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The impact of nutrition on intestinal bacterial communities
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ScienceDirect The impact of nutrition on intestinal bacterial communities Harry J Flint, Sylvia H Duncan and Petra Louis What we eat influences the species composition of our gut microbiota. This is not only because diet composition determines the supply of substrates for microbial growth (in the form of dietary residue, mainly fibre, that reaches the large intestine) but also because of impacts on gut transit and the gut environment. In turn the metabolic activities of the gut microbiota, which have important health consequences, are influenced by diet and diet-driven changes in microbiota composition. Better understanding of the metabolic capabilities and host-interactions of dominant members of the gut microbiota will aid our ability to improve human health through diet. Address Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, UK Corresponding author: Flint, Harry J ([email protected])
Current Opinion in Microbiology 2017, 38:59–65 This review comes from a themed issue on Microbiota Edited by Franc¸ois Leulier and Maria Elena
http://dx.doi.org/10.1016/j.mib.2017.04.005 1369-5274/ã 2017 Elsevier Ltd. All rights reserved.
Introduction Most surveys of gut microbiota composition are based on high throughput analysis of nucleic acid sequences from faecal samples. Within human populations these reveal compositional differences with age and development, including between pre-weaned infants, adults and the frail elderly [1,2]. While host factors, especially immune function and the gut environment, can be important, diet is increasingly offered as the explanation for many of these differences. Thus the supply of human milk oligosaccharides drives the Bifidobacterium-dominated microbiota of breast-fed infants [3], while limited dietary intake that is low in fibre may explain changes seen in the frail elderly [1]. Marked differences are also reported between communities separated by geography and lifestyle, for example between industrialised European or North American cohorts and rural Africans or South Americans [2,4,5]. A multiplicity of factors may be relevant, including genotypes, antibiotic use and inocula www.sciencedirect.com
from the environment [6] but differences in dietary intake are frequently offered as an explanation for such variation in gut microbiota composition. This makes it important therefore to consider what direct evidence exists for dietary modulation of microbiota composition.
Impact of diet on microbiota composition While observational studies can provide correlations, provided that accurate data on nutritional intake are available, it should be recognized that they cannot provide proof that diet is the cause of shifts in microbiota composition. For this we need to consider studies involving gut microbiota profiling in which the human diet has been deliberately altered. Such studies vary in the degree of dietary control, with some relying only on dietary recommendation and some providing defined supplements to volunteers following their habitual diets. Supplementation studies have been used in particular to obtain evidence for the impact of prebiotics upon the gut microbiota and have often studied selected groups of ‘target’ bacteria, notably bifidobacteria [7] although more comprehensive surveys have revealed changes in multiple groups [8–10]. Only a few studies have controlled complete dietary intake over a period of time in human volunteers, thus minimizing the influence of background variations in dietary intake [11–13,14]. Walker et al. [11] used a cross-over design in which volunteers switched between diets with the same protein, fat and carbohydrate proportions, but with either resistant starch (RS) or wheat-bran as the main dietary source of non-digestible (ND) carbohydrate. This study identified different species that became strongly enriched within the community either by the RS or by the wheat bran diet [11,15]. These responses occurred rapidly (within 2–3 days) and were found to be reversed following a subsequent dietary switch. Interestingly, it was also noted that overall microbiota composition was more strongly influenced by the individual volunteer than by the diet. One likely explanation for this is that only a minority of species are responsive to the specific dietary manipulation (change in ND carbohydrate) employed [11,15]. In another controlled dietary intervention study, David et al. [13] provided volunteers with ‘animal-based’ or ‘plant-based’ diets that differed widely in protein, fat, fibre and carbohydrate content. This also resulted in rapid shifts in microbial community composition but these changes were more wide ranging than those seen following controlled changes only in ND carbohydrates. Current Opinion in Microbiology 2017, 38:59–65
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Diet is also reported to influence overall gut microbiota diversity. In adults, low alpha diversity within the microbial community has been linked with poor health outcomes. In particular, Le Chatelier et al. [16] showed that microbiota diversity within human populations followed a bimodal distribution with higher (HGC) and lower (LGC) diversity peaks. The LGC individuals on average showed a higher incidence of metabolic syndrome. The microbiota diversity of obese LGC (but not HGC) individuals could however be increased through dietary management, suggesting that the habitual diet may have been responsible for the LGC state [17]. Interestingly, in the study of Walker et al. [11] diversity was significantly lower on the RS-enriched diet than on the NSP (wheat bran-enriched) diet, indicating that the complexity and variety of non-digestible carbohydrate substrates may have a real impact on community diversity. The LGC microbiota tended to be dominated by Bacteroides, and may correspond to one of the three ‘enterotypes’ previously proposed by the same researchers [18]. Interestingly, Wu et al. [19] reported that they could subdivide US volunteers into two ‘enterotypes’: those with Prevotella-dominant microbial communities had higher habitual plant fibre intake and those with Bacteroides-dominant communities had higher protein and fat intake, indicating that alternative states of the intestinal microbiota may be driven by long-term dietary habits.
Microbial ecology Changes in microbial community composition can occur in response to changes in growth requirements (sources of energy, nitrogen, micronutrients) or growth-inhibitory factors (e.g., pH, toxic metabolites, bacteriophage-mediated lysis). Iron availability has been shown to modulate butyrate-producing bacteria [20] as well as pathogenic proteobacteria [21] while there also is likely to be competition for and exchange of vitamins between different groups of gut bacteria [22]. Two classes of molecule that are likely to play roles in selective inhibition of bacterial groups are phytochemicals [23,24] and dietary fats. The ‘animal based’ diet of David et al. [13] which consisted of almost 70% of calories from fat and 30% from protein resulted in enrichment of Bacteroides, Alistipes and Bilophila spp. in the faecal microbiota of human volunteers, with selective inhibition of other groups by increased bile acid [25] proposed as a possible mechanism. On the other hand the absence of fibre in this diet was assumed to contribute to the decrease in many Firmicutes by comparison with the ‘plant-based’ diet [13]. The availability of carbohydrates as the main energy sources for microbial growth offers opportunities for beneficial manipulation of the microbiota and is considered further below. Current Opinion in Microbiology 2017, 38:59–65
Bacterial utilization of ND carbohydrates The spectacular increase in genome sequence information for cultured representatives of the gut microbiota should be of great help in predicting the responses of gut bacteria to dietary factors, especially changes in the intake of different non-digestible carbohydrates. While this information is proving helpful, there are also limitations to what can be inferred. Detailed knowledge of microbial CAZyme (carbohydrate active enzyme) families allows for profiling of representative bacteria and prediction of their likely substrate-degrading abilities [26–28] and understanding has been advanced by the identification of polysaccharide utilization loci in the Bacteroidetes [29] and in the Lachnospiraceae family of Firmicutes [30]. Nevertheless, the wide functional diversity within CAZyme families means that function still cannot be predicted with any great confidence from sequence data alone, while degradation of a given polymer may not be accompanied by the ability to take up and utilize the products [30]. This makes it essential to confirm substrate utilization patterns for individual strains experimentally, but data on isolated strains still cannot predict the competitive ability of a given species within the complex community. Competition within the community has been addressed recently using a continuous flow fermentor community derived from a faecal inoculum that is supplied with single ND carbohydrates. Chung et al. [31] found that among the Bacteroidetes in three different microbial communities, inulin selected for the species B. uniformis or B. caccae, depending on the faecal donor, whereas apple pectin selected for six different Bacteroides species. While broadly consistent with the CAZyme profiles of each species, this outcome would have been difficult to predict a priori. Another significant point to emerge is that environmental conditions can alter competition for the same substrate. Thus, controlling the pH at 5.5 tended to limit Bacteroides growth, allowing Gram-positive bacteria to compete more successfully [31,32]. Dietary fibre intake increases fermentation and short chain fatty acid production and also influences gut transit, with consequences for absorption and gut pH [33,34] (Figure 1). Interestingly, a recent survey reported that stool consistency, which is likely to be related to gut transit, was the variable most reliably correlated with inter-individual differences in faecal microbiota composition [35]. Much non-digestible dietary fibre arrives in the large intestine in the form of insoluble complex particles (plant fragments, starch particles) rather than as soluble carbohydrate. Degradation of such material is likely to involve specialist groups of bacteria that have the ability to adhere to the substrate [36] and that are equipped with sophisticated enzyme systems that can attack such recalcitrant material. Examples are Ruminococcus bromii, a bacterium able to degrade raw starch particles [37,38], and R. champanellensis, the only human gut bacterium so far show to degrade crystalline cellulose [39], which produce extracellular amylosome and cellulosome complexes www.sciencedirect.com
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Figure 1
dietary residue (fibre, starch)
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community composition Current Opinion in Microbiology
Factors likely to be involved in the impact of dietary fibre intake upon gut microbiota composition in humans. In addition to direct growth stimulation, changes in gut transit and microbial metabolites (largely via effects on SCFA concentrations and gut pH) are likely to be involved in mediating shifts on microbiota composition.
respectively. Thus it is not only the genomic complement of CAZymes, but the organization of the enzymes on the cell surface that determines the ability to degrade such complex substrates [40]. Other specialist bacteria able to degrade wheat bran have been identified recently that belong to the Lachnospiraceae [41]. The activities of such primary degraders result in the release of energy in the form of soluble breakdown products that can be used by other bacteria within the community, making them candidates to be ‘keystone species’ in fibre degradation. Apparently small differences in the origin or pretreatment of dietary ND carbohydrates may create very different niches and consequences. Thus different types of RS are reported to differentially stimulate two ‘keystone’ starchdegrading species, B. adolescentis and R. bromii [42].
Keystone species, metabolite cross-feeding and microbiota diversity A keystone species can be defined as one whose absence has profound consequences for other members of the microbial community and their metabolic outputs [43,44]. As noted above, the ability to degrade particular types of RS or cellulosic plant cell wall substrates appears to be limited to a few specialized species [37,43]. The absence of these species can mean that substrate remains un-degraded, as was found from faecal starch measurements in two out of 14 individuals who lacked the keystone species R. bromii in their gut microbiota [11]. www.sciencedirect.com
The activities of primary degraders of such insoluble substrates release energy in the form of breakdown products and metabolites that support the growth of other members of the community via cross-feeding [37,45,46]. A microbial community that lacks a number of keystone species involved with complex polysaccharide breakdown is therefore likely to be less efficient at recovering energy from dietary fibre. This could provide a basis for the idea of ‘permissive’ and ‘restrictive’ microbiotas recently proposed by Wu et al. [47]. This proposal was offered to explain why faecal SCFA are reported to increase in response to increased intake of dietary fibre and starch, notably in rural African populations (‘permissive microbiota’) [48] whereas such an increase was not observed in a dietary intervention in a US population (‘restrictive microbiota’) [47]. As noted earlier, urbanized populations do not generally achieve the non-digestible carbohydrate intake levels typical of rural African and Amerindian populations [14]. This hypothesis implies that the higher overall microbiota diversity reported from rural African and Amerindian communities will include many ‘keystone species’ that are not represented in the gut microbiota of urbanised populations with high levels of antibiotic use and caesarian birth [2]. It has been proposed that such urbanised populations are suffering a steady loss in microbiota diversity with each generation [49] and it would be good to see these important hypotheses tested critically in a number of ways. First, much-needed bacterial isolation work from groups of non-industrialised individuals would allow the characteristics and genomes of these putative ‘missing’ bacteria to be investigated directly. Second, we know remarkably little about the survival of anaerobic gut bacteria outside the gut or their ability to be transmitted between individuals and to recolonize following loss from the microbiota. However, the discovery that many of the dominant gut anaerobes previously classified as ‘nonsporing’ can in fact form resistant spores under challenging environmental conditions should spark a re-examination of these questions [50].
What impact does microbiota composition have on gut metabolites? Some metabolic capabilities show very limited phylogenetic distribution, for example methane formation which correlates well with numbers of methanogenic archaea in different human subjects [51]. The problem of generating energy through anaerobic fermentation of organic compounds has a limited number of solutions and fermentative metabolism requires that substrates and products remain in redox balance. Thus many different carbohydrate-fermenting bacteria have evolved to use the products butyrate, propionate or lactate, for example, as ‘hydrogen sinks’ to dispose of reducing equivalents formed during glycolysis. Nevertheless pathways responsible for reductive acetogenesis and those leading to butyrate and propionate are not universal and are limited Current Opinion in Microbiology 2017, 38:59–65
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to certain groups and species of colonic bacteria [52–55]. The proportion of propionate among faecal SCFA has been found to correlate with % Bacteroidetes in at least one human study [15]. Butyrate concentrations have been found to be related to populations of butyrate-producing bacteria, especially Roseburia and E. rectale populations, in intervention studies using weight loss diets (Figure 2b, [56–58]) and between individuals at baseline [59]. As information improves on the phylogenetic distribution of microbial capabilities to perform many other transformations, such as bile salts [60] and phytochemicals [61] many other examples of such relationships should emerge. Thus the impact of diet upon the microbially-produced metabolome is driven in part by diet-mediated changes in microbiota composition that influence the abundance of different enzymatic pathways for processing diet- and host-derived compounds. Sequence-based community analyses provide information on relative rather than absolute numbers (discussed in Box 1). In general absolute population levels are more relevant to understanding metabolite formation as they can be related quantitatively to metabolite fluxes and concentrations [62] (Figure 2).
Box 1 Absolute and relative changes in gut microbiota It should be recognized that sequence-based community analyses by themselves provide no information on the absolute abundance of bacterial cells in a gut sample. Thus an increase in relative sequence abundance of one group does not necessarily imply an increase in numbers of that group, since the change might be due to a decrease in other species, or be accompanied by a decrease in total bacterial numbers. Absolute numbers are estimated most accurately by techniques such as fluorescent in situ hybridisation (FISH) (at least for planctonic cells) although approximate estimates can also be obtained by qPCR. In the hypothetical example shown in Figure 2a the absolute population of group 2 is completely unaffected by the supplement (B) or the inhibitor (C), whereas the relative abundance of this group within the community is markedly affected. The data shown in Figure 2b from a real human dietary study showed that low carbohydrate weight loss diets decreased total bacterial numbers, thus explaining a decrease in total faecal SCFA, while the decreased proportion of butyrate-producing bacteria (mainly the group shown in yellow) explained a decrease in the proportion of butyric acid among SCFA [56–58].
Figure 2
Absolute numbers
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8 7
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Absolute and relative changes in microbial community composition resulting from dietary change. (a) Hypothetical case for microbiota classified into 5 phylogenetic groups (1–5). A refers to baseline samples, B to samples following dietary supplementation with an ND carbohydrate (e.g., prebiotic) that favours group 4, and C to the impact of a selective inhibitor (e.g., antibiotic) that inhibits groups 1 and 3. (b) Human study data from [56] (see Box 1); diets, M = maintenance (13% protein, 52% carbohydrates), HPMC = weight loss (30% protein 35% carbohydrates), HPLC = weight loss (30% protein, 4% carbohydrates). Bacterial groups (detected in faecal samples by FISH): Bac = Bacteroides + Prevotella; Bif = bifidobacteria; RumFpr = Faecalibacterium prausnitzii; RumRum = Ruminococcaceae, Ruminococcus genus; LacRos = Roseburia + Eubacterium rectale; LacNonRos = other Lachnospiraceae. Total bacterial counts (on the left) and proportion not accounted for by FISH probes (on the right) are shown in light grey.
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et al.: Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016, 352:565-569.
Conclusions The finding that dietary changes produce reproducible shifts in the phylogenetic make-up of the gut microbiota across different individuals implies considerable conservation of ecological and nutritional characteristics within phylogenetic groups of bacteria. More insight into metabolism and competition for substrates among the dominant species will therefore improve our ability to predict the consequences of dietary modification. While there is clear evidence for the impact of specific dietary components on the gut microbiota in the short-term, we also know that inter-individual differences in gut microbiota composition before a dietary intervention can affect responses to dietary change [9,11,59]. The factors underlying inter-individual microbiota variation are not yet fully understood and may include host genotype, early colonisation, environmental acquisition, disease states and medication, but it is becoming increasingly apparent that long-term dietary habits also play a crucial role in creating inter-individual variation in microbiota composition. This may reflect not only the direct impact of diet upon the microbiota, which has been considered here, but also long-term consequences of diet for immune function and host physiology that are likely to affect microbiota composition. Nevertheless, many of the mechanisms that drive short and long-term dietary effects on the microbiota are likely to be common to both, which should make it easier to research them and also to reverse the deleterious effects of poor diets.
Acknowledgement The authors receive support from the Scottish Government Food, Land and People research programme.
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of spore formation. This finding has wide-ranging implications for the colonisation of the gut during early and adult life. 51. Nava GM, Carbonero F, Croix JA, Greenberg E, Gaskins HR: Abundance and diversity of mucosa-associated hydrogenotrophic microbes in the healthy human colon. ISME J. 2012, 6:57-70.
metabolite profiles likely to be detrimental to colonic health. Am. J. Clin. Nutr. 2011, 93:1062-1072. 58. Russell WR, Hoyles L, Flint HJ, Dumas ME: Colonic bacterial metabolites and human health. Curr. Opin. Microbiol. 2013, 16:246-254.
53. Flint HJ, Duncan SH, Scott KP, Louis P: Links between diet, gut microbiota composition and gut metabolism. Proc. Nutr. Soc. 2015, 74:13-22.
59. Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM: Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome 2016, 4:33. An intriguing recent study that emphasises the importance of interindividual variation in baseline microbiota to gut metabolites and responses to diet. Demonstrating heterogeneous responses of amylolytic bacteria and faecal butyrate to dietary supplementation with raw potato starch among 20 young adults.
54. Wolf PG, Biswas A, Morales SE, Greening C, Gaskins HR: H2 metabolism is widespread and diverse among human colonic microbes. Gut Microbes 2016, 7:235-245.
60. Ridlon JM, Harris SC, Bhowmik S, Kang DJ, Hylemon PB: Consequences of bile salt biotransformations by intestinal bacteria. Gut Microbes 2016, 7:22-39.
55. Louis P, Flint HJ: Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 2016, 19:29-41.
61. Braune A, Blaut M: Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut Microbes 2016, 7:216-234.
52. Reichardt N, Duncan SH, Young P, Belenguer A, McWilliam Leitch C, Scott KP, Flint HJ, Louis P: Phylogenetic distribution of three pathways for propionate production within the human gut microbiota. ISME J. 2014, 8:1323-1335.
56. Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ, Lobley GE: Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl. Environ. Microbiol. 2007, 73:1073-1078. 57. Russell WR, Gratz SW, Duncan SH, Holtrop G, Ince J, Scobbie L, Duncan G, Johnstone AM, Lobley GE, Wallace RJ et al.: Highprotein, reduced-carbohydrate weight-loss diets promote
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62. Kettle H, Louis P, Holtrop G, Duncan SH, Flint HJ: Modelling the emergent dynamics and major metabolites of the human colonic microbiota. Environ. Microbiol. 2015, 17:1615-1630. Theoretical modelling of the dynamics of the human colonic microbiota based on assumption of 10 functional groups. The model provided a reasonable simulation of shifts in community composition and metabolite outputs resulting from an experimental one unit pH shift. Predictions should improve with new data to refine underlying assumptions.
Current Opinion in Microbiology 2017, 38:59–65
ScienceDirect The impact of nutrition on intestinal bacterial communities Harry J Flint, Sylvia H Duncan and Petra Louis What we eat influences the species composition of our gut microbiota. This is not only because diet composition determines the supply of substrates for microbial growth (in the form of dietary residue, mainly fibre, that reaches the large intestine) but also because of impacts on gut transit and the gut environment. In turn the metabolic activities of the gut microbiota, which have important health consequences, are influenced by diet and diet-driven changes in microbiota composition. Better understanding of the metabolic capabilities and host-interactions of dominant members of the gut microbiota will aid our ability to improve human health through diet. Address Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, UK Corresponding author: Flint, Harry J ([email protected])
Current Opinion in Microbiology 2017, 38:59–65 This review comes from a themed issue on Microbiota Edited by Franc¸ois Leulier and Maria Elena
http://dx.doi.org/10.1016/j.mib.2017.04.005 1369-5274/ã 2017 Elsevier Ltd. All rights reserved.
Introduction Most surveys of gut microbiota composition are based on high throughput analysis of nucleic acid sequences from faecal samples. Within human populations these reveal compositional differences with age and development, including between pre-weaned infants, adults and the frail elderly [1,2]. While host factors, especially immune function and the gut environment, can be important, diet is increasingly offered as the explanation for many of these differences. Thus the supply of human milk oligosaccharides drives the Bifidobacterium-dominated microbiota of breast-fed infants [3], while limited dietary intake that is low in fibre may explain changes seen in the frail elderly [1]. Marked differences are also reported between communities separated by geography and lifestyle, for example between industrialised European or North American cohorts and rural Africans or South Americans [2,4,5]. A multiplicity of factors may be relevant, including genotypes, antibiotic use and inocula www.sciencedirect.com
from the environment [6] but differences in dietary intake are frequently offered as an explanation for such variation in gut microbiota composition. This makes it important therefore to consider what direct evidence exists for dietary modulation of microbiota composition.
Impact of diet on microbiota composition While observational studies can provide correlations, provided that accurate data on nutritional intake are available, it should be recognized that they cannot provide proof that diet is the cause of shifts in microbiota composition. For this we need to consider studies involving gut microbiota profiling in which the human diet has been deliberately altered. Such studies vary in the degree of dietary control, with some relying only on dietary recommendation and some providing defined supplements to volunteers following their habitual diets. Supplementation studies have been used in particular to obtain evidence for the impact of prebiotics upon the gut microbiota and have often studied selected groups of ‘target’ bacteria, notably bifidobacteria [7] although more comprehensive surveys have revealed changes in multiple groups [8–10]. Only a few studies have controlled complete dietary intake over a period of time in human volunteers, thus minimizing the influence of background variations in dietary intake [11–13,14]. Walker et al. [11] used a cross-over design in which volunteers switched between diets with the same protein, fat and carbohydrate proportions, but with either resistant starch (RS) or wheat-bran as the main dietary source of non-digestible (ND) carbohydrate. This study identified different species that became strongly enriched within the community either by the RS or by the wheat bran diet [11,15]. These responses occurred rapidly (within 2–3 days) and were found to be reversed following a subsequent dietary switch. Interestingly, it was also noted that overall microbiota composition was more strongly influenced by the individual volunteer than by the diet. One likely explanation for this is that only a minority of species are responsive to the specific dietary manipulation (change in ND carbohydrate) employed [11,15]. In another controlled dietary intervention study, David et al. [13] provided volunteers with ‘animal-based’ or ‘plant-based’ diets that differed widely in protein, fat, fibre and carbohydrate content. This also resulted in rapid shifts in microbial community composition but these changes were more wide ranging than those seen following controlled changes only in ND carbohydrates. Current Opinion in Microbiology 2017, 38:59–65
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Diet is also reported to influence overall gut microbiota diversity. In adults, low alpha diversity within the microbial community has been linked with poor health outcomes. In particular, Le Chatelier et al. [16] showed that microbiota diversity within human populations followed a bimodal distribution with higher (HGC) and lower (LGC) diversity peaks. The LGC individuals on average showed a higher incidence of metabolic syndrome. The microbiota diversity of obese LGC (but not HGC) individuals could however be increased through dietary management, suggesting that the habitual diet may have been responsible for the LGC state [17]. Interestingly, in the study of Walker et al. [11] diversity was significantly lower on the RS-enriched diet than on the NSP (wheat bran-enriched) diet, indicating that the complexity and variety of non-digestible carbohydrate substrates may have a real impact on community diversity. The LGC microbiota tended to be dominated by Bacteroides, and may correspond to one of the three ‘enterotypes’ previously proposed by the same researchers [18]. Interestingly, Wu et al. [19] reported that they could subdivide US volunteers into two ‘enterotypes’: those with Prevotella-dominant microbial communities had higher habitual plant fibre intake and those with Bacteroides-dominant communities had higher protein and fat intake, indicating that alternative states of the intestinal microbiota may be driven by long-term dietary habits.
Microbial ecology Changes in microbial community composition can occur in response to changes in growth requirements (sources of energy, nitrogen, micronutrients) or growth-inhibitory factors (e.g., pH, toxic metabolites, bacteriophage-mediated lysis). Iron availability has been shown to modulate butyrate-producing bacteria [20] as well as pathogenic proteobacteria [21] while there also is likely to be competition for and exchange of vitamins between different groups of gut bacteria [22]. Two classes of molecule that are likely to play roles in selective inhibition of bacterial groups are phytochemicals [23,24] and dietary fats. The ‘animal based’ diet of David et al. [13] which consisted of almost 70% of calories from fat and 30% from protein resulted in enrichment of Bacteroides, Alistipes and Bilophila spp. in the faecal microbiota of human volunteers, with selective inhibition of other groups by increased bile acid [25] proposed as a possible mechanism. On the other hand the absence of fibre in this diet was assumed to contribute to the decrease in many Firmicutes by comparison with the ‘plant-based’ diet [13]. The availability of carbohydrates as the main energy sources for microbial growth offers opportunities for beneficial manipulation of the microbiota and is considered further below. Current Opinion in Microbiology 2017, 38:59–65
Bacterial utilization of ND carbohydrates The spectacular increase in genome sequence information for cultured representatives of the gut microbiota should be of great help in predicting the responses of gut bacteria to dietary factors, especially changes in the intake of different non-digestible carbohydrates. While this information is proving helpful, there are also limitations to what can be inferred. Detailed knowledge of microbial CAZyme (carbohydrate active enzyme) families allows for profiling of representative bacteria and prediction of their likely substrate-degrading abilities [26–28] and understanding has been advanced by the identification of polysaccharide utilization loci in the Bacteroidetes [29] and in the Lachnospiraceae family of Firmicutes [30]. Nevertheless, the wide functional diversity within CAZyme families means that function still cannot be predicted with any great confidence from sequence data alone, while degradation of a given polymer may not be accompanied by the ability to take up and utilize the products [30]. This makes it essential to confirm substrate utilization patterns for individual strains experimentally, but data on isolated strains still cannot predict the competitive ability of a given species within the complex community. Competition within the community has been addressed recently using a continuous flow fermentor community derived from a faecal inoculum that is supplied with single ND carbohydrates. Chung et al. [31] found that among the Bacteroidetes in three different microbial communities, inulin selected for the species B. uniformis or B. caccae, depending on the faecal donor, whereas apple pectin selected for six different Bacteroides species. While broadly consistent with the CAZyme profiles of each species, this outcome would have been difficult to predict a priori. Another significant point to emerge is that environmental conditions can alter competition for the same substrate. Thus, controlling the pH at 5.5 tended to limit Bacteroides growth, allowing Gram-positive bacteria to compete more successfully [31,32]. Dietary fibre intake increases fermentation and short chain fatty acid production and also influences gut transit, with consequences for absorption and gut pH [33,34] (Figure 1). Interestingly, a recent survey reported that stool consistency, which is likely to be related to gut transit, was the variable most reliably correlated with inter-individual differences in faecal microbiota composition [35]. Much non-digestible dietary fibre arrives in the large intestine in the form of insoluble complex particles (plant fragments, starch particles) rather than as soluble carbohydrate. Degradation of such material is likely to involve specialist groups of bacteria that have the ability to adhere to the substrate [36] and that are equipped with sophisticated enzyme systems that can attack such recalcitrant material. Examples are Ruminococcus bromii, a bacterium able to degrade raw starch particles [37,38], and R. champanellensis, the only human gut bacterium so far show to degrade crystalline cellulose [39], which produce extracellular amylosome and cellulosome complexes www.sciencedirect.com
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Figure 1
dietary residue (fibre, starch)
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Factors likely to be involved in the impact of dietary fibre intake upon gut microbiota composition in humans. In addition to direct growth stimulation, changes in gut transit and microbial metabolites (largely via effects on SCFA concentrations and gut pH) are likely to be involved in mediating shifts on microbiota composition.
respectively. Thus it is not only the genomic complement of CAZymes, but the organization of the enzymes on the cell surface that determines the ability to degrade such complex substrates [40]. Other specialist bacteria able to degrade wheat bran have been identified recently that belong to the Lachnospiraceae [41]. The activities of such primary degraders result in the release of energy in the form of soluble breakdown products that can be used by other bacteria within the community, making them candidates to be ‘keystone species’ in fibre degradation. Apparently small differences in the origin or pretreatment of dietary ND carbohydrates may create very different niches and consequences. Thus different types of RS are reported to differentially stimulate two ‘keystone’ starchdegrading species, B. adolescentis and R. bromii [42].
Keystone species, metabolite cross-feeding and microbiota diversity A keystone species can be defined as one whose absence has profound consequences for other members of the microbial community and their metabolic outputs [43,44]. As noted above, the ability to degrade particular types of RS or cellulosic plant cell wall substrates appears to be limited to a few specialized species [37,43]. The absence of these species can mean that substrate remains un-degraded, as was found from faecal starch measurements in two out of 14 individuals who lacked the keystone species R. bromii in their gut microbiota [11]. www.sciencedirect.com
The activities of primary degraders of such insoluble substrates release energy in the form of breakdown products and metabolites that support the growth of other members of the community via cross-feeding [37,45,46]. A microbial community that lacks a number of keystone species involved with complex polysaccharide breakdown is therefore likely to be less efficient at recovering energy from dietary fibre. This could provide a basis for the idea of ‘permissive’ and ‘restrictive’ microbiotas recently proposed by Wu et al. [47]. This proposal was offered to explain why faecal SCFA are reported to increase in response to increased intake of dietary fibre and starch, notably in rural African populations (‘permissive microbiota’) [48] whereas such an increase was not observed in a dietary intervention in a US population (‘restrictive microbiota’) [47]. As noted earlier, urbanized populations do not generally achieve the non-digestible carbohydrate intake levels typical of rural African and Amerindian populations [14]. This hypothesis implies that the higher overall microbiota diversity reported from rural African and Amerindian communities will include many ‘keystone species’ that are not represented in the gut microbiota of urbanised populations with high levels of antibiotic use and caesarian birth [2]. It has been proposed that such urbanised populations are suffering a steady loss in microbiota diversity with each generation [49] and it would be good to see these important hypotheses tested critically in a number of ways. First, much-needed bacterial isolation work from groups of non-industrialised individuals would allow the characteristics and genomes of these putative ‘missing’ bacteria to be investigated directly. Second, we know remarkably little about the survival of anaerobic gut bacteria outside the gut or their ability to be transmitted between individuals and to recolonize following loss from the microbiota. However, the discovery that many of the dominant gut anaerobes previously classified as ‘nonsporing’ can in fact form resistant spores under challenging environmental conditions should spark a re-examination of these questions [50].
What impact does microbiota composition have on gut metabolites? Some metabolic capabilities show very limited phylogenetic distribution, for example methane formation which correlates well with numbers of methanogenic archaea in different human subjects [51]. The problem of generating energy through anaerobic fermentation of organic compounds has a limited number of solutions and fermentative metabolism requires that substrates and products remain in redox balance. Thus many different carbohydrate-fermenting bacteria have evolved to use the products butyrate, propionate or lactate, for example, as ‘hydrogen sinks’ to dispose of reducing equivalents formed during glycolysis. Nevertheless pathways responsible for reductive acetogenesis and those leading to butyrate and propionate are not universal and are limited Current Opinion in Microbiology 2017, 38:59–65
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to certain groups and species of colonic bacteria [52–55]. The proportion of propionate among faecal SCFA has been found to correlate with % Bacteroidetes in at least one human study [15]. Butyrate concentrations have been found to be related to populations of butyrate-producing bacteria, especially Roseburia and E. rectale populations, in intervention studies using weight loss diets (Figure 2b, [56–58]) and between individuals at baseline [59]. As information improves on the phylogenetic distribution of microbial capabilities to perform many other transformations, such as bile salts [60] and phytochemicals [61] many other examples of such relationships should emerge. Thus the impact of diet upon the microbially-produced metabolome is driven in part by diet-mediated changes in microbiota composition that influence the abundance of different enzymatic pathways for processing diet- and host-derived compounds. Sequence-based community analyses provide information on relative rather than absolute numbers (discussed in Box 1). In general absolute population levels are more relevant to understanding metabolite formation as they can be related quantitatively to metabolite fluxes and concentrations [62] (Figure 2).
Box 1 Absolute and relative changes in gut microbiota It should be recognized that sequence-based community analyses by themselves provide no information on the absolute abundance of bacterial cells in a gut sample. Thus an increase in relative sequence abundance of one group does not necessarily imply an increase in numbers of that group, since the change might be due to a decrease in other species, or be accompanied by a decrease in total bacterial numbers. Absolute numbers are estimated most accurately by techniques such as fluorescent in situ hybridisation (FISH) (at least for planctonic cells) although approximate estimates can also be obtained by qPCR. In the hypothetical example shown in Figure 2a the absolute population of group 2 is completely unaffected by the supplement (B) or the inhibitor (C), whereas the relative abundance of this group within the community is markedly affected. The data shown in Figure 2b from a real human dietary study showed that low carbohydrate weight loss diets decreased total bacterial numbers, thus explaining a decrease in total faecal SCFA, while the decreased proportion of butyrate-producing bacteria (mainly the group shown in yellow) explained a decrease in the proportion of butyric acid among SCFA [56–58].
Figure 2
Absolute numbers
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8 7
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Absolute and relative changes in microbial community composition resulting from dietary change. (a) Hypothetical case for microbiota classified into 5 phylogenetic groups (1–5). A refers to baseline samples, B to samples following dietary supplementation with an ND carbohydrate (e.g., prebiotic) that favours group 4, and C to the impact of a selective inhibitor (e.g., antibiotic) that inhibits groups 1 and 3. (b) Human study data from [56] (see Box 1); diets, M = maintenance (13% protein, 52% carbohydrates), HPMC = weight loss (30% protein 35% carbohydrates), HPLC = weight loss (30% protein, 4% carbohydrates). Bacterial groups (detected in faecal samples by FISH): Bac = Bacteroides + Prevotella; Bif = bifidobacteria; RumFpr = Faecalibacterium prausnitzii; RumRum = Ruminococcaceae, Ruminococcus genus; LacRos = Roseburia + Eubacterium rectale; LacNonRos = other Lachnospiraceae. Total bacterial counts (on the left) and proportion not accounted for by FISH probes (on the right) are shown in light grey.
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et al.: Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016, 352:565-569.
Conclusions The finding that dietary changes produce reproducible shifts in the phylogenetic make-up of the gut microbiota across different individuals implies considerable conservation of ecological and nutritional characteristics within phylogenetic groups of bacteria. More insight into metabolism and competition for substrates among the dominant species will therefore improve our ability to predict the consequences of dietary modification. While there is clear evidence for the impact of specific dietary components on the gut microbiota in the short-term, we also know that inter-individual differences in gut microbiota composition before a dietary intervention can affect responses to dietary change [9,11,59]. The factors underlying inter-individual microbiota variation are not yet fully understood and may include host genotype, early colonisation, environmental acquisition, disease states and medication, but it is becoming increasingly apparent that long-term dietary habits also play a crucial role in creating inter-individual variation in microbiota composition. This may reflect not only the direct impact of diet upon the microbiota, which has been considered here, but also long-term consequences of diet for immune function and host physiology that are likely to affect microbiota composition. Nevertheless, many of the mechanisms that drive short and long-term dietary effects on the microbiota are likely to be common to both, which should make it easier to research them and also to reverse the deleterious effects of poor diets.
Acknowledgement The authors receive support from the Scottish Government Food, Land and People research programme.
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Impact of nutrition on gut microbiota Flint, Duncan and Louis 65
of spore formation. This finding has wide-ranging implications for the colonisation of the gut during early and adult life. 51. Nava GM, Carbonero F, Croix JA, Greenberg E, Gaskins HR: Abundance and diversity of mucosa-associated hydrogenotrophic microbes in the healthy human colon. ISME J. 2012, 6:57-70.
metabolite profiles likely to be detrimental to colonic health. Am. J. Clin. Nutr. 2011, 93:1062-1072. 58. Russell WR, Hoyles L, Flint HJ, Dumas ME: Colonic bacterial metabolites and human health. Curr. Opin. Microbiol. 2013, 16:246-254.
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59. Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM: Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome 2016, 4:33. An intriguing recent study that emphasises the importance of interindividual variation in baseline microbiota to gut metabolites and responses to diet. Demonstrating heterogeneous responses of amylolytic bacteria and faecal butyrate to dietary supplementation with raw potato starch among 20 young adults.
54. Wolf PG, Biswas A, Morales SE, Greening C, Gaskins HR: H2 metabolism is widespread and diverse among human colonic microbes. Gut Microbes 2016, 7:235-245.
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61. Braune A, Blaut M: Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut Microbes 2016, 7:216-234.
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62. Kettle H, Louis P, Holtrop G, Duncan SH, Flint HJ: Modelling the emergent dynamics and major metabolites of the human colonic microbiota. Environ. Microbiol. 2015, 17:1615-1630. Theoretical modelling of the dynamics of the human colonic microbiota based on assumption of 10 functional groups. The model provided a reasonable simulation of shifts in community composition and metabolite outputs resulting from an experimental one unit pH shift. Predictions should improve with new data to refine underlying assumptions.
Current Opinion in Microbiology 2017, 38:59–65