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Ciamponi FE, Procópio DP, Murad NF, Franco TT, Basso TO, Brandão MM. Multi-omics network model reveals key genes associated with p-coumaric acid stress response in an industrial yeast strain. Sci Rep 2022; 12:22466. [PMID: 36577778 PMCID: PMC9797568 DOI: 10.1038/s41598-022-26843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/21/2022] [Indexed: 12/30/2022] Open
Abstract
The production of ethanol from lignocellulosic sources presents increasingly difficult issues for the global biofuel scenario, leading to increased production costs of current second-generation (2G) ethanol when compared to first-generation (1G) plants. Among the setbacks encountered in industrial processes, the presence of chemical inhibitors from pre-treatment processes severely hinders the potential of yeasts in producing ethanol at peak efficiency. However, some industrial yeast strains have, either naturally or artificially, higher tolerance levels to these compounds. Such is the case of S. cerevisiae SA-1, a Brazilian fuel ethanol industrial strain that has shown high resistance to inhibitors produced by the pre-treatment of cellulosic complexes. Our study focuses on the characterization of the transcriptomic and physiological impact of an inhibitor of this type, p-coumaric acid (pCA), on this strain under chemostat cultivation via RNAseq and quantitative physiological data. It was found that strain SA-1 tend to increase ethanol yield and production rate while decreasing biomass yield when exposed to pCA, in contrast to pCA-susceptible strains, which tend to decrease their ethanol yield and fermentation efficiency when exposed to this substance. This suggests increased metabolic activity linked to mitochondrial and peroxisomal processes. The transcriptomic analysis also revealed a plethora of differentially expressed genes located in co-expressed clusters that are associated with changes in biological pathways linked to biosynthetic and energetical processes. Furthermore, it was also identified 20 genes that act as interaction hubs for these clusters, while also having association with altered pathways and changes in metabolic outputs, potentially leading to the discovery of novel targets for metabolic engineering toward a more robust industrial yeast strain.
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Affiliation(s)
- F. E. Ciamponi
- grid.411087.b0000 0001 0723 2494Center for Molecular Biology and Genetic Engineering (CBMEG), State University of Campinas (Unicamp), Av. Cândido Rondon, 400, Campinas, SP 13083-875 Brazil
| | - D. P. Procópio
- grid.11899.380000 0004 1937 0722Department of Chemical Engineering, University of São Paulo (USP), Av. Prof. Luciano Gualberto, 380, São Paulo, SP 05508-010 Brazil
| | - N. F. Murad
- grid.411087.b0000 0001 0723 2494Center for Molecular Biology and Genetic Engineering (CBMEG), State University of Campinas (Unicamp), Av. Cândido Rondon, 400, Campinas, SP 13083-875 Brazil
| | - T. T. Franco
- grid.411087.b0000 0001 0723 2494School of Chemical Engineering (FEQ), State University of Campinas (Unicamp), Av. Albert Einstein, 500, Campinas, SP 13083-852 Brazil
| | - T. O. Basso
- grid.11899.380000 0004 1937 0722Department of Chemical Engineering, University of São Paulo (USP), Av. Prof. Luciano Gualberto, 380, São Paulo, SP 05508-010 Brazil
| | - M. M. Brandão
- grid.411087.b0000 0001 0723 2494Center for Molecular Biology and Genetic Engineering (CBMEG), State University of Campinas (Unicamp), Av. Cândido Rondon, 400, Campinas, SP 13083-875 Brazil
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2
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Zhang X, Li J, Pan BZ, Chen W, Chen M, Tang M, Xu ZF, Liu C. Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data. BMC Bioinformatics 2021; 22:409. [PMID: 34407772 PMCID: PMC8375076 DOI: 10.1186/s12859-021-04319-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand. RESULTS In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein-protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles. CONCLUSIONS Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application.
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Affiliation(s)
- Xuan Zhang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Li
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Bang-Zhen Pan
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Wen Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Maosheng Chen
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Mingyong Tang
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China.,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Zeng-Fu Xu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
| | - Changning Liu
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, 666303, Yunnan, China. .,The Innovative Academy of Seed Design, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
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3
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Chen D, Wang Y, Manakkat Vijay GK, Fu S, Nash CW, Xu D, He D, Salomonis N, Singh H, Xu H. Coupled analysis of transcriptome and BCR mutations reveals role of OXPHOS in affinity maturation. Nat Immunol 2021; 22:904-913. [PMID: 34031613 DOI: 10.1038/s41590-021-00936-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 04/19/2021] [Indexed: 02/03/2023]
Abstract
Antigen-activated B cells diversify variable regions of B cell antigen receptors by somatic hypermutation in germinal centers (GCs). The positive selection of GC B cells that acquire high-affinity mutations enables antibody affinity maturation. In spite of considerable progress, the genomic states underlying this process remain to be elucidated. Single-cell RNA sequencing and topic modeling revealed increased expression of the oxidative phosphorylation (OXPHOS) module in GC B cells undergoing mitoses. Coupled analysis of somatic hypermutation in immunoglobulin heavy chain (Igh) variable gene regions showed that GC B cells acquiring higher-affinity mutations had further elevated expression of OXPHOS genes. Deletion of mitochondrial Cox10 in GC B cells resulted in reduced cell division and impaired positive selection. Correspondingly, augmentation of OXPHOS activity with oltipraz promoted affinity maturation. We propose that elevated OXPHOS activity promotes B cell clonal expansion and also positive selection by tuning cell division times.
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Affiliation(s)
- Dianyu Chen
- College of Life Sciences, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Yan Wang
- College of Life Sciences, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Godhev K Manakkat Vijay
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shujie Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Colt W Nash
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Di Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Danyang He
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Nathan Salomonis
- Division of Biomedical Informatics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Harinder Singh
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Heping Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
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4
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Isaza C, Rosas JF, Lorenzo E, Marrero A, Ortiz C, Ortiz MR, Perez L, Cabrera‐Ríos M. Biological signaling pathways and potential mathematical network representations: biological discovery through optimization. Cancer Med 2018; 7:1875-1895. [PMID: 29635835 PMCID: PMC5943441 DOI: 10.1002/cam4.1301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 11/17/2017] [Accepted: 11/21/2017] [Indexed: 01/04/2023] Open
Abstract
Establishing the role that different genes play in the development of cancer is a daunting task. A step toward this end is the detection of genes that are important in the illness from high-throughput biological experiments. Furthermore, it is safe to say that it is highly unlikely that these show expression changes independently, even with a list of potentially important genes. A biological signaling pathway is a more plausible underlying mechanism as favored in the literature. This work attempts to build a mathematical network problem through the analysis of microarray experiments. A preselection of genes is carried out with a multiple criteria optimization framework previously published by our research group . Afterward, application of the Traveling Salesperson Problem and Minimum Spanning Tree network optimization models are proposed to identify potential signaling pathways via the most correlated path among the genes of interest. Biological evidencing is provided to assess the effectiveness of the proposed methods. The capability of our analysis strategy is also demonstrated through the undertaking of meta-analysis studies. Three important aspects are novel in this work: (1) our joint analyses of different groups of lung cancer states reveal new correlations, biologically evidenced, and previously undocumented; (2) computation of the correlation coefficients from expression differences leads to an effective use of network optimization methods; and (3) the methods yield mathematically optimal correlation structures: no other configuration is better correlated using the available information.
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Affiliation(s)
- Clara Isaza
- Public Health ProgramPonce Health Sciences UniversityPonce00732‐7004Puerto Rico
| | - Juan F. Rosas
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Enery Lorenzo
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Arlette Marrero
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Cristina Ortiz
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Michael R. Ortiz
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Lynn Perez
- Biology DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
| | - Mauricio Cabrera‐Ríos
- Industrial Engineering DepartmentThe Applied Optimization GroupUniversity of Puerto Rico‐MayagüezMayagüez00681‐9043Puerto Rico
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5
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Zhao Y, Hoang TH, Joshi P, Hong SH, Giardina C, Shin DG. A route-based pathway analysis framework integrating mutation information and gene expression data. Methods 2017. [PMID: 28647608 DOI: 10.1016/j.ymeth.2017.06.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
We propose a new way of analyzing biological pathways in which the analysis combines both transcriptome data and mutation information and uses the outcome to identify "routes" of aberrant pathways potentially responsible for the etiology of disease. Each pathway route is encoded as a Bayesian Network which is initialized with a sequence of conditional probabilities which are designed to encode directionality of regulatory relationships encoded in the pathways, i.e. activation and inhibition relationships. First, we demonstrate the effectiveness of our model through simulation in which the model was able to easily separate Test samples from Control samples using fictitiously perturbed pathway routes. Second, we apply our model to analyze the Breast Cancer data set, available from TCGA, against many cancer pathways available from KEGG and rank the significance of identified pathways. The outcome is consistent with what have already been reported in the literature. Third, survival analysis has been carried out on the same data set by using pathway routes as features. Overall, we envision that our model of using pathway routes for analysis can further refine the conventional ways of subtyping cancer patients as it can discover additional characteristics specific to individual's tumor.
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Affiliation(s)
- Yue Zhao
- Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT 06269, United States.
| | - Tham H Hoang
- Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT 06269, United States
| | - Pujan Joshi
- Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT 06269, United States
| | - Seung-Hyun Hong
- Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT 06269, United States
| | - Charles Giardina
- Department of Molecular and Cell Biology, University of Connecticut, 91 North Eagleville Road, Unit 3125, Storrs, CT 06269, United States
| | - Dong-Guk Shin
- Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT 06269, United States
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6
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Peng L, Carvalho L. Bayesian degree-corrected stochastic blockmodels for community detection. Electron J Stat 2016. [DOI: 10.1214/16-ejs1163] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Hung FH, Chiu HW, Chang YC. Revealing pathway maps of renal cell carcinoma by gene expression change. Comput Biol Med 2014; 51:111-21. [DOI: 10.1016/j.compbiomed.2014.04.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Revised: 04/24/2014] [Accepted: 04/28/2014] [Indexed: 11/29/2022]
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8
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Mohamed A, Hancock T, Nguyen CH, Mamitsuka H. NetPathMiner: R/Bioconductor package for network path mining through gene expression. ACTA ACUST UNITED AC 2014; 30:3139-41. [PMID: 25075120 DOI: 10.1093/bioinformatics/btu501] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
UNLABELLED NetPathMiner is a general framework for mining, from genome-scale networks, paths that are related to specific experimental conditions. NetPathMiner interfaces with various input formats including KGML, SBML and BioPAX files and allows for manipulation of networks in three different forms: metabolic, reaction and gene representations. NetPathMiner ranks the obtained paths and applies Markov model-based clustering and classification methods to the ranked paths for easy interpretation. NetPathMiner also provides static and interactive visualizations of networks and paths to aid manual investigation. AVAILABILITY The package is available through Bioconductor and from Github at http://github.com/ahmohamed/NetPathMiner.
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Affiliation(s)
- Ahmed Mohamed
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Timothy Hancock
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Canh Hao Nguyen
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan and Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
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Hoppe A. What mRNA Abundances Can Tell us about Metabolism. Metabolites 2012; 2:614-31. [PMID: 24957650 PMCID: PMC3901220 DOI: 10.3390/metabo2030614] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 08/24/2012] [Accepted: 09/04/2012] [Indexed: 01/23/2023] Open
Abstract
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.
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Affiliation(s)
- Andreas Hoppe
- Institute for Biochemistry, Charité University Medicine Berlin, Charitéplatz 1, Berlin 10117, Germany.
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10
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Gianoulis TA, Griffin MA, Spakowicz DJ, Dunican BF, Alpha CJ, Sboner A, Sismour AM, Kodira C, Egholm M, Church GM, Gerstein MB, Strobel SA. Genomic analysis of the hydrocarbon-producing, cellulolytic, endophytic fungus Ascocoryne sarcoides. PLoS Genet 2012; 8:e1002558. [PMID: 22396667 PMCID: PMC3291568 DOI: 10.1371/journal.pgen.1002558] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Accepted: 01/12/2012] [Indexed: 11/19/2022] Open
Abstract
The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Endophytes represent a promising group of organisms, as they are a mostly untapped reservoir of metabolic diversity. They are often able to degrade cellulose, and they can produce an extraordinary diversity of metabolites. The filamentous fungal endophyte Ascocoryne sarcoides was shown to produce potential-biofuel metabolites when grown on a cellulose-based medium; however, the genetic pathways needed for this production are unknown and the lack of genetic tools makes traditional reverse genetics difficult. We present the genomic characterization of A. sarcoides and use transcriptomic and metabolomic data to describe the genes involved in cellulose degradation and to provide hypotheses for the biofuel production pathways. In total, almost 80 biosynthetic clusters were identified, including several previously found only in plants. Additionally, many transcriptionally active regions outside of genes showed condition-specific expression, offering more evidence for the role of long non-coding RNA in gene regulation. This is one of the highest quality fungal genomes and, to our knowledge, the only thoroughly annotated and transcriptionally profiled fungal endophyte genome currently available. The analyses and datasets contribute to the study of cellulose degradation and biofuel production and provide the genomic foundation for the study of a model endophyte system. A renewable source of energy is a pressing global need. The biological conversion of lignocellulose to biofuels by microorganisms presents a promising avenue, but few organisms have been studied thoroughly enough to develop the genetic tools necessary for rigorous experimentation. The filamentous-fungal endophyte A. sarcoides produces metabolites when grown on a cellulose-based medium that include eight-carbon volatile organic compounds, which are potential biofuel targets. Here we use broadly applicable methods including genomics, transcriptomics, and metabolomics to explore the biofuel production of A. sarcoides. These data were used to assemble the genome into 16 scaffolds, to thoroughly annotate the cellulose-degradation machinery, and to make predictions for the production pathway for the eight-carbon volatiles. Extremely high expression of the gene swollenin when grown on cellulose highlights the importance of accessory proteins in addition to the enzymes that catalyze the breakdown of the polymers. Correlation of the production of the eight-carbon biofuel-like metabolites with the expression of lipoxygenase pathway genes suggests the catabolism of linoleic acid as the mechanism of eight-carbon compound production. This is the first fungal genome to be sequenced in the family Helotiaceae, and A. sarcoides was isolated as an endophyte, making this work also potentially useful in fungal systematics and the study of plant–fungus relationships.
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Affiliation(s)
- Tara A. Gianoulis
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Meghan A. Griffin
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Daniel J. Spakowicz
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Brian F. Dunican
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Cambria J. Alpha
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Andrea Sboner
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - A. Michael Sismour
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts, United States of America
| | - Chinnappa Kodira
- Roche 454 Life Sciences, Branford, Connecticut, United States of America
| | - Michael Egholm
- Pall Corporation, Long Island City, New York, United States of America
| | - George M. Church
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
- Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts, United States of America
| | - Mark B. Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- * E-mail: (MBG); (SAS)
| | - Scott A. Strobel
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- * E-mail: (MBG); (SAS)
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11
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Hancock T, Wicker N, Takigawa I, Mamitsuka H. Identifying neighborhoods of coordinated gene expression and metabolite profiles. PLoS One 2012; 7:e31345. [PMID: 22355360 PMCID: PMC3280297 DOI: 10.1371/journal.pone.0031345] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2011] [Accepted: 01/06/2012] [Indexed: 11/19/2022] Open
Abstract
In this paper we investigate how metabolic network structure affects any coordination between transcript and metabolite profiles. To achieve this goal we conduct two complementary analyses focused on the metabolic response to stress. First, we investigate the general size of any relationship between metabolic network gene expression and metabolite profiles. We find that strongly correlated transcript-metabolite profiles are sustained over surprisingly long network distances away from any target metabolite. Secondly, we employ a novel pathway mining method to investigate the structure of this transcript-metabolite relationship. The objective of this method is to identify a minimum set of metabolites which are the target of significantly correlated gene expression pathways. The results reveal that in general, a global regulation signature targeting a small number of metabolites is responsible for a large scale metabolic response. However, our method also reveals pathway specific effects that can degrade this global regulation signature and complicates the observed coordination between transcript-metabolite profiles.
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