Methylation-MicroRNA-MRNA-GO) was proposed to identify potential COPD genes by integrating function information with a methylation profile, a microRNA expression profile and an mRNA expression profile. 8 co-functional classes and 102 potential COPD genes were identified. These genes displayed a high performance in classifying COPD patients and normal samples, revealed COPD-related pathways, and have been confirmed to be associated with COPD by Matthews correlation coefficient (MCC)-values, literature, an independent data set, and pathways. The MMMG method that analyzed multi-omics data at the functional level could effectively identify potential COPD genes. These potential COPD genes would provide in-depth insights into understanding the complexity of COPD genome landscapes, improve the early diagnostics, and guide new efforts to develop therapeutics in the future.
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Affiliation(s)
- Zhe Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
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Shi H, Schwender J. Mathematical models of plant metabolism. Curr Opin Biotechnol 2015; 37:143-152. [PMID: 26723012 DOI: 10.1016/j.copbio.2015.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 10/16/2015] [Accepted: 10/26/2015] [Indexed: 11/24/2022]
Abstract
Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.
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Affiliation(s)
- Hai Shi
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Jörg Schwender
- Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, United States.
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53
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Kim M, Yi JS, Lakshmanan M, Lee DY, Kim BG. Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor. Biotechnol Bioeng 2015; 113:651-60. [PMID: 26369755 DOI: 10.1002/bit.25830] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 08/26/2015] [Accepted: 09/07/2015] [Indexed: 12/23/2022]
Abstract
In silico model-driven analysis using genome-scale model of metabolism (GEM) has been recognized as a promising method for microbial strain improvement. However, most of the current GEM-based strain design algorithms based on flux balance analysis (FBA) heavily rely on the steady-state and optimality assumptions without considering any regulatory information. Thus, their practical usage is quite limited, especially in its application to secondary metabolites overproduction. In this study, we developed a transcriptomics-based strain optimization tool (tSOT) in order to overcome such limitations by integrating transcriptomic data into GEM. Initially, we evaluated existing algorithms for integrating transcriptomic data into GEM using Streptomyces coelicolor dataset, and identified iMAT algorithm as the only and the best algorithm for characterizing the secondary metabolism of S. coelicolor. Subsequently, we developed tSOT platform where iMAT is adopted to predict the reaction states, and successfully demonstrated its applicability to secondary metabolites overproduction by designing actinorhodin (ACT), a polyketide antibiotic, overproducing strain of S. coelicolor. Mutants overexpressing tSOT targets such as ribulose 5-phosphate 3-epimerase and NADP-dependent malic enzyme showed 2 and 1.8-fold increase in ACT production, thereby validating the tSOT prediction. It is expected that tSOT can be used for solving other metabolic engineering problems which could not be addressed by current strain design algorithms, especially for the secondary metabolite overproductions.
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Affiliation(s)
- Minsuk Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea
| | - Jeong Sang Yi
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, A*STAR (Agency for Science, Technology and Research), Centros, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, A*STAR (Agency for Science, Technology and Research), Centros, Singapore. .,Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore. .,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore.
| | - Byung-Gee Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea. .,Bioengineering Institute, Seoul National University, Seoul, Republic of Korea. .,Interdisciplinary Program for Biochemical Engineering and Biotechnology, Seoul National University, Seoul, Republic of Korea.
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54
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Spies D, Ciaudo C. Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput Struct Biotechnol J 2015; 13:469-77. [PMID: 26430493 PMCID: PMC4564389 DOI: 10.1016/j.csbj.2015.08.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/05/2015] [Accepted: 08/07/2015] [Indexed: 12/17/2022] Open
Abstract
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
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From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell Mol Life Sci 2015; 72:4287-308. [PMID: 26254872 PMCID: PMC4611022 DOI: 10.1007/s00018-015-2004-1] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2015] [Revised: 07/23/2015] [Accepted: 07/28/2015] [Indexed: 12/30/2022]
Abstract
Microorganisms and the viruses that infect them are the most numerous biological entities on Earth and enclose its greatest biodiversity and genetic reservoir. With strength in their numbers, these microscopic organisms are major players in the cycles of energy and matter that sustain all life. Scientists have only scratched the surface of this vast microbial world through culture-dependent methods. Recent developments in generating metagenomes, large random samples of nucleic acid sequences isolated directly from the environment, are providing comprehensive portraits of the composition, structure, and functioning of microbial communities. Moreover, advances in metagenomic analysis have created the possibility of obtaining complete or nearly complete genome sequences from uncultured microorganisms, providing important means to study their biology, ecology, and evolution. Here we review some of the recent developments in the field of metagenomics, focusing on the discovery of genetic novelty and on methods for obtaining uncultured genome sequences, including through the recycling of previously published datasets. Moreover we discuss how metagenomics has become a core scientific tool to characterize eco-evolutionary patterns of microbial ecosystems, thus allowing us to simultaneously discover new microbes and study their natural communities. We conclude by discussing general guidelines and challenges for modeling the interactions between uncultured microorganisms and viruses based on the information contained in their genome sequences. These models will significantly advance our understanding of the functioning of microbial ecosystems and the roles of microbes in the environment.
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Takeda H, Takai A, Marusawa H. Comprehensive characterization of hepatitis B virus-associated multifocal hepatocellular carcinoma using a multi-omics strategy. ANNALS OF TRANSLATIONAL MEDICINE 2015; 3:3. [PMID: 25705635 PMCID: PMC4293482 DOI: 10.3978/j.issn.2305-5839.2014.12.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 11/26/2014] [Indexed: 11/14/2022]
Affiliation(s)
- Haruhiko Takeda
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Atsushi Takai
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroyuki Marusawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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