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• W2879078868 abstract "Meta-omic analysis has produced an immense amount of knowledge on the composition and development of human and animal microbiomes, yet most functions encoded in the metagenome are unknown. Stable-isotope probing (SIP) is a powerful strategy for elucidating the ecophysiology of individual microorganisms in complex host-associated microbiotas. An array of SIP approaches has been developed that allow testing of hypotheses from meta-omic datasets and discovery of new functions. SIP is being used to determine the nutrient-utilization strategies of microbes in vivo and ex vivo as well as ecological relationships such as competition, cooperation, and cross-feeding. Humans and animals host diverse communities of microorganisms important to their physiology and health. Despite extensive sequencing-based characterization of host-associated microbiomes, there remains a dramatic lack of understanding of microbial functions. Stable-isotope probing (SIP) is a powerful strategy to elucidate the ecophysiology of microorganisms in complex host-associated microbiotas. Here, we suggest that SIP methodologies should be more frequently exploited as part of a holistic functional microbiomics approach. We provide examples of how SIP has been used to study host-associated microbes in vivo and ex vivo. We highlight recent developments in SIP technologies and discuss future directions that will facilitate deeper insights into the function of human and animal microbiomes. Humans and animals host diverse communities of microorganisms important to their physiology and health. Despite extensive sequencing-based characterization of host-associated microbiomes, there remains a dramatic lack of understanding of microbial functions. Stable-isotope probing (SIP) is a powerful strategy to elucidate the ecophysiology of microorganisms in complex host-associated microbiotas. Here, we suggest that SIP methodologies should be more frequently exploited as part of a holistic functional microbiomics approach. We provide examples of how SIP has been used to study host-associated microbes in vivo and ex vivo. We highlight recent developments in SIP technologies and discuss future directions that will facilitate deeper insights into the function of human and animal microbiomes. Humans and other animals live in an intimate relationship with their microbiota – a community of trillions of single-celled organisms, most of which reside in the intestinal tract. This symbiosis is characterized by bidirectional interactions between host and microbe. Genetics, lifestyle, and health of the host (e.g., diet, living environment, social behavior, infection, medication) determine the presence and dynamics of ecological niches in different body habitats that microorganisms can occupy for growth. Besides their important role in shaping and modulating the immune system, it is through their metabolic functions that symbiotic microorganisms impact the physiology and health of their host. Microorganisms have, or can rapidly acquire or evolve, the necessary enzymes for metabolizing virtually any substrate – be it a dietary, a host-derived or a xenobiotic compound [1Hehemann J.-H. et al.Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota.Nature. 2010; 464: 908-912Crossref PubMed Scopus (723) Google Scholar]. They remove, create new, and modify metabolites and thereby shape the chemical landscape of their habitat by altering nutrient composition, bioactive compounds, and signaling molecules for other microorganisms and their host [2Geller L.T. et al.Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine.Science. 2017; 357: 1156-1160Crossref PubMed Scopus (699) Google Scholar]. Recent developments in DNA/RNA sequencing and bioinformatics now allow for strain-resolved metagenomic and metatranscriptomic analyses [3Turaev D. Rattei T. High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved.Curr. Opin. Biotechnol. 2016; 39: 174-181Crossref PubMed Scopus (23) Google Scholar, 4Morowitz M.J. et al.Strain-resolved community genomic analysis of gut microbial colonization in a premature infant.Proc. Natl. Acad. Sci. U. S. A. 2011; 108: 1128-1133Crossref PubMed Scopus (159) Google Scholar, 5Li S.S. et al.Durable coexistence of donor and recipient strains after fecal microbiota transplantation.Science. 2016; 352: 586-589Crossref PubMed Scopus (332) Google Scholar]. This promises unprecedented insights into the diversity and evolution of the microbiome, including differences between individuals and social groups, lifetime dynamics, acquisition-loss-dispersal events, and associations between individual strains with specific host phenotypes or diseases [6Mallick H. et al.Experimental design and quantitative analysis of microbial community multiomics.Genome Biol. 2017; 18: 228Crossref PubMed Scopus (96) Google Scholar]. However, our interpretation of microbial metabolism from sequencing data alone remains remarkably limited because the functions of most microbial genes are unknown [7Heintz-Buschart A. Wilmes P. Human gut microbiome: function matters.Trends Microbiol. 2017; 26: 563-574Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar, 8Stecher B. et al.Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle.FEMS Microbiol. Rev. 2013; 37: 793-829Crossref PubMed Scopus (77) Google Scholar]. Only between 30 and 60% of the genes in the expanding human microbiome gene catalog can be assigned to known orthologous groups [7Heintz-Buschart A. Wilmes P. Human gut microbiome: function matters.Trends Microbiol. 2017; 26: 563-574Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar, 9Li J. et al.An integrated catalog of reference genes in the human gut microbiome.Nat. Biotechnol. 2014; 32: 834-841Crossref PubMed Scopus (1145) Google Scholar], and only a minute proportion of these genes has been characterized biochemically [10Craciun S. Balskus E.P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 21307-21312Crossref PubMed Scopus (437) Google Scholar, 11Denger K. et al.Sulphoglycolysis in Escherichia coli K-12 closes a gap in the biogeochemical sulphur cycle.Nature. 2014; 507: 114-117Crossref PubMed Scopus (75) Google Scholar]. Even the population of Escherichia coli strains, the best-studied microbial model species, still contains thousands of genes with unknown functions [12Yu G. Stoltzfus A. Population diversity of ORFan genes in Escherichia coli.Genome Biol. Evol. 2012; 4: 1176-1187Crossref PubMed Scopus (17) Google Scholar]. Only a small fraction of an individual microorganism’s genetic repertoire is transcribed in a fecal sample [13Abu-Ali G.S. et al.Metatranscriptome of human faecal microbial communities in a cohort of adult men.Nat. Microbiol. 2018; 3: 356-366Crossref PubMed Scopus (101) Google Scholar]. This dramatic lack of understanding of microbial functions calls for integration of available technologies such as omics approaches, use of defined experimental systems (e.g., in vitro gut bioreactors, gnotobiotic animals), and human intervention studies [7Heintz-Buschart A. Wilmes P. Human gut microbiome: function matters.Trends Microbiol. 2017; 26: 563-574Abstract Full Text Full Text PDF PubMed Scopus (288) Google Scholar, 8Stecher B. et al.Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle.FEMS Microbiol. Rev. 2013; 37: 793-829Crossref PubMed Scopus (77) Google Scholar]. Here, we suggest that stable-isotope probing (SIP) methodologies should be more frequently exploited in such a holistic, functional microbiomics approach [14de Graaf A.A. Venema K. Gaining insight into microbial physiology in the large intestine: a special role for stable isotopes.Adv. Microb. Physiol. 2008; 53: 73-168Crossref PubMed Scopus (44) Google Scholar]. Stable isotopes have been used for decades in applied human nutrition [15Walczyk T. et al.Stable isotope techniques in human nutrition research: concerted action is needed.Food Nutr. Bull. 2002; 23: 69-75Crossref PubMed Google Scholar], yet still rarely in microbiome research [15Walczyk T. et al.Stable isotope techniques in human nutrition research: concerted action is needed.Food Nutr. Bull. 2002; 23: 69-75Crossref PubMed Google Scholar, 16Butler R.N. et al.Stable isotope techniques for the assessment of host and microbiota response during gastrointestinal dysfunction.J. Pediatr. Gastroenterol. Nutr. 2017; 64: 8-14Crossref PubMed Scopus (10) Google Scholar]. Specific physiological capabilities of microbiota members can be studied in situ by SIP without prior knowledge of the biochemical and genetic basis of the involved enzymatic machinery [17Egert M. et al.Beyond diversity: functional microbiomics of the human colon.Trends Microbiol. 2006; 14: 86-91Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar]. Additionally, the metabolome contains many uncharacterized metabolites, and SIP also offers an opportunity to trace active metabolic production even without knowing the identity of the produced metabolites. SIP involves the administration of an isotopically labelled substrate, followed by tracing the fate of the isotope label through the host’s body and the microbiota. Microorganisms that have incorporated the isotope label in their biomass are identified by molecular biology methods or by single-cell imaging. The physiology under investigation is determined by substrate type, isotope label (18O, 13C, 15N, 2H), dosage, and mode of application (ingestion or endoscopy; orally, rectally, intravenously), as well as the physiological and health status of the host (Figure 1). The microbiota is typically thought of in taxonomic and genetic terms – that is, its phylogenetic/taxonomic composition, or the genetic content of the microbiome – and how it changes under different conditions, such as with diet shift or in different disease states. However, the microbiota–host symbiosis can also be conceptualized as a mass balance of elements (Figure 2). There are flows of macroelements, such as C, H, O, N, S, and P, as well as microelements, such as Fe, Zn, and Mg, into and out of the many cells of the microbiota as well as between the microbial ecosystem and its host, analogous to global biogeochemical cycles such as the carbon or nitrogen cycles. These elements provide the basis for molecules that have important structural, metabolic, and immunogenic properties. Both microorganisms and macroorganisms are generally constrained homeostatically by a certain ratio, or stoichiometry, of these elements, and therefore an elemental view of the symbiosis can be very powerful in thinking about growth limitations and system efficiency and function. A recent example of considering the importance of thinking about mass balances is the discovery that microbial load (i.e., overall microbial mass) is a determinant of enterotype status, a concept that had been established by analysis of the relative taxonomic composition of the gut microbiota [18Vandeputte D. et al.Quantitative microbiome profiling links gut community variation to microbial load.Nature. 2017; 551: 507-511Crossref PubMed Scopus (491) Google Scholar]. Taking account of mass and mass balances will be an important step to better understand the symbiosis between the microbiota and host. Stable isotopes are a powerful tool to quantify these mass balances and fluxes (techniques to do so are reviewed in [14de Graaf A.A. Venema K. Gaining insight into microbial physiology in the large intestine: a special role for stable isotopes.Adv. Microb. Physiol. 2008; 53: 73-168Crossref PubMed Scopus (44) Google Scholar]). Diet is the source of new elements that are introduced into the host–microbiome system. In the upper gastrointestinal (GI) tract, the host secretes digestive enzymes to break down and absorb dietary components. As the rest of the dietary intake moves down the GI tract, the intestinal microbiota catalyzes the degradation of dietary compounds resistant to digestion by the host – such as cellulose, hemicellulose, xylan, and pectin [19Flint H.J. et al.Microbial degradation of complex carbohydrates in the gut.Gut Microbes. 2012; 3: 289-306Crossref PubMed Scopus (1144) Google Scholar]. The microbiota itself is likely a major sink for dietary macroelements and microelements as it needs to continuously renew itself. Dying microbial cells and exopolysaccharides, such as those produced by bifidobacteria and lactobacilli, can provide a source of nutrients for growing cells [20Salazar N. et al.Exopolysaccharides produced by intestinal Bifidobacterium strains act as fermentable substrates for human intestinal bacteria.Appl. Environ. Microbiol. 2008; 74: 4737-4745Crossref PubMed Scopus (160) Google Scholar], although it is not yet known how extensive the turnover of microbial biomass and nutrient recycling is in vivo. There are also mass flows from the host to the microbiota in the form of secreted compounds via mucus such as mucin glycoproteins [21Berry D. et al.Host-compound foraging by intestinal microbiota revealed by single-cell stable isotope probing.Proc. Natl. Acad. Sci. U. S. A. 2013; 110: 4720-4725Crossref PubMed Scopus (158) Google Scholar], sloughed epithelial cell components such as ethanolamine [22Thiennimitr P. et al.Intestinal inflammation allows Salmonella to use ethanolamine to compete with the microbiota.Proc. Natl. Acad. Sci. U. S. A. 2011; 108: 17480-17485Crossref PubMed Scopus (432) Google Scholar], as well as secretions from the bile duct like primary bile acids [23Devkota S. et al.Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10−/− mice.Nature. 2012; 487: 104-108Crossref PubMed Scopus (1222) Google Scholar]. The microbiota not only regenerates its own biomass but, from fermentation and anaerobic respiration of diet- and host-derived compounds, also produces compounds such as short-chain fatty acids (SCFAs), trimethylamine, and hydrogen sulfide that are absorbed by the host. The vast majority of metabolites in humans are uncharacterized, and it is likely that many of these are of microbial origin. These compounds can have important epigenetic and regulatory effects on the host [24Banerjee R. Hydrogen sulfide: redox metabolism and signaling.Antioxid. Redox Signal. 2011; 15: 339-341Crossref PubMed Scopus (24) Google Scholar, 25Kabil O. et al.Sulfur as a signaling nutrient through hydrogen sulfide.Annu. Rev. Nutr. 2014; 34: 171-205Crossref PubMed Scopus (85) Google Scholar] and are also often important substrates for host cell metabolism [26Kabil O. Banerjee R. Redox biochemistry of hydrogen sulfide.J. Biol. Chem. 2010; 285: 21903-21907Crossref PubMed Scopus (593) Google Scholar]. SCFAs are particularly abundant microbial products in the gut. They interact with G-protein-coupled-receptors and signal to the host. Butyrate is used by colonocytes for energy generation, propionate and succinate are substrates for gluconeogenesis, and acetate is used for lipogenesis in the liver. The flux of microbial products contributes to a major portion of nutrition/energy to ruminant animals and is estimated to contribute about 10% to the human energy balance [27Bergman E.N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species.Physiol. Rev. 1990; 70: 567-590Crossref PubMed Scopus (1677) Google Scholar], though it is not known how this is altered by microbial characteristics (composition, activity, and efficiency) and diet. Additionally, the host may be able to alter the microbiota by depriving it of nutrients such as iron and zinc [28Moschen A.R. et al.Lipocalin 2 protects from inflammation and tumorigenesis associated with gut microbiota alterations.Cell Host Microbe. 2016; 19: 455-469Abstract Full Text Full Text PDF PubMed Scopus (173) Google Scholar] or regulating the composition or amount of secretions that are used as nutrient sources for the microbiota [29Rausch P. et al.Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype.Proc. Natl. Acad. Sci. U. S. A. 2011; 108: 19030-19035Crossref PubMed Scopus (252) Google Scholar]. The principles of ecological stoichiometry can be applied to understand and quantify processes. The use of isotopes as tracers of these flows is vital to applying this view. Isotope tracers have been used extensively in environmental research to study microbial ecophysiology and nutrient cycling [30Hernández M. et al.Enhancing functional metagenomics of complex microbial communities using stable isotopes.in: Charles T.C. Functional Metagenomics: Tools and Applications. Springer, 2017: 139-150Crossref Scopus (4) Google Scholar]. Isotopes also have a long history in human physiological research, for example for the estimation of protein synthesis rates, metabolite flux, and nutrient metabolism [31Schoeller D.A. Uses of stable isotopes in the assessment of nutrient status and metabolism.Food Nutr. Bull. 2002; 23: 17-20Crossref PubMed Google Scholar, 32Fuller M.F. Tomé D. In vivo determination of amino acid bioavailability in humans and model animals.J. AOAC Int. 2005; 88: 923-934Crossref PubMed Scopus (59) Google Scholar, 33Dotz V. et al.13C-labeled oligosaccharides in breastfed infants’ urine: Individual-, structure- and time-dependent differences in the excretion.Glycobiology. 2013; 24: 185-194Crossref PubMed Scopus (43) Google Scholar, 34Koeth R.A. et al.Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis.Nat. Med. 2013; 19: 576-585Crossref PubMed Scopus (2706) Google Scholar]. Isotope tracer tests have been used to monitor gastrointestinal function, including noninvasive breath tests such as the 13C urea breath test for diagnosing Helicobacter pylori infections as well as tests for orocecal transit time and small intestinal malabsorption [16Butler R.N. et al.Stable isotope techniques for the assessment of host and microbiota response during gastrointestinal dysfunction.J. Pediatr. Gastroenterol. Nutr. 2017; 64: 8-14Crossref PubMed Scopus (10) Google Scholar]. However, these studies largely ignore the microbiota or treat it as a black box. Exceptions where stoichiometry and nutrient flux have been used within studies on the microbiota are reviewed below. SIP is based on the assimilation of stable isotopes (most commonly 13C, 15N, 18O, and 2H) into newly-synthesized microbial biomass and the subsequent detection of isotopes in bulk biomass, single cells, or cellular components using specialized instrumentation. This is often combined with molecular techniques such as fluorescence in situ hybridization (FISH) or sequencing-based approaches such as amplicon sequencing of marker genes and metagenomics to identify taxa that have incorporated the isotope (reviewed in [15Walczyk T. et al.Stable isotope techniques in human nutrition research: concerted action is needed.Food Nutr. Bull. 2002; 23: 69-75Crossref PubMed Google Scholar, 16Butler R.N. et al.Stable isotope techniques for the assessment of host and microbiota response during gastrointestinal dysfunction.J. Pediatr. Gastroenterol. Nutr. 2017; 64: 8-14Crossref PubMed Scopus (10) Google Scholar, 35Orphan V.J. Methods for unveiling cryptic microbial partnerships in nature.Curr. Opin. Microbiol. 2009; 12: 231-237Crossref PubMed Scopus (62) Google Scholar, 36Coyotzi S. et al.Targeted metagenomics of active microbial populations with stable-isotope probing.Curr. Opin. Biotechnol. 2016; 41: 1-8Crossref PubMed Scopus (42) Google Scholar, 37Wang Y. et al.Single cell stable isotope probing in microbiology using Raman microspectroscopy.Curr. Opin. Biotechnol. 2016; 41: 34-42Crossref PubMed Scopus (126) Google Scholar]). A holistic SIP experiment would ideally combine different methodologies in a systems biology approach (Figure 3). However, SIP methods are time-consuming and often require expensive, high-end instrumentation, which severely limits the number of samples that can be analyzed and thus requires careful experimental planning. Most SIP studies have focused on microbial utilization of specific, fully isotopically labeled compounds (substrate-mediated SIP). However, it is also possible to use compounds as general activity markers such as heavy water (2H2O), 13CO2, or total 15N-labeling, as active cells will incorporate hydrogen from water, 13C-carbon during autotrophic or heterotrophic CO2 assimilation, or 15N-nitrogen from a completely 15N-labeled diet into newly-synthesized biomass, irrespective of their physiology [38Berry D. et al.Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: E194-E203Crossref PubMed Scopus (261) Google Scholar, 39Oberbach A. et al.Metabolic in vivo labeling highlights differences of metabolically active microbes from the mucosal gastrointestinal microbiome between high-fat and normal chow diet.J. Proteome Res. 2017; 16: 1593-1604Crossref PubMed Scopus (22) Google Scholar]. In DNA- and RNA-SIP, microbial populations that have assimilated stable isotopes (typically 13C or 15N) are identified by separating nucleic acids into unlabeled and labeled fractions (also termed ‘light’ and ‘heavy’ fractions) by isopycnic ultracentrifugation due to their different densities. The fractions can then be analyzed by sequencing-based approaches to reveal the phylogenetic and functional gene composition or to reconstruct the genomes of individual microbial populations from the metagenomes. Alternatively, nucleic acids can be probed by hybridizing them to specially designed DNA microarrays, and isotope composition of individual probe spots can be quantified using spatially resolved elemental analysis [40Mayali X. et al.High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use.ISME J. 2012; 6: 1210-1221Crossref PubMed Scopus (52) Google Scholar]. In phospholipid-derived fatty acid-(PLFA)-SIP, PLFAs are separated by gas chromatography, and isotopes are measured with isotope ratio mass spectrometry [41Neufeld J.D. et al.Methodological considerations for the use of stable isotope probing in microbial ecology.Microb. Ecol. 2006; 53: 435-442Crossref PubMed Scopus (177) Google Scholar]. This method is extremely sensitive and can measure low enrichments in stable isotopes, but has a limited taxonomic resolution as many fatty acids are conserved among microbes. Protein-SIP evaluates stable isotope incorporation into newly-synthesized proteins by mass spectrometry [42Seifert J. et al.Protein-based stable isotope probing (protein-SIP) in functional metaproteomics.Mass Spectrom. Rev. 2012; 31: 683-697Crossref PubMed Scopus (58) Google Scholar]. As it is dependent on peptide identification, it is critical to have a comprehensive reference database (such as from metagenome sequencing) of the community that is being probed. Though targeted metabolite analysis is frequently performed in combination with SIP [21Berry D. et al.Host-compound foraging by intestinal microbiota revealed by single-cell stable isotope probing.Proc. Natl. Acad. Sci. U. S. A. 2013; 110: 4720-4725Crossref PubMed Scopus (158) Google Scholar], there are thus far few examples of combining SIP with untargeted metabolomics [43Wissenbach D.K. et al.Optimization of metabolomics of defined in vitro gut microbial ecosystems.Int. J. Med. Microbiol. 2016; 306: 280-289Crossref PubMed Scopus (20) Google Scholar], and this is an exciting area for future development. More recently, SIP approaches have been developed using two single-cell technologies: Raman microspectroscopy and nano-scale resolution secondary ion mass spectrometry (NanoSIMS) [8Stecher B. et al.Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle.FEMS Microbiol. Rev. 2013; 37: 793-829Crossref PubMed Scopus (77) Google Scholar, 44Lorenz B. et al.Cultivation-free Raman spectroscopic investigations of bacteria.Trends Microbiol. 2017; 25: 413-424Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar, 45Wagner M. Single-Cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging.Annu. Rev. Microbiol. 2009; 63: 411-429Crossref PubMed Scopus (206) Google Scholar] Raman microspectroscopy produces a chemical fingerprint of single cells based on the inelastic scattering of photons from molecular bonds [46Harrison J.P. Berry D. Vibrational spectroscopy for imaging single microbial cells in complex biological samples.Front. Microbiol. 2017; 8: 675Crossref PubMed Scopus (37) Google Scholar]. The resulting fingerprint is a complex spectrum, but 13C and 2H incorporation can be readily discerned by shifts in characteristic peaks in the spectrum. Importantly, as the measurement is nondestructive, cells can be processed postmeasurement, allowing for subsequent sorting and molecular analysis [38Berry D. et al.Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: E194-E203Crossref PubMed Scopus (261) Google Scholar]. In NanoSIMS, a high-energy primary ion beam is used to release atomic and small molecular ions from the cell, and ions are separated and measured by mass spectrometry [45Wagner M. Single-Cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging.Annu. Rev. Microbiol. 2009; 63: 411-429Crossref PubMed Scopus (206) Google Scholar]. Both of these approaches can be combined with FISH to simultaneously identify and quantify the activity of single cells in complex microbial communities. Comprehensive multi-omics analyses are being applied widely [6Mallick H. et al.Experimental design and quantitative analysis of microbial community multiomics.Genome Biol. 2017; 18: 228Crossref PubMed Scopus (96) Google Scholar, 47Franzosa E.A. et al.Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling.Nat. Rev. Microbiol. 2015; 13: 360-372Crossref PubMed Scopus (408) Google Scholar], and many physiological hypotheses are generated from these large, complex datasets. Inclusion of SIP approaches in a study workflow will be an important step forward for testing these hypotheses (Figure 3). SIP has been successfully used to uncover the in situ ecophysiology of a number of clades of intestinal microorganisms using 13C-labeled dietary- and host-derived compounds. By administering 13C-labeled glucose to an in vitro human gut model reactor, Egert et al. demonstrated that Streptococcus bovis and Clostridium perfringens were the most active glucose fermenters using RNA-SIP [48Egert M. et al.Identification of glucose-fermenting bacteria present in an in vitro model of the human intestine by RNA-stable isotope probing.FEMS Microbiol. Ecol. 2007; 60: 126-135Crossref PubMed Scopus (61) Google Scholar]. They then used nuclear magnetic resonance spectroscopy to evaluate the fate of the carbon from the fermented glucose and were able to identify lactate, acetate, butyrate, and formate as the principal fermentation products, which together constituted over 90% of the 13C-carbon balance. Glucose utilization has also been studied in the murine gut microbiota using anaerobic incubations of fecal pellets with 13C-labeled glucose followed by RNA-SIP, with Allobaculum spp. being identified as active glucose utilizers, and with lactate, acetate, and propionate being the principal end-products of glucose fermentation [49Herrmann E. et al.RNA-based stable isotope probing suggests Allobaculum spp. as particularly active glucose assimilators in a complex murine microbiota cultured in vitro.BioMed Res. Int. 2017; 2017: 1-13Crossref Scopus (47) Google Scholar]. 13C-labeled sialic acid, a monosaccharide found in many glycans in the body as well as in milk, was used to identify Prevotella spp. as the most abundant utilizers of sialic acid in the piglet cecal microbiota by in vitro incubation followed by RNA-SIP [50Young W. et al.Detection of sialic acid-utilising bacteria in a caecal community batch culture using RNA-based stable isotope probing.Nutrients. 2015; 7: 2109-2124Crossref PubMed Scopus (19) Google Scholar]. In an in vivo mouse model of total parenteral nutrition, an intravenously administered 13C-labeled leucine tracer was used to demonstrate that members of the Enterobacteriaceae utilize blood-derived amino acids that leak into the gut, which may contribute to their bloom during small intestinal inflammation associated with parenteral nutrition [51Ralls M.W. et al.Bacterial nutrient foraging in a mouse model of enteral nutrient deprivation: insight into the gut origin of sepsis.Am. J. Physiol. Gastrointest. Liver Physiol. 2016; 311: G734-G743Crossref PubMed Scopus (20) Google Scholar]. Using 13C-labeled bicarbonate, several acetogens in the kangaroo forestomach, such as Blautia spp. as well as several taxa not previously reported to be hydrogenotrophic organisms, were identified with RNA-SIP [52Godwin S. et al.Investigation of the microbial metabolism of carbon dioxide and hydrogen in the kangaroo foregut by stable isotope probing.ISME J. 2014; 8: 1855-1865Crossref PubMed Scopus (35) Google Scholar]. In this study, the researchers were also able to link the activity of acetogenic communities with overall methane output, suggesting competition between reductive acetogens and methanogens for hydrogen. Though study of complex carbon utilization using stable isotopes has been limited by the availability of isotope-labeled compounds, a number of studies have been conducted using commercially available substrates as well as by alternative methodologies. Anaerobic in vitro fermentation of murine stool with 13C-labeled potato starch followed by RNA-SIP revealed that members of the Prevotellaceae and Ruminococcaceae were primary assimilators of resistant starch, and that acetate, propionate, and butyrate were among the fermentation products, as determined by high-performance liquid chromatography–isotope ratio mass spectrometry (HPLC–IRMS) analysis [53Herrmann E. et al.Determination of resistant starch assimilating bacteria in fecal samples of mice by in vitro RNA-based stable isotope probing.Front. Microbiol. 2017; 8: 1331Crossref PubMed Scopus (34) Google Scholar]. By administering 13C-labeled starch to an in vitro human gut model reactor, Ruminococcus bromii was identified with RNA-SIP to be an important primary of degrader of starch, and was accompanied by the production of acetate, butyrate, and propionate [54Kovatcheva-Datchary P. et al.Linking phylogenetic identities of bacteria to starch fermentation in an in vitro model of the large intestine by RNA-based stable isotope probing.Environ. Microbiol. 2009; 11: 914-926Crossref PubMed Scopus (128) Google Scholar]. Interestingly, this analysis also indicated that other taxa, such as Prevotella spp., Bifidobacterium adolescentis, and Eubacterium rectale, may be involved in cross-feeding on sugars released from starch degradation or in the downstream trophic chain resulting from R. bromii activity. By amending a human gut model reactor with 13C-labeled galacto-oligosaccharides (GOSs), which are considered to be a prebiotic compound, a number of bifidobacteria as well as lactobacilli were found to be important GOS-assimilators using RNA-SIP [55Maathuis A.J.H. et al.Galacto-oligosaccharides have prebiotic activity in a dynamic in vitro colon model using a 13C-labeling technique.J. Nutr. 2012; 142: 1205-1212Crossref PubMed Scopus (95) Google Scholar]. The authors also observed metabolic cross-feeding. Addition of a 13C-labeled version of another prebiotic compound, inulin, to mouse diet led to the identification of Bacteroides uniformis, Blautia gluceraseae, Clostridium indolis, and Bifidobacterium animalis as major in vivo utilizers of inulin in the mouse cecal community using RNA-SIP [56Tannock G.W. et al.RNA-stable-isotope probing shows utilization of carbon from inulin by specific bacterial populations in the rat large bowel.Appl. Environ. Microbiol. 2014; 80: 2240-2247Crossref PubMed Scopus (31) Google Scholar]. As an alternative to directly adding a labeled complex compound, it is also possible to add simple precursors into the host and allow normal host metabolism to synthesize compounds of interest. For example, using a mouse model with an intravenously-administered 13C/15N-labeled threonine as a tracer for newly-synthesized proteins, Akkermansia muciniphila and Bacteroides acidifaciens were identified as the most abundant in vivo utilizers of secreted host proteins using FISH combined with NanoSIMS [21Berry D. et al.Host-compound foraging by intestinal microbiota revealed by single-cell stable isotope probing.Proc. Natl. Acad. Sci. U. S. A. 2013; 110: 4720-4725Crossref PubMed Scopus (158) Google Scholar]. Interestingly, this activity was partially abrogated in a gnotobiotic mouse model with a simplified community including A. muciniphila and B. acidifaciens, suggesting that their ecophysiology is dependent upon the background microbiota and highlighting the importance of studying microbial ecophysiology in natural, complex communities. Another strategy when isotopically-labeled compounds are not available is to perform incubations with the compound of interest in the presence of a general activity marker such as heavy water. By short in vitro incubation of mouse intestinal contents with the host glycoprotein mucin as a substrate and in the presence of heavy water, A. muciniphila and B. acidifaciens, as well as other members of the Bacteroidetes, were identified as utilizers of mucin by isotope quantification and single-cell sorting with Raman microspectroscopy followed by sequencing of sorted cells [38Berry D. et al.Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: E194-E203Crossref PubMed Scopus (261) Google Scholar]. Kopf et al. used heavy water to estimate the growth rate of Staphylococcus aureus in cystic fibrosis sputum and to demonstrate substantial heterogeneity in growth rates using NanoSIMS imaging of single cells [57Kopf S.H. et al.Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: E110-E116Crossref PubMed Scopus (74) Google Scholar]. General activity has also been monitored in vivo using total 15N-labeled diets as all active microbes will incorporate N for growth. Recently, Oberbach et al. compared the active microbial community in colonic mucus of rats fed either a control diet or a high-fat diet with 15N using metaproteomics SIP and found that Verrucomicrobiaceae and Desulfovibrionaceae were enriched in the active fraction of the community in the high-fat diet [39Oberbach A. et al.Metabolic in vivo labeling highlights differences of metabolically active microbes from the mucosal gastrointestinal microbiome between high-fat and normal chow diet.J. Proteome Res. 2017; 16: 1593-1604Crossref PubMed Scopus (22) Google Scholar]. These examples highlight how key organisms for specific metabolisms of interest can be identified and their metabolic products, as well as their contribution to metabolite pools, can be monitored without the need for prior knowledge of the genetic basis for these metabolisms. The identification of organisms mediating certain metabolisms is an important step for further investigation of these species using biochemical and, when possible, genetic techniques to elucidate the molecular basis of the metabolism. For example, recently 13C-labeled lysine was used in combination with 13C-NMR analysis and high-performance liquid chromatography to elucidate the lysine degradation pathway of Intestinimonas AF211, an abundant member of the human gut microbiota [58Bui T.P.N. et al.Production of butyrate from lysine and the Amadori product fructoselysine by a human gut commensal.Nat. Commun. 2015; 6 (10062)Crossref Scopus (129) Google Scholar]. SIP technologies and methodologies have been rapidly developing, particularly in the area of single-cell analysis, which opens up new possibilities for studying host-associated microbiotas (see Outstanding Questions). A promising new area is the combination of Raman microspectroscopy with high-throughput sorting [59Zhang Q. et al.Towards high-throughput microfluidic Raman-activated cell sorting.Analyst. 2015; 140: 6163-6174Crossref PubMed Google Scholar] to sort hundreds to thousands of labeled cells for molecular characterization or targeted cultivation. This higher throughput will allow a more complete description of the organisms as well as guilds of microbiome members involved in metabolisms of interest. Developments in mass spectrometry-based imaging, such as time-of-flight (TOF) SIMS, 3D OrbiSIMS [60Passarelli M.K. et al.The 3D OrbiSIMS-label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power.Nat. Methods. 2017; (Published online November 2017)https://doi.org/10.1038/nmeth.4504Crossref PubMed Scopus (206) Google Scholar], and MALDI-TOF MS, will also open the door for protein and metabolite SIP imaging in individual microbial cells. These technologies will assist in identifying proteins and metabolites produced by key organisms, which will help to resolve underlying metabolic pathways. An area that should receive further attention is the design of experiments that enable highly resolved quantitative physiological analysis in situ [57Kopf S.H. et al.Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: E110-E116Crossref PubMed Scopus (74) Google Scholar] and include mass balances and stable isotope-aided metabolic flux analysis among the cells in the microbiota and between the microbiota and host [14de Graaf A.A. Venema K. Gaining insight into microbial physiology in the large intestine: a special role for stable isotopes.Adv. Microb. Physiol. 2008; 53: 73-168Crossref PubMed Scopus (44) Google Scholar]. Combined analyses of host and microbiota physiology [61Rauniyar N. et al.Stable isotope labeling of mammals (SILAM) for in vivo quantitative proteomic analysis.Methods. 2013; 61: 260-268Crossref PubMed Scopus (46) Google Scholar, 62Hölper S. et al.Stable isotope labeling for proteomic analysis of tissues in mouse.Methods Mol. Biol. 2014; 1188: 95-106Crossref PubMed Scopus (6) Google Scholar], including simultaneous application of multiple tracers [16Butler R.N. et al.Stable isotope techniques for the assessment of host and microbiota response during gastrointestinal dysfunction.J. Pediatr. Gastroenterol. Nutr. 2017; 64: 8-14Crossref PubMed Scopus (10) Google Scholar, 63Antoniewicz M.R. Using multiple tracers for 13C metabolic flux analysis.Methods Mol. Biol. 2013; 985: 353-365Crossref PubMed Scopus (15) Google Scholar], will help to unravel and quantify the multilayered fluxes of metabolites and elements between the microbiota and its host.Outstanding QuestionsHow can SIP be made more high-throughput for analysis of many samples?Will secondary ion mass spectrometry (SIMS) technologies, such as 3D OrbiSIMS and TOF-SIMS, soon have the spatial resolution and sensitivity required for imaging of isotope-labeled metabolites in individual microbial cells?How can SIP of host and microbiota physiology be better combined to obtain quantitative mass balance and flux data? How can SIP be made more high-throughput for analysis of many samples? Will secondary ion mass spectrometry (SIMS) technologies, such as 3D OrbiSIMS and TOF-SIMS, soon have the spatial resolution and sensitivity required for imaging of isotope-labeled metabolites in individual microbial cells? How can SIP of host and microbiota physiology be better combined to obtain quantitative mass balance and flux data? This work was financially supported by the Austrian Science Fund (FWF; project grants P26127-B20 and I2320-B22) and European Research Council (Starting Grant: FunKeyGut 741623). We thank our research groups and collaborators for helpful discussions, especially Michael Wagner, Arno Schintlmeister, Douglas Morrison, and Wei Huang." @default.
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• W2879078868 title "Stable-Isotope Probing of Human and Animal Microbiome Function" @default.
• W2879078868 cites W1540276165 @default.
• W2879078868 cites W1601800937 @default.
• W2879078868 cites W1820674707 @default.
• W2879078868 cites W1953702613 @default.
• W2879078868 cites W1967764703 @default.
• W2879078868 cites W1971080284 @default.
• W2879078868 cites W1979966476 @default.
• W2879078868 cites W1987055028 @default.
• W2879078868 cites W1995634357 @default.
• W2879078868 cites W1997895029 @default.
• W2879078868 cites W2002360764 @default.
• W2879078868 cites W2002424690 @default.
• W2879078868 cites W2008758799 @default.
• W2879078868 cites W2012653404 @default.
• W2879078868 cites W2015166075 @default.
• W2879078868 cites W2022397401 @default.
• W2879078868 cites W2023566940 @default.
• W2879078868 cites W2024330772 @default.
• W2879078868 cites W2038558765 @default.
• W2879078868 cites W2043900297 @default.
• W2879078868 cites W2055741797 @default.
• W2879078868 cites W2063506573 @default.
• W2879078868 cites W2078599166 @default.
• W2879078868 cites W2082891742 @default.
• W2879078868 cites W2089095343 @default.
• W2879078868 cites W2097124003 @default.
• W2879078868 cites W2099890886 @default.
• W2879078868 cites W2109763742 @default.
• W2879078868 cites W2114089110 @default.
• W2879078868 cites W2120828434 @default.
• W2879078868 cites W2121003339 @default.
• W2879078868 cites W2121923588 @default.
• W2879078868 cites W2133543471 @default.
• W2879078868 cites W2134127753 @default.
• W2879078868 cites W2145228928 @default.
• W2879078868 cites W2151798187 @default.
• W2879078868 cites W2152821364 @default.
• W2879078868 cites W2219454109 @default.
• W2879078868 cites W2294941565 @default.
• W2879078868 cites W2301047578 @default.
• W2879078868 cites W2339478720 @default.
• W2879078868 cites W2344899722 @default.
• W2879078868 cites W2344925875 @default.
• W2879078868 cites W2346886314 @default.
• W2879078868 cites W2401821494 @default.
• W2879078868 cites W2413123072 @default.
• W2879078868 cites W2416704236 @default.
• W2879078868 cites W2417632643 @default.
• W2879078868 cites W2469271927 @default.
• W2879078868 cites W2507424344 @default.
• W2879078868 cites W2519478793 @default.
• W2879078868 cites W2587578821 @default.
• W2879078868 cites W2587895783 @default.
• W2879078868 cites W2591932914 @default.
• W2879078868 cites W2606924981 @default.
• W2879078868 cites W2738379602 @default.
• W2879078868 cites W2756403351 @default.
• W2879078868 cites W2767546077 @default.
• W2879078868 cites W2768511889 @default.
• W2879078868 cites W2769145826 @default.
• W2879078868 cites W2770269406 @default.
• W2879078868 cites W2784053908 @default.
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