What MRNA Abundances Can Tell Us About Metabolism

Overview

Journal Metabolites

Publisher MDPI

Date 2014 Jun 25

PMID 24957650

Citations 21

Authors

Andreas Hoppe

Affiliations

Soon will be listed here.

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.

Citing Articles

Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.

Goncalves D, Henriques R, Costa R Comput Struct Biotechnol J. 2023; 21:4960-4973.

PMID: 37876626 PMC: 10590844. DOI: 10.1016/j.csbj.2023.10.002.

Differential effects of bariatric surgery and caloric restriction on hepatic one-carbon and fatty acid metabolism.

Haran A, Bergel M, Kleiman D, Hefetz L, Israeli H, Weksler-Zangen S iScience. 2023; 26(7):107046.

PMID: 37389181 PMC: 10300224. DOI: 10.1016/j.isci.2023.107046.

Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice.

Macioszek S, Dudzik D, Bartoszewski R, Stokowy T, Lambrechts D, Boeckx B Transl Oncol. 2023; 30:101632.

PMID: 36774883 PMC: 9945753. DOI: 10.1016/j.tranon.2023.101632.

A Single-Cell Raman Spectroscopy Analysis of Bone Marrow Mesenchymal Stem/Stromal Cells to Identify Inter-Individual Diversity.

Kukolj T, Lazarevic J, Borojevic A, Ralevic U, Vujic D, Jaukovic A Int J Mol Sci. 2022; 23(9).

PMID: 35563306 PMC: 9103070. DOI: 10.3390/ijms23094915.

Metabolic Survival Adaptations of Plasmodium falciparum Exposed to Sublethal Doses of Fosmidomycin.

Tewari S, Rajaram K, Swift R, Reifman J, Prigge S, Wallqvist A Antimicrob Agents Chemother. 2021; 65(4).

PMID: 33495219 PMC: 8097461. DOI: 10.1128/AAC.02392-20.

References

Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9. PMC: 3037419. DOI: 10.1038/75556. View

Eddy J, Sung J, Geman D, Price N . Relative expression analysis for molecular cancer diagnosis and prognosis. Technol Cancer Res Treat. 2010; 9(2):149-59. PMC: 2921829. DOI: 10.1177/153303461000900204. View

Reed J, Palsson B . Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res. 2004; 14(9):1797-805. PMC: 515326. DOI: 10.1101/gr.2546004. View

Daran-Lapujade P, Rossell S, van Gulik W, Luttik M, Groot M, Slijper M . The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc Natl Acad Sci U S A. 2007; 104(40):15753-8. PMC: 2000426. DOI: 10.1073/pnas.0707476104. View

Baldwin D, Crane V, Rice D . A comparison of gel-based, nylon filter and microarray techniques to detect differential RNA expression in plants. Curr Opin Plant Biol. 1999; 2(2):96-103. DOI: 10.1016/S1369-5266(99)80020-X. View

Ghosh A, Zhao H, Price N . Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in Saccharomyces cerevisiae. PLoS One. 2011; 6(11):e27316. PMC: 3208632. DOI: 10.1371/journal.pone.0027316. View

Szappanos B, Kovacs K, Szamecz B, Honti F, Costanzo M, Baryshnikova A . An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat Genet. 2011; 43(7):656-62. PMC: 3125439. DOI: 10.1038/ng.846. View

Ladunga I . An overview of the computational analyses and discovery of transcription factor binding sites. Methods Mol Biol. 2010; 674:1-22. DOI: 10.1007/978-1-60761-854-6_1. View

Werner T . Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol. 2008; 19(1):50-4. DOI: 10.1016/j.copbio.2007.11.005. View

10.

Hoffmann S, Holzhutter H . Uncovering metabolic objectives pursued by changes of enzyme levels. Ann N Y Acad Sci. 2009; 1158:57-70. DOI: 10.1111/j.1749-6632.2008.03753.x. View

11.

Reder C . Metabolic control theory: a structural approach. J Theor Biol. 1988; 135(2):175-201. DOI: 10.1016/s0022-5193(88)80073-0. View

12.

Thiele I, Palsson B . A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010; 5(1):93-121. PMC: 3125167. DOI: 10.1038/nprot.2009.203. View

13.

Ishihama Y, Schmidt T, Rappsilber J, Mann M, Hartl F, Kerner M . Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics. 2008; 9:102. PMC: 2292177. DOI: 10.1186/1471-2164-9-102. View

14.

Saito K, Matsuda F . Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol. 2009; 61:463-89. DOI: 10.1146/annurev.arplant.043008.092035. View

15.

Duarte N, Becker S, Jamshidi N, Thiele I, Mo M, Vo T . Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007; 104(6):1777-82. PMC: 1794290. DOI: 10.1073/pnas.0610772104. View

16.

Rossell S, van der Weijden C, Lindenbergh A, van Tuijl A, Francke C, Bakker B . Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A. 2006; 103(7):2166-71. PMC: 1413710. DOI: 10.1073/pnas.0509831103. View

17.

Kanehisa M . The KEGG database. Novartis Found Symp. 2003; 247:91-101; discussion 101-3, 119-28, 244-52. View

18.

Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A . Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science. 2007; 316(5824):593-7. DOI: 10.1126/science.1132067. View

19.

Oberhardt M, Goldberg J, Hogardt M, Papin J . Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J Bacteriol. 2010; 192(20):5534-48. PMC: 2950489. DOI: 10.1128/JB.00900-10. View

20.

Edelman L, Toia G, Geman D, Zhang W, Price N . Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases. BMC Genomics. 2009; 10:583. PMC: 2797819. DOI: 10.1186/1471-2164-10-583. View