Metabolomics

, 12:167 | Cite as

Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes

  • Clare H. Scott Chialvo
  • Ronglin Che
  • David Reif
  • Alison Motsinger-Reif
  • Laura K. Reed
Original Article
First Online:
Received:
Accepted:

Abstract

Introduction

‘Multi-omics’ datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites.

Objectives

Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts.

Methods

To demonstrate the utility of EvMA, we analyzed metabolomic, transcriptomic, and phenotypic datasets produced from 20 Drosophila melanogaster genotypes reared on four dietary treatments. We used a hierarchical distance-based method to cluster the metabolites. The links between metabolite clusters, gene expression, and overt phenotypes were characterized using the eigenmetabolite (first principal component) of each cluster.

Results

EvMA recovered chemically related groups of metabolites within the clusters. Using the eigenmetabolite, we identified genes and phenotypes that significantly correlated with each cluster. EvMA identifies new connections between the phenotypes, metabolites, and gene transcripts.

Conclusion

EvMA provides a simple method to identify correlations between metabolites, gene expression, and phenotypes, which can allow us to partition multivariate datasets into meaningful biological modules and identify under-studied metabolites and unannotated gene transcripts that may be central to important biological processes. This can be used to inform our understanding of the effect of environmental mechanisms underlying physiological states of interest.

Keywords

Eigenvector metabolite analysis Linkage analyses Environment Enrichment analyses 
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Notes

Acknowledgments

Funding for this study was provided by the National Institute of Health (NIH)-R01 GM098856 to LR, NIH-NRSA Fellowship to LR, NIH-R01 GM61600 to Greg Gibson, and Australian Research Council (ARC) DP0880204 to Greg Gibson. We thank Vishal Oza, Pablo Chialvo, and members of the Reed Lab for helpful suggestions and critical comments on this manuscript.

Compliance with ethical standards

Conflicts of interest

The authors declare they have no potential conflicts of interest.

Research involving human participants and/or animals

This article does not include any studies involving humans or regulated animal models.

Informed consent

The studies presented in this article did not include human subjects.

Supplementary material

11306_2016_1117_MOESM1_ESM.xlsx (60 kb)
Supplemental Data 1: List of all metabolic features used in the analysis. For each feature, the list includes the highest level of identification based on the Metabolomic Standards Initiative, assigned identification number, retention time, retention index, identification based on the best NIST hit, likely chemical category, likely compound identification, confirmed identification using standards, assigned chemical class, and cluster placement for the combined dataset and the individual dietary treatments. (XLSX 60 kb)
11306_2016_1117_MOESM2_ESM.xlsx (56 kb)
Supplemental Data 2: Summary table of the clusters for the combined dataset and each diet. For each cluster, the summary includes the number of metabolic features found in the cluster, the eigenvector value of the cluster, the number of genes that were significantly correlated using a Bonferroni Correction and a FDR Correction, the number of genes that could be updated to the sixth FlyBase release (FB2015_2), and the number of the updated genes recognized by the functional annotation tool in the DAVID Bioinformatics Resource v6.7. (XLSX 55 kb)
11306_2016_1117_MOESM3_ESM.xlsx (419 kb)
Supplemental Data 3: List of all significantly correlated gene transcripts identified for each cluster in the combined dataset and individual diets. For each cluster, the list includes the FlyBase transcript ID, the q-value for the correlation, and the name of the gene. (XLSX 419 kb)
11306_2016_1117_MOESM4_ESM.xlsx (455 kb)
Supplemental Data 4: Summary of enriched terms for each cluster in combined dataset and individual diets. For each cluster, the summary includes all the enriched terms and the category of the term (e.g., GO Term, KEGG Pathway), p-value of enrichment, the number of genes enriched for that biological function, percentage of the gene list enriched for a function, and FlyBase transcripts numbers of the genes that possess the enriched term. (XLSX 454 kb)

References

  1. Alvarez, M., Schrey, A. W., & Richards, C. L. (2015). Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Molecular Ecology, 24(4), 710–725. doi: 10.1111/mec.13055.PubMedCrossRefGoogle Scholar
  2. Berg, J. M., Tymoczko, J. L., & Stryer, L. (2002). Section 22.5, Acetyl Coenzyme A carboxylase plays a key role in controlling fatty acid metabolism. In Biochemistry (5th ed., pp. Available from: http://www.ncbi.nlm.nih.gov/books/NBK22381/). New York: W. H. Freeman.
  3. Boirie, Y. (2003). Insulin regulation of mitochondrial proteins and oxidative phosphorylation in human muscle. Trends in Endocrinology and Metabolism, 14(9), 393–394. doi: 10.1016/j.tem.2003.09.002.PubMedCrossRefGoogle Scholar
  4. Buchner, D. A., Yazbek, S. N., Solinas, P., Burrage, L. C., Morgan, M. G., Hoppel, C. L., et al. (2011). Increased mitochondrial oxidative phosphorylation in the liver is associated with obesity and insulin resistance. Obesity (Silver Spring), 19(5), 917–924. doi: 10.1038/oby.2010.214.CrossRefGoogle Scholar
  5. Burke, C. J., Huetteroth, W., Owald, D., Perisse, E., Krashes, M. J., Das, G., et al. (2012). Layered reward signalling through octopamine and dopamine in Drosophila. Nature, 492(7429), 433–437. doi: 10.1038/nature11614.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Cho, K., Evans, B. S., Wood, B. M., Kumar, R., Erb, T. J., Warlick, B. P., et al. (2014). Integration of untargeted metabolomics with transcriptomics reveals active metabolic pathways. Metabolomics. doi: 10.1007/s11306-014-0713-3.PubMedPubMedCentralGoogle Scholar
  7. Cree-Green, M., Newcomer, B. R., Coe, G., Newnes, L., Baumgartner, A., Brown, M. S., et al. (2015). Peripheral insulin resistance in obese girls with hyperandrogenism is related to oxidative phophorylation and elevated serum free fatty acids. American Journal of Physiology-Endocrinology and Metabolism, 308, E726–E733. doi: 10.1152/ajpendo.00619.2014.-Hyperandrogenic.PubMedPubMedCentralCrossRefGoogle Scholar
  8. Culibrk, L., Croft, C. A., & Tebbutt, S. J. (2016). Systems biology approaches for host-fungal interactions: An expanding multi-omics frontier. OMICS: A Journal of Integrative Biology, 20(3), 127–138. doi: 10.1089/omi.2015.0185.PubMedCrossRefGoogle Scholar
  9. dos Santos, G., Schroeder, A. J., Goodman, J. L., Strelets, V. B., Crosby, M. A., Thurmond, J., et al. (2015). FlyBase: Introduction of the Drosophila melanogaster release 6 reference genome assembly and large-scale migration of genome annotations. Nucleic Acids Research, 43, D690–697. doi: 10.1093/nar/gku1099.PubMedCrossRefGoogle Scholar
  10. Dunn, W. B., Erban, A., Weber, R. J. M., Creek, D. J., Brown, M., Breitling, R., et al. (2012). Mass appeal: Metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics, 9(S1), 44–66. doi: 10.1007/s11306-012-0434-4.CrossRefGoogle Scholar
  11. Evans, P. D. (1980). Biogenic amines in the insect nervous system. Advances in Insect Physiology, 15, 317–473.CrossRefGoogle Scholar
  12. Fei, F., Mendonca, M. L., McCarry, B. E., Bowdish, D. M. E., & Surette, M. G. (2016). Metabolic and transcriptomic profiling of Streptococcus intermedius during aerobic and anaerobic growth. Metabolomics, 12(3), 1–13. doi: 10.1007/s11306-016-0966-0.CrossRefGoogle Scholar
  13. Gehlenborg, N., O’Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M. A., Kitano, H., et al. (2010). Visualization of omics data for systems biology. Nature Methods, 7(3S), S56–S68. doi: 10.1038/nmeth.1436.PubMedCrossRefGoogle Scholar
  14. Gligorijević, V., Malod-Dognin, N., & Pržulj, N. (2016). Integrative methods for analyzing big data in precision medicine. Proteomics, 16(5), 741–758. doi: 10.1002/pmic.201500396.PubMedCrossRefGoogle Scholar
  15. Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A. L., Baranzini, S., Brunak, S., et al. (2014). Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome Medicine, 6, 82.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Halouska, S., & Powers, R. (2006). Negative impact of noise on the principal component analysis of NMR data. Journal of Magnetic Resonance, 178, 88–95.PubMedCrossRefGoogle Scholar
  17. Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1–13. doi: 10.1093/nar/gkn923.CrossRefGoogle Scholar
  18. Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57.CrossRefGoogle Scholar
  19. Jia, X., Sun, C., Zuo, Y., Li, G., Li, G., Ren, L., et al. (2016). Integrating transcriptomics and metabolomics to characterise the response of Astragalus membranaceus Bge. var. mongolicus (Bge.) to progressive drought stress. BMC Genomics, 17(1), 188. doi: 10.1186/s12864-016-2554-0.PubMedPubMedCentralCrossRefGoogle Scholar
  20. Kaever, A., Landesfeind, M., Feussner, K., Mosblech, A., Heilmann, I., Morgenstern, B., et al. (2015). MarVis-pathway: Integrative and exploratory pathway analysis of non-targeted metabolomics data. Metabolomics, 11(3), 764–777. doi: 10.1007/s11306-014-0734-y.PubMedCrossRefGoogle Scholar
  21. Katz, L., & Baltz, R. H. (2016). Natural product discovery: Past, present, and future. Journal of Industrial Microbiology and Biotechnology, 43(2–3), 155–176. doi: 10.1007/s10295-015-1723-5.PubMedCrossRefGoogle Scholar
  22. Kuo, T. C., Tian, T. F., & Tseng, Y. J. (2013). 3Omics: A web-based systems biology tool for analysis, integration and visualization of human, transcriptomic, proteomic and metabolomic data. BMC Systems Biology, 7, 64.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Lakshmanan, M., Lim, S. H., Mohanty, B., Kim, J. K., Ha, S. H., & Lee, D. Y. (2015). Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multiomics analysis. Plant Physiology, 169(4), 3002–3020. doi: 10.1104/pp.15.01379.PubMedPubMedCentralGoogle Scholar
  24. Linstrom, P. J., & Mallard, W. G. (Eds.). (2016). NIST chemistry webbook, NIST standard reference database number 69 (Vol. Retrieved July 18, 2012). Gaithersburg, MD 20899: National Institute of Standards and Technology.Google Scholar
  25. Marmiesse, L., Peyraud, R., & Cottret, L. (2015). FlexFlux: Combining metabolic flux and regulatory network analyses. BMC Systems Biology, 9, 93. doi: 10.1186/s12918-015-0238-z.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Martínez-Ramírez, A. C., Ferré, J., & Silva, F. J. (1992). Catecholamines in Drosophila melanogaster: Dopa and dopamine accumulation during development. Insect Biochemistry and Molecular Biology, 22(5), 491–494.CrossRefGoogle Scholar
  27. McHardy, I. H., Goudarzi, M., Tong, M., Ruegger, P. M., Schwager, E., Weger, J. R., et al. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome, 1, 17.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Nuwaysir, E. F., Huang, W., Albert, T. J., Singh, J., Nuwaysir, K., Pitas, A., et al. (2002). Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Genome Research, 12, 1749–1755. doi: 10.1101/gr.362402.PubMedPubMedCentralCrossRefGoogle Scholar
  29. Osorio, S., Alba, R., Nikoloski, Z., Kochevenko, A., Fernie, A. R., & Giovannoni, J. J. (2012). Integrative comparative analyses of transcript and metabolite profiles from pepper and tomato ripening and development stages uncovers species-specific patterns of network regulatory behavior. Plant Physiology, 159(4), 1713–1729. doi: 10.1104/pp.112.199711.PubMedPubMedCentralCrossRefGoogle Scholar
  30. Pavey, S. A., Bernatchez, L., Aubin-Horth, N., & Landry, C. R. (2012). What is needed for next-generation ecological and evolutionary genomics? Trends in Ecology & Evolution, 27(12), 673–678. doi: 10.1016/j.tree.2012.07.014.CrossRefGoogle Scholar
  31. Peng, J., Zeng, J., Cai, B., Yang, H., Cohen, M. J., Chen, W., et al. (2014). Establishment of quantitative severity evalution model for spinal cord injury by metabolomic fingerprinting. PLoS One, 9(4), e93736.PubMedPubMedCentralCrossRefGoogle Scholar
  32. Raupach, M. J., Amann, R., Wheeler, Q. D., & Roos, C. (2016). The application of “-omics” technologies for the classification and identification of animals. Organisms Diversity & Evolution, 16(1), 1–12. doi: 10.1007/s13127-015-0234-6.CrossRefGoogle Scholar
  33. RCoreTeam (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
  34. Rebollar, E. A., Antwis, R. E., Becker, M. H., Belden, L. K., Bletz, M. C., Brucker, R. M., et al. (2016). Using “Omics” and integrated multi-omics approaches to guide probiotic selection to mitigate chytridiomycosis and other emerging infectious diseases. Front Microbiol, 7, 68. doi: 10.3389/fmicb.2016.00068.PubMedPubMedCentralCrossRefGoogle Scholar
  35. Redestig, H., & Costa, I. G. (2011). Detection and interpretation of metabolite-transcript coresponses using combined profiling data. Bioinformatics, 27(13), i357–365. doi: 10.1093/bioinformatics/btr231.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Reed, L. K., Lee, K., Zhang, Z., Rashid, L., Poe, A., Hsieh, B., et al. (2014). Systems genomics of metabolic phenotypes in wild-type Drosophila melanogaster. Genetics, 197, 781–793. doi: 10.1534/genetics.114.163857/-/DC1.PubMedPubMedCentralCrossRefGoogle Scholar
  37. Reed, L. K., Williams, S., Springston, M., Brown, J., Freeman, K., DesRoches, C. E., et al. (2010). Genotype-by-diet interactions drive metabolic phenotype variation in Drosophila melanogaster. Genetics, 185(3), 1009–1019. doi: 10.1534/genetics.109.113571.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Serra, A. A., Couee, I., Heijnen, D., Michon-Coudouel, S., Sulmon, C., & Gouesbet, G. (2015). Genome-wide transcriptional profiling and metabolic analysis uncover multiple molecular responses of the grass species lolium perenne under low-intensity xenobiotic stress. Front Plant Sci, 6, 1124. doi: 10.3389/fpls.2015.01124.PubMedPubMedCentralCrossRefGoogle Scholar
  39. Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society. Series B, Methodological, 64(3), 479–498.CrossRefGoogle Scholar
  40. Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276.CrossRefGoogle Scholar
  41. Trikka, F. A., Nikolaidis, A., Ignea, C., Tsaballa, A., Tziveleka, L. A., Ioannou, E., et al. (2015). Combined metabolome and transcriptome profiling provides new insights into diterpene biosynthesis in S. pomifera glandular trichomes. BMC Genomics, 16(1), 935. doi: 10.1186/s12864-015-2147-3.PubMedPubMedCentralCrossRefGoogle Scholar
  42. Valcàrcel, B., Ebbels, T. M., Kangas, A. J., Soininen, P., Elliot, P., Ala-Korpela, M., et al. (2014). Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: An application to obesity. Journal of the Royal Society, Interface, 11(94), 20130908. doi: 10.1098/rsif.2013.0908.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Van Swinderen, B., & Andretic, R. (2011). Dopamine in Drosophila: Setting arousal thresholds in a miniature brain. Proc Biol Sci, 278(1707), 906–913. doi: 10.1098/rspb.2010.2564.PubMedPubMedCentralCrossRefGoogle Scholar
  44. Wägele, B., Witting, M., Schmitt-Kopplin, P., & Suhre, K. (2012). MassTRIX reloaded: Combined analysis and visualization of transcriptome and metabolome data. PLoS One, 7(7), e39860. doi: 10.1371/journal.pone.0039860.PubMedPubMedCentralCrossRefGoogle Scholar
  45. Williams, S., Dew-Budd, K., Davis, K. C., Anderson, J., Bishop, R., Freeman, K., et al. (2015). Metabolomic and gene expresion profiles exhibit modular genetic and dietary structure linking metabolic syndrome phenotypes in Drosophila., G3(5), 2817–2829. doi: 10.1534/g3.115.023564/-/DC1.Google Scholar
  46. Zhang, W., Li, F., & Nie, L. (2010). Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology, 156(Pt 2), 287–301. doi: 10.1099/mic.0.034793-0.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Biological SciencesUniversity of AlabamaTuscaloosaUSA
  2. 2.Department of Biological SciencesNorth Carolina State UniversityRaleighUSA
  3. 3.Department of StatisticsNorth Carolina State UniversityRaleighUSA

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