Adipositas - Ursachen, Folgeerkrankungen, Therapie 2019; 13(04): 205-213
DOI: 10.1055/a-0966-9652
Review

Chancen und Risiken von Metaproteomik in der Mikrobiomforschung

Opportunities and pitfalls of metaproteomics in microbiome research
Sven-Bastiaan Haange
1   Department Molekulare Systembiologie, Helmholtz-Zentrum für Umweltforschung – UFZ GmbH, Leipzig, Deutschland
2   Institut für Biochemie, Fakultät für Lebenswissenschaften, Universität Leipzig, Leipzig, Deutschland
,
Nico Jehmlich
1   Department Molekulare Systembiologie, Helmholtz-Zentrum für Umweltforschung – UFZ GmbH, Leipzig, Deutschland
,
Thomas Jacobi
3   Integriertes Forschungs- und Behandlungszentrum Adipositas Erkrankungen (IFB- Adiposity Diseases), Leipzig, Deutschland
,
Martin von Bergen
1   Department Molekulare Systembiologie, Helmholtz-Zentrum für Umweltforschung – UFZ GmbH, Leipzig, Deutschland
2   Institut für Biochemie, Fakultät für Lebenswissenschaften, Universität Leipzig, Leipzig, Deutschland
› Author Affiliations

Zusammenfassung

Die Erforschung des menschlichen Darmmikrobioms in den letzten 15 Jahren führte zu neuen Erkenntnissen wodurch Korrelationen und sogar Kausalitäten vieler Erkrankungen mit der Veränderungen der Mikrobiota beschrieben werden konnten. Wegbereitend für die Analyse der Mikrobiota von in vivo Proben war dabei die Entwicklung der sogenannten Meta-omics Methoden, insbesondere die 16 S rRNA Gensequenzierung und die Metagenomik. Jedoch hat die taxonomische Zusammensetzung der Darmmikrobiota aufgrund der hohen funktionellen Redundanz und Vielfältigkeit des Stoffwechselpotentials einzelner Bakterienarten nur geringe Vorhersagekraft bezüglich ihrer tatsächlichen Funktionalität. Aus diesem Grund wurden metaproteomische und metabolische Markierungsmethoden mit stabilen Isotopen (Protein-SIP) entwickelt. Wenn man die Gesamtanzahl der Proteine einer mikrobiellen Gemeinschaft betrachtet, können somit Informationen zur taxonomischen Verteilung, metabolischer Funktion, metabolischer Aktivität sowie für die Funktion der Mikrobiota Schlüsselarten identifiziert werden.

Der Fokus des Artikels liegt auf der Methode Metaproteomik sowie der proteinbasierten Stabil-Isotopen-Markierung (Protein-SIP), weil diese Ansätze funktionelle und taxonomische Ergebnisse vereinen. Zusätzlich werden Chancen aber auch Risiken betrachtet, mit denen die Methoden naturgemäß einhergehen.

Abstract

Over the last 15 years research into the intestinal microbiota has become a growing field. This has led to changes in the microbiota being associated or even causally linked to a number of diseases. Assessment of the microbial composition of in vivo samples has been made possible by the introduction and development of the so called meta-omics methods, especially 16 S rRNA gene profiling and metagenomics. Due to the high functional redundancy and metabolic variety of bacteria the composition has only little predictive power for the actually performed functionality. In order to assess function, metaproteomics and stable isotope probing methods have been developed. By analyzing the complete complement of proteins in the microbiota community, information on taxonomic distribution, metabolic function, metabolic activity and key players shaping the microbiota can be elucidated.

In this article we focus on metaproteomics and protein stable isotope probing (protein-SIP) since these approaches are able to combine functional and taxonomic analysis. In addition, we will also discuss some opportunities, challenges and pitfalls inherently linked to these approaches.



Publication History

Article published online:
02 December 2019

© Georg Thieme Verlag KG
Stuttgart · New York

 
  • Literatur

  • 1 Davis CD. The Gut Microbiome and Its Role in Obesity. Nutrition today 2016; 51: 167-174
  • 2 Schaubeck M, Clavel T, Calasan J. et al. Dysbiotic gut microbiota causes transmissible Crohn’s disease-like ileitis independent of failure in antimicrobial defence. Gut 2016; 65: 225-237
  • 3 Morgan XC, Tickle TL, Sokol H. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome boil 2012; 13: R79
  • 4 Kostic AD, Gevers D, Pedamallu C. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 2012; 22: 292-298
  • 5 Armstrong H, Bording-Jorgensen M, Dijk S. et al. The Complex Interplay between Chronic Inflammation, the Microbiome, and Cancer: Understanding Disease Progression and What We Can Do to Prevent It. Cancers 2018; 10: pii:E83
  • 6 Arrieta MC, Stiemsma LT, Dimitriu PA. et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med 2015; 7: 307ra152
  • 7 Lynch SV, Boushey HA. The microbiome and development of allergic disease. Curr Opin Allergy Clin immunol 2016; 16: 165-171
  • 8 Qin J, Li Y, Cai Z. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 55-60
  • 9 Yoshimoto S, Loo TM, Atarshi K. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 2013; 499: 97-101
  • 10 Bajaj JS, Ridlon JM, Hylemon PB. et al. Linkage of gut microbiome with cognition in hepatic encephalopathy. Am J Physiol Gastrointest Liver Physiol 2012; 302: G168-75
  • 11 Turnbaugh PJ, Ley RE, Mahowald MA. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2005; 444: 1027-1031
  • 12 Hillman ET, Lu H, Yao T. et al. Microbial Ecology along the Gastrointestinal Tract. Microbes Environ 2017; 32: 300-313
  • 13 Muir P, Li S, Lou S. et al. The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol 2016; 17: 53
  • 14 Reigstad CS, Kashyap PC. Beyond phylotyping: understanding the impact of gut microbiota on host biology. Neurogastroenterol Motil 2013; 25: 358-372
  • 15 Rodríguez E, García-Encina PA, Stams AJM. et al. Meta-omics approaches to understand and improve wastewater treatment systems. Rev Environ Sci Biotechnol 2015; 14: 385-406
  • 16 Rojo D, Méndez-Garcia C, Raczkowska BA. et al. Exploring the human microbiome from multiple perspectives: factors altering its composition and function. FEMS Microbiol Rev 2017; 41: 453-478
  • 17 Heintz-Buschart A, Wilmes P. Human Gut Microbiome: Function Matters. Trends Microbiol 2018; 26: 563-574
  • 18 Lozupone CA, Stombaugh JI, Gordon JI. et al. Diversity, stability and resilience of the human gut microbiota. Nature 2012; 489: 220-230
  • 19 Hettich RL, Pan C, Chourey K. et al. Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem 2013; 85: 4203-4214
  • 20 Karpievitch YV, Polpitiya AD, Anderson GA. et al. Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects. Ann Appl Stat 2010; 4: 1797-1823
  • 21 Donaldson GP, Lee SM, Mazmanian SK. Gut biogeography of the bacterial microbiota. Nat Rev Microbiol 2016; 14: 20-32
  • 22 Li H, Limenitakis JP, Fuhrer T. et al. The outer mucus layer hosts a distinct intestinal microbial niche. Nat Commun 2015; 6: 8292
  • 23 Haange SB, Oberbach A, Schlichting N. et al. Metaproteome analysis and molecular genetics of rat intestinal microbiota reveals section and localization resolved species distribution and enzymatic functionalities. J Proteome Res 2012; 11: 5406-5417
  • 24 Thatcher SA. DNA/RNA preparation for molecular detection. Clin Chem 2015; 61: 89-99
  • 25 Sabidó Selevsek N, Aebersold R. Mass spectrometry-based proteomics for systems biology. Curr Opin Biotechnol 2012; 23: 591-597
  • 26 Deutsch EW, Lam H, Aebersold R. Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics. Physiol Genomics 2008; 33: 18-25
  • 27 Perkins DN, Pappin DJ, Creasy DM. et al. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999; 20: 3551-3567
  • 28 Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 1994; 5: 976-989
  • 29 Geer LY, Markey SP, Kowalek JA. et al. Open mass spectrometry search algorithm. J Proteome Res 2004; 3: 958-964
  • 30 Cox J, Neuhause N, Michalski A. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 2011; 10: 1794-1805
  • 31 Elias JE, Gygi SP. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol Biol 2010; 604: 55-71
  • 32 Gupta N, Pevzner PA. False discovery rates of protein identifications: a strike against the two-peptide rule. J Proteome Res 2009; 8: 4173-4181
  • 33 Muth T, Behne A, Heyer R. et al. The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation. J Proteome Res 2015; 14: 1557-1565
  • 34 Seifert J, Herbst FA, Halkjaer Nielsen P. et al. Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics 2013; 13: 2786-2804
  • 35 Blackburn JM, Martens L. The challenge of metaproteomic analysis in human samples. Expert review of proteomics 2016; 13: 135-138
  • 36 Tanca A, Palomba A, Fraumene C. et al. The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome 2016; 4: 51
  • 37 Kleiner M, Thorson E, Sharp CE. et al. Assessing species biomass contributions in microbial communities via metaproteomics. Nat Commun 2017; 8: 1558
  • 38 Verberkmoes NC, Russell AL, Shah M. et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J 2009; 3: 179-189
  • 39 Hecker M, Antelmann H, Büttner K. et al. Gel-based proteomics of Gram-positive bacteria: a powerful tool to address physiological questions. Proteomics 2008; 8: 4958-4975
  • 40 Starosta AL, Lassak J, Jung K. et al. The bacterial translation stress response. FEMS Microbiol Rev 2014; 38: 1172-1201
  • 41 Bergen M von, Jehmlich N, Taubert M. et al. Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology. ISME J 2013; 7: 1877-1885
  • 42 Taubert M, Jehmlich N, Vogt C. et al. Time resolved protein-based stable isotope probing (Protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteomics 2011; 11: 2265-2274
  • 43 Jehmlich N, Vogt C, Lünsmann V. et al. Protein-SIP in environmental studies. Curr Opin Biotechnol 2016; 41: 26-33
  • 44 Sachsenberg T, Herbst FA Taubert M. et al. MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res 2015; 14: 619-627
  • 45 Seifert J, Taubert M, Jehmlich M. et al. Protein-based stable isotope probing (protein-SIP) in functional metaproteomics. Mass Spectrom Rev 2012; 31: 683-697
  • 46 Maurice CF, Haiser HJ, Turnbaugh PJ. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 2013; 152: 39-50
  • 47 Hecker M, Reder A, Fuchs S. et al. Physiological proteomics and stress/starvation responses in Bacillus subtilis and Staphylococcus aureus. Res Microbiol 2009; 160: 245-258
  • 48 Haange SB, Jehmlich N. Proteomic interrogation of the gut microbiota: potential clinical impact. Expert Rev Proteomics 2016; 13: 535-537
  • 49 Aguiar-Pulido V, Huang W, Suarez-Ulloa V. et al. Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis. Evol Bioinform Online 2016; 12: 5-16