Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Çakir, Tunahan ^a  

^a GEBZE TECHNICAL UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTOME;

ALGORITHM; ALZHEIMER DISEASE; BIOLOGICAL MODEL; GENE EXPRESSION REGULATION; GENETICS; HUMAN; METABOLISM; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; ALZHEIMER DISEASE; GENE EXPRESSION REGULATION; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; SACCHAROMYCES CEREVISIAE; TRANSCRIPTOME;

EID: 84942456183     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep14563     Document Type: Article

References (58)

1. Curtis, R. K., Oresic, M. & Vidal-Puig, A. Pathways to the analysis of microarray data. Trends Biotechnol. 23, 429-435 (2005).
2. Grosu, P., Townsend, J. P., Hartl, D. L. & Cavalieri, D. Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res. 12, 1121-1126 (2002).
3. Ertel, A., Verghese, A., Byers, S. W., Ochs, M. & Tozeren, A. Pathway-specific differences between tumor cell lines and normal and tumor tissue cells. Mol. Cancer 5, 55, doi: 10.1186/1476-4598-5-55 (2006).
4. Tarca, A. L., Bhatti, G. & Romero, R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PloS one 8, e79217, doi: 10.1371/journal.pone.0079217 (2013).
5. Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic acids Res. 39, W316-W322 (2011).
6. Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267-273 (2003).
7. Patil, K. R. & Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. United States Am. 102, 2685-2689 (2005).
8. Zelezniak, A., Pers, T. H., Soares, S., Patti, M. E. & Patil, K. R. Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes. PLoS Comput. Biol. 6, e1000729, doi: 10.1371/journal.pcbi.1000729 (2010).
9. Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids Res. 42, D199-D205 (2014).
10. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic acids Res. 42, D459-D471 (2014).
11. Croft, D. et al. The Reactome pathway knowledgebase. Nucleic acids Res. 42, D472-D477 (2014).
12. Kim, T. Y., Sohn, S. B., Kim, Y. B., Kim, W. J. & Lee, S. Y. Recent advances in reconstruction and applications of genome-scale metabolic models. Curr. Opin. Biotechnol. 23, 617-623 (2012).
13. Nissen, T. L., Kielland-Brandt, M. C., Nielsen, J. & Villadsen, J. Optimization of ethanol production in Saccharomyces cerevisiae by metabolic engineering of the ammonium assimilation. Metab. Eng. 2, 69-77 (2000).
14. Bro, C., Regenberg, B. & Nielsen, J. Genome-wide transcriptional response of aSaccharomyces cerevisiae strain with an altered redox metabolism. Biotechnol. Bioeng. 85, 269-276, (2004).
15. Villas-Bôas, S. G., Moxley, J. F., Akesson, M., Stephanopoulos, G. & Nielsen, J. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem. J. 388, 669-677 (2005).
16. Celton, M., Goelzer, A., Camarasa, C., Fromion, V. & Dequin, S. A constraint-based model analysis of the metabolic consequences of increased NADPH oxidation in Saccharomyces cerevisiae. Metab. Eng. 14, 366-379 (2012).
17. Knijnenburg, T. A. et al. Exploiting combinatorial cultivation conditions to infer transcriptional regulation. BMC Genomic 8, 25, doi: 10.1186/1471-2164-8-25 (2007).
18. Daran-Lapujade, P. et al. The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc. Natl. Acad. Sci. United States Am. 104, 15753-15758 (2007).
19. Wiebe, M. G. et al. Central carbon metabolism of Saccharomyces cerevisiae in anaerobic, oxygen-limited and fully aerobic steady-state conditions and following a shift to anaerobic conditions. FEMS yeast Res. 8, 140-154 (2008).
20. Fiaux, J. et al. Metabolic-flux profiling of the yeasts Saccharomyces cerevisiae and Pichia stipitis. Eukaryot. cell 2, 170-180 (2003).
21. Bruckmann, A. et al. Proteome analysis of aerobically and anaerobically grown Saccharomyces cerevisiae cells. J. proteomics 71, 662-669 (2009).
22. Rintala, E. et al. Transcriptional responses of Saccharomyces cerevisiae to shift from respiratory and respirofermentative to fully fermentative metabolism. Omics: J. Integr. Biol. 15, 461-476 (2011).
23. Verduyn, C., Postma, E., Scheffers, W. A. & van Dijken, J. P. Physiology of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. J. Gen. Microbiol. 136, 395-403 (1990).
24. Ishtar Snoek, I. S. & Yde Steensma, H. Factors involved in anaerobic growth of Saccharomyces cerevisiae. Yeast 24, 1-10 (2007).
25. Rintala, E. et al. Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae. BMC Genomic 10, 461, doi: 10.1186/1471-2164-10-461 (2009).
26. Zavrel, M., Hoot, S. J. & White, T. C. Comparison of sterol import under aerobic and anaerobic conditions in three fungal species, Candida albicans, Candida glabrata, and Saccharomyces cerevisiae. Eukaryot. cell 12, 725-738 (2013).
27. Hickman, M. J. & Winston, F. Heme levels switch the function of Hap1 of Saccharomyces cerevisiae between transcriptional activator and transcriptional repressor. Mol. Cell. Biol. 27, 7414-7424 (2007).
28. Panozzo, C. et al. Aerobic and anaerobic NAD+ metabolism in Saccharomyces cerevisiae. FEBS Lett. 517, 97-102 (2002).
29. Durrenberger, P. F. et al. Common mechanisms in neurodegeneration and neuroinflammation: a BrainNet Europe gene expression microarray study. J. Neural Transm. 122, 1055-1068 (2015).
30. Sertbaş, M., Ulgen, K. & Cakir, T. Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS open bio 4, 542-553 (2014).
31. Stokes, C. E. & Hawthorne, J. N. Reduced Phosphoinositide Concentrations in Anterior Temporal Cortex of Alzheimer-Diseased Brains. J. Neurochem. 48, 1018-1021 (1987).
32. Miller, B. L. et al. Alzheimer disease: depiction of increased cerebral myo-inositol with proton MR spectroscopy. Radiology 187, 433-437 (1993).
33. Bubber, P., Haroutunian, V., Fisch, G., Blass, J. P. & Gibson, G. E. Mitochondrial abnormalities in Alzheimer brain: Mechanistic implications. Ann. Neurol. 57, 695-703 (2005).
34. Gibson, G. E., Sheu, K.-F. R. & Blass, J. P. Abnormalities of mitochondrial enzymes in Alzheimer disease. J. Neural Transm. 105, 855-870 (1998).
35. Gibson, G. E., Blass, J. P., Beal, M. F. & Bunik, V. The α-ketoglutarate-dehydrogenase complex. Mol. Neurobiol. 31, 43-63 (2005).
36. Lin, L. et al. Melatonin in Alzheimer's disease. International J. Mol. Sci. 14, 14575-14593 (2013).
37. Liu, R. Y., Zhou, J. N., van Heerikhuize, J., Hofman, M. A. & Swaab, D. F. Decreased melatonin levels in postmortem cerebrospinal fluid in relation to aging, Alzheimer's disease, and apolipoprotein E-epsilon4/4 genotype. J. Clin. Endocrinol. Metab. 84, 323-327 (1999).
38. Martins, I. J. et al. Apolipoprotein E, cholesterol metabolism, diabetes, and the convergence of risk factors for Alzheimer's disease and cardiovascular disease. Mol. Psychiatry 11, 721-736 (2006).
39. Van Horssen, J., Wesseling, P., van den Heuvel, L. P. W. J., de Waal, R. M. W. & Verbeek, M. M. Heparan sulphate proteoglycans in Alzheimer's disease and amyloid-related disorders. Lancet. Neurol. 2, 482-492 (2003).
40. Nadanaka, S. & Kitagawa, H. Heparan sulphate biosynthesis and disease. J. Biochem. 144, 7-14 (2008).
41. Zhang, G., Zhang, X., Wang, X. & Li, J.-P. Towards understanding the roles of heparan sulfate proteoglycans in Alzheimer's disease. BioMed Res. Int. 2014, 516028, doi: 10.1155/2014/516028 (2014).
42. Goodman, A. B. Retinoid receptors, transporters, and metabolizers as therapeutic targets in late onset Alzheimer disease. J. Cell. Physiol. 209, 598-603 (2006).
43. Grimm, M. O. W. et al. Amyloid precursor protein (APP) mediated regulation of ganglioside homeostasis linking Alzheimer's disease pathology with ganglioside metabolism. PloS one 7, e34095, doi: 10.1371/journal.pone.0034095 (2012).
44. Ariga, T., McDonald, M. P. & Yu, R. K. Role of ganglioside metabolism in the pathogenesis of Alzheimer's disease-a review. J. Lipid Res. 49, 1157-1175 (2008).
45. Schauer, R. & Varki, A. in Essentials of Glycobiology 2nd edn (eds. Varki, A. et al.) Ch. 14, 199-217 (Cold Spring Harbor Laboratory Press, 2009).
46. Schulz, J. B., Lindenau, J., Seyfried, J. & Dichgans, J. Glutathione, oxidative stress and neurodegeneration. Eur. J. Biochem./FEBS 267, 4904-4911 (2000).
47. Barnham, K. J., Masters, C. L. & Bush, A. I. Neurodegenerative diseases and oxidative stress. Nat. Rev. Drug Discov. 3, 205-214 (2004).
48. Oliveira, A. P., Patil, K. R. & Nielsen, J. Architecture of transcriptional regulatory circuits is knitted over the topology of biomolecular interaction networks. BMC Syst. Biol. 2, 17, doi: 10.1186/1752-0509-2-17 (2008).
49. Fischer, E. & Sauer, U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat. Genet. 37, 636-640 (2005).
50. Fell, D. A. Increasing the flux in metabolic pathways: a metabolic control analysis perspective. Biotechnol. Bioeng. 58, 121-124 (1998).
51. Cvijovic, M. et al. BioMet Toolbox: genome-wide analysis of metabolism. Nucleic acids Res. 38, W144-W149 (2010).
52. Vemuri, G. N., Eiteman, M. A., McEwen, J. E., Olsson, L. & Nielsen, J. Increasing NADH oxidation reduces overflow metabolism in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. United States Am. 104, 2402-2407 (2007).
53. Mutch, D. M. et al. Clément. A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects. Am. J. Clin. Nutr. 94, 1399-1409 (2011).
54. Cicek, A. E., Roeder, K. & Ozsoyoglu, G. MIRA: mutual information-based reporter algorithm for metabolic networks. Bioinformatics 30, i175-i184 (2014).
55. Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083, doi: 10.1038/ncomms4083 (2014).
56. Çakir, T. et al. Integration of metabolome data with metabolic networks reveals reporter reactions. Mol. Syst. Biol. 2, 50, doi: 10.1038/msb4100085 (2006).
57. Tarca, A. L., Draghici, S., Bhatti, G. & Romero, R. Down-weighting overlapping genes improves gene set analysis. BMC Bioinforma. 13, 136; doi: 10.1186/1471-2105-13-136 (2012).
58. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 1-25, (2004).