Abstract
Most microbe-associated infectious diseases severely affect human health. However, clinical diagnosis of pathogenic diseases remains challenging due to the lack of specific and highly reliable methods. To better understand the diagnosis, pathogenesis, and treatment of these diseases, systems biology-driven metabolomics goes beyond the annotated phenotype and better targets the functions than conventional approaches. As a novel strategy for analysis of metabolomes in microbes, microbial metabolomics has been recently used to study many diseases, such as obesity, urinary tract infection (UTI), and hepatitis C. In this chapter, we attempt to introduce various microbial metabolomics methods to better interpret the microbial metabolism underlying a diversity of infectious diseases and inspire scientists to pay more attention to microbial metabolomics, enabling broadly and efficiently its translational applications to infectious diseases, from molecular diagnosis to therapeutic discovery.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cani, P. D., & Jordan, B. F. (2018). Gut microbiota-mediated inflammation in obesity: A link with gastrointestinal cancer. Nature Reviews Gastroenterology & Hepatology, 15(11), 671–682.
Chen, S. L., Wu, M., Henderson, J. P., Hooton, T. M., Hibbing, M. E., Hultgren, S. J., & Gordon, J. I. (2013). Genomic diversity and fitness of E. Coli strains recovered from the intestinal and urinary tracts of women with recurrent urinary tract infection. Science Translational Medicine, 5, 160r–184r.
Saccenti, E., & Timmerman, M. E. (2016). Approaches to sample size determination for multivariate data: Applications to PCA and PLS-DA of omics data. Journal of Proteome Research, 15, 2379–2393.
Zhang, Y., Lin, L., Xu, Y., Lin, Y., Jin, Y., & Zheng, C. (2013). 1h NMR-based spectroscopy detects metabolic alterations in serum of patients with early-stage ulcerative colitis. Biochemical and Biophysical Research Communications, 433, 547–551.
Mamas, M., Dunn, W. B., Neyses, L., & Goodacre, R. (2011). The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Archives of Toxicology, 85, 5–17.
Wang, J., Wang, C., Liu, H., Qi, H., Chen, H., & Wen, J. (2018). Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology. Critical Reviews in Biotechnology, 38, 1–15.
Koek, M. M., Muilwijk, B., van der Werf, M. J., & Hankemeier, T. (2006). Microbial metabolomics with gas chromatography/mass spectrometry. Analytical Chemistry, 78, 1272–1281.
Garcia, D. E., Baidoo, E. E., Benke, P. I., Pingitore, F., Tang, Y. J., Villa, S., & Keasling, J. D. (2008). Separation and mass spectrometry in microbial metabolomics. Current Opinion in Microbiology, 11, 233–239.
Wu, X., Yu, H., Ba, Z., Chen, J., Sun, H., & Han, B. (2010). Sampling methods for NMR-based metabolomics of Staphylococcus Aureus. Biotechnology Journal, 5, 75–84.
Dunn, W. B., & Ellis, D. I. (2005). Metabolomics: Current analytical platforms and methodologies. TrAC Trends in Analytical Chemistry, 24, 285–294.
Mashego, M. R., Rumbold, K., De Mey, M., Vandamme, E., Soetaert, W., & Heijnen, J. J. (2007). Microbial metabolomics: Past, present and future methodologies. Biotechnology Letters, 29, 1–16.
Cortina, N. S., Krug, D., Plaza, A., Revermann, O., & Müller, R. (2012). Myxoprincomide: A natural product from Myxococcus Xanthus discovered by comprehensive analysis of the secondary metabolome. Angewandte Chemie International Edition, 51, 811–816.
Marcobal, A., Kashyap, P. C., Nelson, T. A., Aronov, P. A., Donia, M. S., Spormann, A., Fischbach, M. A., & Sonnenburg, J. L. (2013). A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. The ISME Journal, 7, 1933–1943.
Southam, A. D., Weber, R. J., Engel, J., Jones, M. R., & Viant, M. R. (2016). A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics. Nature Protocols, 12, 310–328.
Cloarec, O., Dumas, M.-E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E., & Nicholson, J. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77(5), 1282–1289.
Lv, H. (2013). Mass spectrometry-based metabolomics towards understanding of gene functions with a diversity of biological contexts. Mass Spectrometry Reviews, 32, 118–128.
Wang, C., Li, M., Jiang, H., Tong, H., Feng, Y., Wang, Y., Pi, X., Guo, L., Nie, M., Feng, H., & Li, E. (2016). Comparative analysis of VOCs in exhaled breath of amyotrophic lateral sclerosis and cervical spondylotic myelopathy patients. Science Reports-UK, 6, 26120.
Frolkis, A., Knox, C., Lim, E., Jewison, T., Law, V., Hau, D. D., Liu, P., Gautam, B., Ly, S., Guo, A. C., Xia, J., Liang, Y., Shrivastava, S., & Wishart, D. S. (2010). SMPDB: The small molecule pathway database. Nucleic Acids Research, 38, D480–D487.
Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., Jupe, S., Kalatskaya, I., Mahajan, S., May, B., Ndegwa, N., Schmidt, E., Shamovsky, V., Yung, C., Birney, E., Hermjakob, H., D’Eustachio, P., & Stein, L. (2010). Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Research, 39, D691–D697.
Joshi-Tope, G. (2004). Reactome: A knowledgebase of biological pathways. Nucleic Acids Research, 33, D428–D432.
Kanehisa, M. (2004). The KEGG resource for deciphering the genome. Nucleic Acids Research, 32, 277D–280D.
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2013). Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Research, 42, D199–D205.
Karp, P. D. (2005). Expansion of the biocyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Research, 33, 6083–6089.
Xia, J., & Wishart, D. S. (2010). MetPA: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics, 26, 2342–2344.
Krummenacker, M., Paley, S., Mueller, L., Yan, T., & Karp, P. D. (2005). Querying and computing with biocyc databases. Bioinformatics, 21, 3454–3455.
Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6, 743–760.
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., Wishart, D. S., & Xia, J. (2018). Metaboanalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Research, 46, W486–W494.
Neuweger, H., Albaum, S. P., Dondrup, M., Persicke, M., Watt, T., Niehaus, K., Stoye, J., & Goesmann, A. (2008). MeltDB: A software platform for the analysis and integration of metabolomics experiment data. Bioinformatics, 24, 2726–2732.
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L., Lewis, M., Coutouly, M. A., Forsythe, I., Tang, P., Shrivastava, S., Jeroncic, K., Stothard, P., Amegbey, G., Block, D., Hau, D. D., Wagner, J., Miniaci, J., Clements, M., Gebremedhin, M., Guo, N., Zhang, Y., Duggan, G. E., MacInnis, G. D., Weljie, A. M., Dowlatabadi, R., Bamforth, F., Clive, D., Greiner, R., Li, L., Marrie, T., Sykes, B. D., Vogel, H. J., & Querengesser, L. (2007). HMDB the human metabolome database. Nucleic Acids Research, 35, D521–D526.
Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A. E., Wolan, D. W., Spilker, M. E., Benton, H. P., & Siuzdak, G. (2018). Metlin: A technology platform for identifying knowns and unknowns. Analytical Chemistry, 90, 3156–3164.
Cui, Q., Lewis, I. A., Hegeman, A. D., Anderson, M. E., Li, J., Schulte, C. F., Westler, W. M., Eghbalnia, H. R., Sussman, M. R., & Markley, J. L. (2008). Metabolite identification via the Madison metabolomics consortium database. Nature Biotechnology, 26, 162–164.
Lv, H., Hung, C. S., Chaturvedi, K. S., Hooton, T. M., & Henderson, J. P. (2011). Development of an integrated metabolomic profiling approach for infectious diseases research. The Analyst, 136, 4752.
Lam, C., Law, C., Sze, K., & To, K. K. (2015). Quantitative metabolomics of urine for rapid etiological diagnosis of urinary tract infection: Evaluation of a microbial-mammalian co-metabolite as a diagnostic biomarker. Clinica Chimica Acta, 438, 24–28.
Lin, Z., Ye, W., Zu, X., Xie, H., Li, H., Li, Y., & Zhang, W. (2018). Integrative metabolic and microbial profiling on patients with spleen-yang-deficiency syndrome. Science Reports-UK, 8, 6619.
Quinn, R. A., Phelan, V. V., Whiteson, K. L., Garg, N., Bailey, B. A., Lim, Y. W., Conrad, D. J., Dorrestein, P. C., & Rohwer, F. L. (2016). Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome. The ISME Journal, 10, 1483–1498.
Preter, V. D., & Verbeke, K. (2013). Metabolomics as a diagnostic tool in gastroenterology. World Journal of Gastrointestinal Pharmacology and Therapeutics, 4, 97.
Walton, C., Fowler, D. P., Turner, C., Jia, W., Whitehead, R. N., Griffiths, L., Dawson, C., Waring, R. H., Ramsden, D. B., Cole, J. A., Cauchi, M., Bessant, C., & Hunter, J. O. (2013). Analysis of volatile organic compounds of bacterial origin in chronic gastrointestinal diseases. Inflammatory Bowel Diseases, 19, 2069–2078.
Stephens, N. S., Siffledeen, J., Su, X., Murdoch, T. B., Fedorak, R. N., & Slupsky, C. M. (2013). Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. Journal of Crohn’s and Colitis, 7, e42–e48.
Ahmed, I., Greenwood, R., Costello, B. L., Ratcliffe, N. M., & Probert, C. S. (2013). An investigation of fecal volatile organic metabolites in irritable bowel syndrome. PLoS One, 8, e58204.
Overgaard, A. J., Weir, J. M., De Souza, D. P., Tull, D., Haase, C., Meikle, P. J., & Pociot, F. (2016). Lipidomic and metabolomic characterization of a genetically modified mouse model of the early stages of human type 1 diabetes pathogenesis. Metabolomics, 12, 13.
Morowitz, M. J., Poroyko, V., Caplan, M., Alverdy, J., & Liu, D. C. (2010). Redefining the role of intestinal microbes in the pathogenesis of necrotizing enterocolitis. Pediatrics, 125, 777–785.
Su, Q., Guan, T., & Lv, H. (2016). Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia Coli and human urine. Science Reports-UK, 6, 24099.
Deatherage, K. B., Li, J., Sanford, J. A., Kim, Y. M., Kronewitter, S. R., Jones, M. B., Peterson, C. T., Peterson, S. N., Frank, B. C., Purvine, S. O., Brown, J. N., Metz, T. O., Smith, R. D., Heffron, F., & Adkins, J. N. (2013). A multi-omic view of host-pathogen-commensal interplay in salmonella-mediated intestinal infection. PLoS One, 8, e67155.
Sun, H., Zhang, A., Yan, G., Piao, C., Li, W., Sun, C., Wu, X., Li, X., Chen, Y., & Wang, X. (2013). Metabolomic analysis of key regulatory metabolites in hepatitis C virus-infected tree shrews. Molecular & Cellular Proteomics, 12, 710–719.
Al-Mubarak, R., Vander, H. J., Broeckling, C. D., Balagon, M., Brennan, P. J., & Vissa, V. D. (2011). Serum metabolomics reveals higher levels of polyunsaturated fatty acids in lepromatous leprosy: Potential markers for susceptibility and pathogenesis. PLoS Neglected Tropical Diseases, 5, e1303.
Davies, J., & Davies, D. (2010). Origins and evolution of antibiotic resistance. Microbiology and Molecular Biology Reviews, 74, 417–433.
Lobritz, M. A., Belenky, P., Porter, C. B. M., Gutierrez, A., Yang, J. H., Schwarz, E. G., Dwyer, D. J., Khalil, A. S., & Collins, J. J. (2015). Antibiotic efficacy is linked to bacterial cellular respiration. Proceedings of the National Academy of Sciences, 112, 8173–8180.
Stipetic, L. H., Dalby, M. J., Davies, R. L., Morton, F. R., Ramage, G., & Burgess, K. E. V. (2016). A novel metabolomic approach used for the comparison of Staphylococcus Aureus planktonic cells and biofilm samples. Metabolomics, 12, 1.
Hess, D. J., Henry-Stanley, M. J., Lusczek, E. R., Beilman, G. J., & Wells, C. L. (2013). Anoxia inhibits biofilm development and modulates antibiotic activity. The Journal of Surgical Research, 184, 488–494.
Guiton, P. S., Cusumano, C. K., Kline, K. A., Dodson, K. W., Han, Z., Janetka, J. W., Henderson, J. P., Caparon, M. G., & Hultgren, S. J. (2012). Combinatorial small-molecule therapy prevents uropathogenic Escherichia Coli catheter-associated urinary tract infections in mice. Antimicrobial Agents Chemotherapy, 56, 4738–4745.
Zampieri, M., Zimmermann, M., Claassen, M., & Sauer, U. (2017). Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations. Cell Reports, 19, 1214–1228.
Rees, C. A., Smolinska, A., & Hill, J. E. (2016). The volatile metabolome of Klebsiella Pneumoniae in human blood. Journal of Breath Research, 10, 27101.
Li, H., Xia, X., Li, X., Naren, G., Fu, Q., Wang, Y., Wu, C., Ding, S., Zhang, S., Jiang, H., Li, J., & Shen, J. (2014). Untargeted metabolomic profiling of amphenicol-resistant campylobacter jejuni by ultra-high-performance liquid chromatography-mass spectrometry. Journal of Proteome Research, 14, 1060–1068.
Aminov, R. (2017). History of antimicrobial drug discovery: Major classes and health impact. Biochemical Pharmacology, 133, 4–19.
Dodds, D. R. (2017). Antibiotic resistance: a current epilogue. Biochemical Pharmacology, 134, 139–146.
de la Fuente-Nunez, C., Torres, M. D., Mojica, F. J., & Lu, T. K. (2017). Next-generation precision antimicrobials: Towards personalized treatment of infectious diseases. Current Opinion in Microbiology, 37, 95–102.
Vincent, I. M., & Barrett, M. P. (2015). Metabolomic-based strategies for anti-parasite drug discovery. Journal of Biomolecular Screening, 20, 44–55.
Yoshikawa, T. T. (2002). Antimicrobial resistance and aging: Beginning of the end of the antibiotic era? Journal of the American Geriatrics Society, 50, S226–S229.
Sajjan, U. S., Tran, L. T., Sole, N., Rovaldi, C., Akiyama, A., Friden, P. M., Forstner, J. F., & Rothstein, D. M. (2001). P-113d, an antimicrobial peptide active against Pseudomonas Aeruginosa, retains activity in the presence of sputum from cystic fibrosis patients. Antimicrobial Agents and Chemotherapy, 45, 3437–3444.
Paton, A. W., Morona, R., & Paton, J. C. (2012). Bioengineered microbes in disease therapy. Trends in Molecular Medicine, 18, 417–425.
Duan, F., & March, J. C. (2010). Engineered bacterial communication prevents vibrio cholerae virulence in an infant mouse model. Proceedings of the National Academy of Sciences, 107, 11260–11264.
Hamblin, M. R., & Hasan, T. (2004). Photodynamic therapy: A new antimicrobial approach to infectious disease? Photochemical & Photobiological Sciences, 3, 436–450.
Friedberg, J. S., Skema, C., Baum, E. D., Burdick, J., Vinogradov, S. A., Wilson, D. F., Horan, A. D., & Nachamkin, I. (2001). In vitro effects of photodynamic therapy on Aspergillus Fumigatus. Journal of Antimicrobial Chemotherapy, 48, 105–107.
Grellier, P., Santus, R., Mouray, E., Agmon, V., Maziere, J. C., Rigomier, D., Dagan, A., Gatt, S., & Schrevel, J. (1997). Photosensitized inactivation of plasmodium falciparum- and babesia divergens-infected erythrocytes in whole blood by lipophilic pheophorbide derivatives. Vox Sanguinis, 72, 211–220.
Cerveny, K. E., DePaola, A., Duckworth, D. H., & Gulig, P. A. (2002). Phage therapy of local and systemic disease caused by vibrio vulnificus in iron-dextran-treated mice. Infection and Immunity, 70, 6251–6262.
Yan, L., Nie, W., Parker, T., Upton, Z., & Lu, H. (2013). MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts. Future Medicinal Chemistry, 5, 1953–1965.
Acknowledgments
This work was supported by the National Key R&D Program of China (No. 2017YFC1308600 and 2017YFC1308605), the National Natural Science Foundation of China Grants (No. 81274175 and 31670031), the Startup Funding for Specialized Professorship Provided by Shanghai Jiao Tong University (No. WF220441502), and the Fundamental Research Funds for the Central Universities (grant no. 106112015CDJZR468808).
Declarations of Interest
None.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Guo, R., Luo, X., Xin, X., Liu, L., Wang, X., Lu, H. (2021). Microbial Metabolomics: From Methods to Translational Applications. In: Hu, S. (eds) Cancer Metabolomics. Advances in Experimental Medicine and Biology, vol 1280. Springer, Cham. https://doi.org/10.1007/978-3-030-51652-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-51652-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51651-2
Online ISBN: 978-3-030-51652-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)